1

Financial Distress Predictions in an International Context A Review and Empirical Analysis of Altmanrsquos Z-Score Model

ABSTRACT

The purpose of this paper is to assesses the classification performance of the Z-Score model in

the predictionng of bankruptcy and other types of firm distress firms encounter The goal is in

order aiming to confirm the modelrsquosits usefulness for all parties especially banks that operate

internationally and that are interested in assessing the firmsrsquo failure risk of firms We analyze the

Z-Score performance for firms from 31 European and 3 non-European countries using different

modifications of the original model This study is the first to offer such akind of comprehensive

international analysis has not been presented thus far Except for the USUS and China the firms

in the sample are primarily private and cover non-financial companies across all industrial

sectors are included Thus we use the version of the Z-Score model developed by Altman (1983)

for private manufacturing and non-manufacturing firms (Zrsquorsquo-Score model) is used in our testing

The literature review shows that Z-Score models have been outperformed by competing market-

based or hazard models but in some studies they Z-Score models still performed very well None

of the reviewed studies performedwas based on a comprehensive international comparison

which makinges the results difficult to generalize The analysis in the currentis study shows that

while a general international model works reasonably well for most countries (AUC area under

curve prediction accuracy levels of approximately 075) and exceptionally well for some (above

090) the classification accuracy may can be considerably improved with country-specific

estimations especially with the use of additional variables In some country models the

2

information provided by additional variables helps boost the classification accuracy to a much

higher level

JEL codes G15 G32 G33

Keywords Z-Score bankruptcy failure default financial distress

3

1 Introduction

The first multivariate bankruptcy prediction model was developed by EI Altman (1968) in the

late 1960s After this pioneering work the multivariate approach to failure prediction spread

worldwide among researchers in finance banking and credit risk Failure prediction models are

important tools for bankers investors asset managers rating agencies and even the distressed

firms themselves The banking industry as the main provider of financing in the economy is

especially interested in minimizing the level of non-performing loans in order to maximize profit

on the credit activity and banks seek to reduce their own risk of default Another issue of interest

for bankers is capital adequacy and an the internal ratings-based approach encouraged by Basel

2 The Z-Score model has become a prototype for many of these models Asset managers and

investors need to have reliable tools for that can help them the select ion appropriate of

companies to be added forto their portfolios Financial distress of companies is on the one hand

detrimental to investor returns but on the other hand risk may provide opportunities for high

returns on short-sale strategies Rating agencies assess the risk of the entities and of securities

issues and thus they need to have a tool to predict default In addition Altman (1983 1993 and

2006) has suggested that the management of distressed firms can utilize the Z-Score model as a

guide to a financial turnaround

The approach used for bankruptcy prediction has been evolving evolved over time Beaver (1966

1968) used univariate analysis for selected ratios and found that some of them had very good

predictive power Altman (1968) moved significantly forwardmade strides by developing a

multiple discriminant analysis model (MDA) called the Z-Score model The next two decades

Commented [AFS1] Per the journal guidelines each citation must have a corresponding reference in the reference list Please note that there is no corresponding reference for this citation Please supply the missing information where indicated in the reference list at the end of the text

4

brought saw additional contributions to financial distress research For example such as Ohlson

(1980) with proposed a logit model1 Taffler (1984) with offered a Z-Score model for the UK and

Zmijewski (1984)2 who used a probit approach Dimitras et al (1996) reviewed 47 studies on

business prediction models They summarizinged the methods employed and the variety of

ratios used Discriminant analysis was the prevailing method and the most important financial

ratios came from the solvency category with profitability ratios being also being important

The next summary of different approaches to credit risk analysis was given by Altman and

Saunders (1998) Balcaen and Ooghe (2006) reviewed models of business failure prediction and

classified 43 models presented in the literature into four categories (univariate models 1 risk

index models 2 MDA models 21 and conditional probability models 19) However they

omitted the rapidly fast-growing type of number of models based on the option pricing theory

and contingent claims (eg Vassalou and Xing 2004 commercialized into the KMV model) and

they omittedas well as hazard models (eg Shumway 2001) Kumar and Ravi (2007) reviewed

128 statistical and artificial intelligence models for bank and firm bankruptcy predictions of banks

and firms with paying special attention paid to the techniques used in the different models

These authorsy noted that neural networks were the most popular intelligence technique In

their review Jackson and Wood (2013) presented the frequency of occurrence of specific

forecasting techniques in the prior literature The five most popular techniques were as follows

1 Altmanrsquos Z-score and Ohlsonrsquos O-score have beenwere compared by Dichev (1998) 2 A re-estimation of Ohlsonrsquos and Zmijewskirsquos models was presented by Grice and Dugan (2003)

Commented [S Ed2] Abbreviations and acronyms are often defined the first time they are used within the main text and then used throughout the remainder of the manuscript Please consider adhering to this convention

5

(1) multiple discriminant analysis (2) logit models (3) neural networks (4) contingent claims and

(5) univariate analysis

Recent reviews of the efficacy of these models have been delivered offered by Agarwal and

Taffler (2008) Das et al (2009) and Bauer and Agarwal (2014) These reviews takeing into

account the performance of accounting-based models market-based models and hazard

models These three model types of models prevail in the literature According to Agarwal and

Taffler (2008) there is little difference in the predictive accuracy of accounting-based and

market-based models however the usage of accounting-based models allows for a higher level

of risk-adjusted return on the credit activity Das et al (2009) showed that accounting-based

models perform comparably to the Merton structural market-based approach for CDS spread

estimation However the comprehensive model which used both sources of variables

outperformed both models In Bauer and Agarwal (2014) hazard models using accounting and

market information (Shumway 2001 and Campbell et al 2008) were compared with two other

approaches the original Tafflerrsquos (1984) Z-score model which that was tested in by Agarwal and

Taffler (2008) and a contingent claims model using Bharath and Shumwayrsquos (2008) approach

Using UK data tThe hazard models were superior in UK data in bankruptcy prediction accuracy

ROC (Receiver Operating Characteristic) ROC analysis and information content

Despite the vast amount of research on failure prediction the Z-Score model introduced by

Altman (1968) has been applied worldwide Thus although the Z-Score model has been in

existednce for more than 45 years it is still used as a main or supporting tool for bankruptcy or

6

financial distress prediction or and analysis both in research and in practice Therefore question

arises of why focus on the accounting-based model which is at least occasionally outperformed

by other models Most of the firms operating on the market are privately held and therefore

there are no market data (eg stock prices) are available Theose firms are usually financed by

banks which are obligated to assess their creditworthiness and monitor their performance In

the case of internationally active banks it is especially important to use a universal model across

the banking group for distress prediction for provisioning and for economic capital calculation

In following subsequent regulatory requirements they need to validate models and prove their

efficacy Thus we found it useful to analyze the performance of an accounting-based model in

an international context

In our study we use a large international sample of firms to assess the classification performance

of the Zrsquorsquo-Score model in bankruptcy prediction3 We analyze its the modelrsquos performance in for

firms from 31 European and three non-European countries (China Colombia and the USUS) T

these firms are mostly privately held and aA large number of firms are from non-manufacturing

industries Therefore we use the version of the model developed by Altman (1983) for private

and public manufacturing and non-manufacturing firms (Zrsquorsquo-Score model) Such an extensive

international analysis of the performance of the Z-Score modelrsquos performance has not been

presented thus farto date We regard our review and analysis as important contributions to the

economic literature

3 We use bankruptcy failure default and financial distress as equivalents

7

The paper is structured as follows In the introduction we present a brief summary of the

development of failure prediction research starting beginning with Beaver (1966) and Altman

(1968) In the second section we summarize the original Z-Score model (1968) and its extension

for private firms that is the Zrsquo-Score and Zrsquorsquo-Score models (1983) IThen in the third section we

present the results and conclusions from the literature review on these models The fourth

section presents seven research hypotheses on the performance of the Zrsquorsquo-Score model for

empirical analysis In the fifth section the empirical data and statistical methods are discussed

while the sixth section presents empirical findings Finally the seventh section summarizes the

study

2 Classic Z-Score models

21 Z-Score model for public firms

Altmanrsquos (1968) initial sample was composed of 66 corporations with 33 firms in each of the two

groups The bankrupt group (Group 1) consisted of manufacturers that filed a bankruptcy

petitions under Chapter X of the National Bankruptcy Act during the period 1946ndash1965 period

The mean asset size of these firms was 64 million USD with a ranginge of between 07 and 259

million USD Altman recognized that this group was not homogenous with respect to size and

industry although all firms were relatively small and from manufacturing industries Therefore

he attempted to make a carefully selection select of non-bankrupt firms (Group 2) Group 2

consisted of a paired sample of manufacturing firms chosen on a stratified random basis These

firms were stratified by industry and size with the asset size range restricted to 1ndash25 million USD

Altman eliminated both the small firms (less than 1 million US dollars in total assets) because of

8

a lack of data and the very large firms because of a lack of data because offor small firms and the

rarity of bankruptcies among these firms in that period of large firms He did not match the asset

size of the two groups exactly and therefore the firms in Group 2 are were slightly larger than

those in Group 1 The data collected for the firms in both groups were from the same years For

Group 1 the data were derived from financial statements one reporting period prior to

bankruptcy

Using financial statements Altman compiled a list of 22 potentially important financial ratios

based on financial statements for evaluation He classified these variables into five standard ratio

categories liquidity profitability leverage solvency and activity ratios These ratios were

chosen based on their popularity in the literature and their potential relevancey to the study The

final discriminant function estimated by Altman (1968) is as follows

Z = 0012X1 + 0014X2 + 0033X3 + 0006X4 + 0 999X5 (eq 1)

where X1 = Working CcapitalTotal Aassets X2 = Retained EarningsTotal Aassets X3 = Earnings before Iinterest and TtaxesTotal Aassets X4 = Market Vvalue of EequityBook Vvalue of Ttotal Lliabilities X5 = SalesTotal Aassets Z = Overall Index 22 Zrsquo-Score and Zrsquorsquo-Score models for private firms

The original Z-Score model was based on the market value of the firm and was thus applicable

only to publicly traded companies Altman (1983) emphasized that the Z-Score model is a

intended for publicly traded firms model and that ad hoc adjustments are not scientifically valid

9

Therefore Altman (1983) advocated a complete re-estimation of the model substituting the

book value of equity for the market value in X4 Using the same data Altman extracted the

following revised Zrsquo-Score model

Zrsquo = 0717X1 + 0847X2 + 3107X3 + 0420X4 + 0 998X5 (eq 2)

where X4 = Book value of equityBook value of total liabilities with the other variables being

the same as those in the original (1968) Z-Score model

DAltman ue to the lack of a private firm database Altman did not test the Zrsquo-Score model on a

secondary sample due to the lack of a private firm data base However he analyzed the accuracy

of a four-variable Zrsquorsquo-Score model that excludeding the SalesTotal assets ratio X5 from the

revised model because of a potential industry effect The industry effect that is more likely to

take place when this kind of industry-sensitive variable (asset turnover) is included in the model

Altman then estimated the following four-variable Zrsquorsquo-Score model (Altman 1983)

Zrsquorsquo = 325 + 656X1 + 326X2 + 672X3 + 105X4 (eq 3)

The EBITTotal assets ratio X3 made the highest contributioncontributed most to the

discrimination power in this version of the model The classification results for the Zrsquorsquo-Score

model were identical to the revised five-variable Zrsquo-Score model In the currentis study our

empirical analysis focuses on the performance of the Zrsquorsquo-Score model version in predicting

bankruptcy prediction wheredue to it has its widest scope that is because as it is intended for

both privately held and publicly listed firms and for both manufacturing and non-manufacturing

firms

10

3 Literature review

We searched for papers published from the year 2000 onward 4 in prominent international

journals or and books We selected 31 articles in which the Z-Score was either used either as a

failure prediction proxy or was assessed mostly in terms of predictive ability Of the 31 studies

Altmanrsquos Z-Score model5 was used in 16 cases as the measure of distress or of financial strength

In 13 studies Altmanrsquos original model was modified and (or) verified including re-estimation and

in 2 cases it was used solely for the robustness check As Pindado et al (2008) noted the Z-Score

was also used for other purposes such as the evaluation of the costs and benefits of covenants

in bonds the choice of the type of debt type (bank vs non-bank private or public) and the

relationship between investment and internal funds We focused on this part of the literature

that verified and (or) modified Altmanrsquos original model The wide broad usage of the Z-Score

model foras a measuringe of financial distress and performingas well as for the robustness checks

indicates that it is acceptabilityed as a reasonable simple and consistent measure of the

distressed firms

In the case of the modification of the The most common modification to the Z-Score model

model the most common change was the use of other estimation techniques or country-specific

data The use of Altmanrsquos ratios in combination with techniques other than MDA has improved

the its prediction capability TAdditionally the application of new data of on both US and non-US

4 Research devoted to the application of the Z-Score model before 2000 was reviewed by Grice and Ingram (2001) 5 Most of the models focused on stock- exchange- listed firms thus the Zrsquo-Score and Zrsquorsquo-Score models were not used

11

firms also improved model performance Kwak et al (2005) applied used Multiple Criteria Linear

Programming (MCLP) to model 5 Altman and 9 Ohlson variables They used with data on US

bankrupt US firms from 1992 to 1998 and nearly 6 times more matched US control firms The

MCLP approach performed better than Altmanrsquos original model and gave results similar to or

better than those of Ohlsonrsquos original model The original models were not recalculated and the

authors referred solely to their original prediction rates only Merkevicius et al (2006) using data

on US and Lithuanian firms developed a hybrid artificial -discriminantte model combining MDA

and an unsupervised learning artificial neural network (called the SOM-Altman model) with the

use of data on US and Lithuanian firms Thise hybrid SOM-Altman model reached a high

prediction rate of 9235 Xu and Zhang (2009) applied Altmanrsquos Z-score Ohlsonrsquos O-score and

Mertonrsquos distance-to-default (D-score) models to Japanese firms to check whether these models

are useful for bankruptcy prediction in Japan They also ldquomergedrdquo theose models into a new C-

score model They then introduced variables unique to Japan to check whether corporate

structure variables have any impact on the probability of bankruptcy t They called this the model

X-score model These two models were useful for Japanese firms in predicting bankruptcy

prediction but the market-based model was the most successful In summary the developed C-

score and X-score (with country-specific variables) models improved bankruptcy prediction

Tinoco and Wilson (2013) used the original Z-Score as one of the benchmarks to assess the

performance of their model developed for UK listed companies with combined accounting

market and macroeconomic data Altmanrsquos Z-Score presented very good classification accuracy

in the case of financially distressed firms (81 vs 87 for the new model) however it was less

correct for financial health prediction Another modification was introduced by Lyandres and

12

Zhdanov (2013) who posed the question of whether the inclusion of variables related to

investment opportunities improved the predictive power of three models (Altmanrsquos Z-score

model and Zmijewskirsquos and Shumwayrsquos models) They used three proxies for investment

opportunities (market-to-book value-to-book and RampD-to-assets) The measures of investment

opportunities were linked to the likelihood of default The inclusion of either of these measures

improved the out-of-sample forecasting ability of all three

The verification of Altmanrsquos model concentrated on its efficacy or on how it compares a

comparison with other accounting-based market-based or hazard models However although

the original Z-Score model was not solely an accounting-based approach because the market

value of equity was utilized we classify it here as an accounting -based approachbecause the

market value of equity was utilized Grice and Ingram (2001) used a new novel data set of US

firms and posed three questions about the efficacy of Altmanrsquos model They concludinged that

the prediction accuracy of Altmanrsquos model had declined over time and that the coefficients of

the model had significantly changed which means that the relation between the financial ratios

and the signs of financial distress had changed over time The model was sensitive to industry

classification (more efficient for manufacturing firms than for non-manufacturing firms) but was

not sensitive to the type of financial distress Similar conclusions were drawn by Grice and Dugan

(2003) regarding Ohlsonrsquos (1980) and Zmijewskirsquos (1984) models

Hillegeist et al (2004) compared Altmanrsquos Z-score and Ohlsonrsquos O-score (with original and

updated coefficients) with the model based on BSM option pricing (BSM-Prob) They used

13

relative information content tests to compare the out-of-sample performance BSM-Prob

outperformed accounting-based models The conclusions were robust to various modifications

of accounting-based models such as updated coefficients industry effects and the separation

of variables Chava and Jarrow (2004) employed an extended bankruptcy database of US listed

firms to prove the superiority of Shumwayrsquos model (2001) over Altmanrsquos (1968) and Zmijewskirsquos

(1984) models The authors re-estimated the models over the 1962ndash1990 period and forecasted

bankruptcies over the 1991ndash1999 period In the case of Shumwayrsquos model 744 (in the first

decile) of the bankruptcies were correctly identified for with Altmanrsquos model it was 632 and

for with Zmijewskirsquos model it was 432 Shumwayrsquos market-based model also outperformed

accounting-based models in terms of the ROC curve (091)

Reisz and Perlich (2007) used the barrier option for bankruptcy prediction and compared its

discriminatory power with other market-based models and Altmanrsquos Z-Score and Zrdquo-Score The

dataset covered nearly 6000 industrial firms over the period 1988ndash2002 period The authors

proved the domination superiority of Altmanrsquos Z-Score and Zrdquo-Score models for short-term (up

to 1 year) bankruptcy prediction For medium- and long-term bankruptcy prediction their barrier

option model outperformed the other models Pindado et al (2008) developed an ex -ante model

for the estimation of financial distress likelihood (FDL) using awith the use of panel data

methodology and they presented a financially (not legally) based definition of distress Their

sample covered 1583 US companies and 2250 companies from other G-7 countries for the

period 1990ndash2002 period They used a re-estimated Z-Score as a benchmark The FDL model

outperformed the Z-Score model in terms of stability and classification power for different

14

countries and time periods In the case of the re-estimated Z-Score model only profitability and

retained earnings kept maintained their significance for different years and countries

Wu et al (2010) evaluated the performance of five models (Altman 1968 Ohlson 1980

Zmijewski 1984 Shumway 2001 and Hillegeist et al 2004) using an up-to-date data set for

US listed firms Based on these models the authors built their own integrated model that is a

multi-period logit model with an expanded set of variables The integrated model which

combined accounting and market data as well as firmsrsquo characteristics outperformed the other

models Altmanrsquos Z-score performed poorly in compared withison to the other 4 models

Shumwayrsquos model performed best Hillegeist et alrsquos model performed adequately and Ohlsonrsquos

and Zmijewskirsquos models performed adequately although their performance deteriorated over

time

Jackson and Wood (2013) tested 13 different models of bankruptcy prediction and assessed their

efficacy using the ROC curve They selected three single- variable models three accounting-based

models (including Altmanrsquos Z-Score) in two versions (with up-dated coefficients and a neural

network approach) and four contingent claims models The contingent claims models and the

latter group outperformed the other models The best four best models were contingent claims

models based on European call and barrier options Although tThe predictive performance

improved withIn the case of the application of the neural network to accounting-based models

the predictive ability improved but it was still lower than within the case of the market-based

models

15

Acosta-Gonzaacutelez and Fernaacutendez-Rodriacuteguez (2014) used genetic algorithms with the Schwarz

information criterion (GASIC) for variable selection combined with the logit model for bankruptcy

prediction Altmanrsquos Z-Score model was used as one of two benchmarks for the authorsrsquo model

evaluation For one- step- ahead forecasting Altmanrsquos model was better in at predicting failed

firms but the type II error was high For two- and three- steps- ahead forecasts the performance

of the models was similar forin terms of failed firms but forin the case of non-failed firms and

the prediction accuracy of Altmanrsquos model was worse For four- steps- ahead forecasts the GASIC

model outperformed the other models forin the case of failed firms and but it performedwas

comparablye forin the case of non-failed firms

In general Altmanrsquos Z-Score model underperformed in compared withison to market-based

models but there was also evidence indicated that it performed well for short-term distress

prediction The question of whether market-based models are perform better than accounting-

based models has been raised many times (eg discussion in Das et al 2009 Bauer and d

Agarwal 2014) Our purpose is not to contribute to this strand of research but rather to focus on

the accounting-based approach In this study we analyze primarily analyze privately held firms

so there are by definition no market data for these firms In this case aAn accounting-based

approach is the only solution that could be appliedapplicable in this case by banks as lenders or

by investors holding debt securities of firms not listed on the stock exchange Thus far most of

the studies have concentrated on the US market only a few of them have used data from other

16

countries such as Japan the UK Lithuania or the G-7 countries Our analysis is a significant

extension of the previous research

4 Methodology and research hypotheses

The literature survey shows that the Z-Score model (publicly traded firms) the Zrsquo-Score model

(private manufacturing firms) and the Zrsquorsquo-Score model (private and publicly

traded manufacturing and non-manufacturing firms) have been adapted for different purposes

In this study we are first interested first in assessing the performance of the original Zrsquorsquo-Score

model in classifying bankrupt and non-bankrupt firms in an international context with special

focus on the European market However we also validate the results in a set of non-European

countries to generalize the results for circumstances outside Europe Second we will re-estimate

the model using extensive international data and then use the re-estimated Zrsquorsquo-Score model as a

benchmark for assessing the effects of different factors on the modelrsquos performance in terms of

classification accuracy We will assess the effects of the following five factors on this

performance year of bankruptcy size of firms age of firms industry and country of origin WIn

all we will test a set of hypotheses based on the effects of the model on performance on two

different levels First we will test a set of hypotheses on a pooled set of all firms and second on

data from each country individually CIn compared withison to the previous research our this

studyrsquos contribution is the focus on an international context not just simply the model

application or re-estimation for of a given countryrsquos data Because weour aim is focusing on the

performance of the Zrsquorsquo-Score mModel when using ain large body of international data the

research hypotheses are of a technical nature and are presented given as follows

17

H1 Obsolescence of the coefficients The Zrsquorsquo-Score mModel was originally estimated using the

same sample of firms that was used to develop the original Z-Score mModel The bankruptcies

in the estimation data occurred during the period 1946ndash-1965 period Thus the oldest

observations are from nearly seventy years ago during the post-war period Altman (1983)

recommended utilizing data as near to the present as possible when developing a bankruptcy

prediction model It is obvious that firmsrsquo the financial behavior of firms and their business

environment have significantly changed since then Thereforen potentially making the

importance of the financial ratios as reflected by the coefficients of the model may differ from

their original importance reflected by the coefficients of the model Therefore we suggest as

the first hypothesis (H1) that the re-estimation of the coefficients of the four original variables

of the Zrsquorsquo-Score model will improve the classification performance of the model in an

international context This hypothesis is supported by the previous research (eg Grice and

Ingram 2001) and by practice H1 aims to support this evidence on the international level and is

expressed as follows

H1 RThe re-estimatingon of the coefficients of the Zrsquorsquo-Score model improves the its

classification accuracy of the model

H2 Method of estimation The original Zrsquorsquo-Score model has been estimated using MDA However

MDA is based on the ordinary least squares (OLS) method and thus requires assumptions of

multinormality homoscedasticity and linearity which are not often met in empirical financial

ratio analysis Therefore we re-estimate the Zrsquorsquo-Score model using logistic regression analysis

(LRA) to assess the effect of the method of estimation method LRA does not require most of the

18

restricting assumptions of MDA In LRA the multivariate normality of the independent variables

is not required nor are homoscedasticity and linearity For the sake of OLS MDA can be more

useful than LRA for small samples such as the original sample of 66 firms used in the estimation

of the Zrsquorsquo-Score mModel However in a large sample LRA may potentially perform better In this

study we use large samples which is advantageous for LRA Therefore our second hypothesis

(H2) suggests is that the classification performance of the re-estimated Zrsquorsquo-Score model will be

improved when it is estimated using LRA instead of MDA The model re-estimated for the original

variables using LRA and all pooled data is called here the Zrsquorsquo-Score LR-modelLR model The

performance of this re-estimated model is used in this study as the benchmark for further

analyses Thus the second hypothesis is as follows

H2 The prediction accuracy of the logistic regression version of the Zrsquorsquo-Score model is higher

than that of the multiple discriminant analysis version

H3 Bankruptcy year The model based on the relationship between bankruptcy and financial

ratios is likely to be affected by the macroeconomic environment These effects may significantly

decrease the classification accuracy of the model If the model is estimated using data from one

year and it will be applied to data from another year the validity of the model can be questioned

Business cycles inIn terms of economic growth credit policy and interest rates business cycles

can ahave an effect on the boundary between bankrupt and non-bankrupt firms The original Zrsquorsquo-

Score model is estimated using data from the period 1946ndash1965 period which includes several

business cycles Therefore the model is not focused on any specific stage of a cycle and does not

explicitly take into account of the bankruptcy year Altman (1983) suggested gathering data from

19

firms for the last most recent few years when developing a prediction model In this study the

benchmark Zrsquorsquo-Score LR -model will beis estimated for a shorter period than in the original

estimation although it covers several recent years on for different stages of the business cycle in

different countries Therefore the third hypothesis (H3) assumes is that the classification

accuracy of the benchmark model can be increased by explicitly taking into account of the year

of bankruptcy in the estimation of the model estimation The third hypothesis is presented as

follows

H3 The prediction accuracy of the modelrsquos prediction accuracy is higher whenith the

effect of the year of bankruptcy is included is higher than that of the model without the

effect

H4 Size of the firm The boundary between bankrupt and non-bankrupt firms is different for small

and larger firms which decreases the performance of the model estimation when estimated for

data from one size category and isare applied to for data from another size category For the

bankrupt and non-bankrupt firms in the original data for Zrsquorsquo-Score model estimation the range

of asset sizes was ranged between approximately 1 and 25 million US dollars The data did not

include very small or very large firms Altman (1983) regarded the suitability of the original Z-

Score model (and likewisein the same way the Zrsquorsquo-Score model) for all firms as debatable

because of this omission it did not scrutinize very large or very small firms In the currentis study

the benchmark Zrsquorsquo-Score LR -model will beis estimated for data from many size categories from

very small firms to very large firms The fourth hypothesis (H4) assumes that the classification

performance of the uniform benchmark LR -model based on the original four financial variables

20

of the Zrsquorsquo-Score model is improved when the size category of the firm is explicitly taken into

account Thus we present the fourth hypothesis as follows

H4 The modelrsquos prediction accuracy of the modelis higher whenith the effect of size is

includede is higher than that of the model without this effect

H5 Age of the firm International insolvency statistics generally show that bankruptcy risk is a

function of the age of the firm Very young firms typically show a very high risk The original Zrsquorsquo-

Score model does not explicitly take age into account age However Altman (1983) noted that

the age of a firm is implicitly considered in the Retained EarningsTotal Assets ratio (X2) which

was regarded as a new ratio in the bankruptcy prediction context A relatively young firm will

probably show a low ratio because it has not had time to build up cumulative profits Thus a

young firm is to some degree discriminated against in the model and its likelihood of being

classified as bankrupt is relatively higher than that of an older firm The incidence of failure is

much higher in the early years of a firm AlthoughDespite that the age of the firm in this way is

implicitly taken into account in X2 we expect that an explicit consideration of the age will improve

the classification accuracy bydue to controlling for the age factor Therefore the fifth hypothesis

(H5) proposes that the performance of the uniform benchmark model based on the original four

financial variables of the Zrsquorsquo-Score model is increasesd when the age of the firm is explicitly taken

into account The fifth hypothesis is can be presented as follows

H5 The modelrsquos prediction accuracy of the model is higher when with the effect of firm

age is higher than that of the model without this effectincluded

21

H6 Industry of the firm The original Zrsquo-Score model was estimated only for manufacturing firms

Altman (1983) stated that it would be ideal to develop a bankruptcy prediction model utilizing a

homogenous group of bankrupt firms If we are interested in a particular industry grouping we

should gather data from bankrupt and non-bankrupt firms in these groupings Previous studies

show that financial distress analysis is influenced by the industry effect (Smith and Liou 2007)

Firms in different industries tend to report different levels of the same financial ratios which may

have an effect on the boundary between bankrupt and non-bankrupt firms This industry effect

may be present in the Zrsquo-Score model especially due to the SalesTotal Assets ratio (X5) This ratio

which showed the least significance on a univariate basis while making but it had made a very

significant contribution to the discriminant power of the multivariate model Altman (1983)

recognized the potential industry effect due to a the wide variation among industries in asset

turnover and specified the Zrsquorsquo-Score model without X5 However also the Zrsquorsquo-Score model was

also estimated using the original sample of manufacturing firms In our analysis the uniform

benchmark model based on the original four financial variables of the Zrsquorsquo-Score model is

estimated for a statistical sample representing different industries Therefore the sixth

hypothesis (H6) assumes that an explicit consideration of industry will improve the classification

accuracy of this benchmark model H6It can be expressed in the following form

H6 The modelrsquos prediction accuracy of the modelis higher whenith the effect of industry is

higher than that of the model without this effectincluded

H7 Country of origin The original Zrsquorsquo-Score model has been estimated only for USUS firms It

can be expected that the international applicability of the model to other countries is affected by

22

country-specific differences The eEconomic environment legislation culture financial markets

and accounting practices in a country may affect the financial behavior of firms and the boundary

between bankrupt and non-bankrupt firms These factors may potentially weaken the

classification performance of the model in for other countries outside other than that e one fore

country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the

seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm

into account will improve the classification accuracy of the benchmark model In our empirical

study the country effect will beis assessed by including a variable of for country risk Therefore

the seventh hypothesis is presented as follows

H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country

risk is higher than that of the model without this effectincluded

5 Empirical data and statistical methods

51 Sample of firms

The principal data of for this study are were extracted from the ORBIS databases of Bureau Van

Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the

moment of sampling contained administrative information on more than 50 million European

firms However income statement and balance sheet information was available for

approximately 8 million companies More than 99 of the companies covered in this database

are private companies from different various industries justifying the use of the Zrsquorsquo-Score model

instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all

industrial groupings and for both private and public entities (Altman 1983 124 Altman and

Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope

23

of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all

industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly

used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that

international comparability may be a problem when administrative firm-level data are

internationally pooled For administrative data Although the definition of variables is usually less

harmonized for administrative data tThis is less of a problem in the ORBIS database because of

the common international format of balance sheets For example although some discrepancies

in profitloss statements may arise because of differences in fiscal systems across countries

balance sheet variables largely adhere to international standards

AThere are a number of factors that influence the international applicability of bankruptcy

prediction models These factors include accounting legislation and practice creditor rights and

investor protection judicial efficiency corporate governance bankruptcy protection and

insolvency management and firm risk-taking These factors strongly differ between European

and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-

Score model outside Europe First it is especiallyparticularly important to include the USUS as

because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It

is include d in our study as well because it has the largest market capitalization in the world

Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it

applicable to emerging market companies Therefore we include firms from China and Colombia

which represent two very culturally and institutionally very dissimilar emerging market

countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt

24

firms ie the limit we set for European countries) from ORBIS World were not

available Thus the results are also estimated and tested for three non-European countries (the

United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-

Score modelrsquos performance The samples of firms from these countries are were extracted from

ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms

from around the world

SFor statistical sampling several requirements are set for the statistical sampling of the empirical

data First we require that the firm to be selected be an industrial (non-financial) company

Second its owners must have limited liability (so partnerships and sole proprietors are left out

of the study) Third we set a minimum requirement for the size of the firm Because the financial

ratios of very small firms are generally too unstable for a failure prediction model these firms

are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have

exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we

include in our estimation sample firms from all European countries and 3 pre-selected non-

European countries where the number of failed firms is more greater than 60 If the number of

failed firms for any European country is less than 60 the firms from this country are only included

only in the test sample For qualifying European countries the firms are randomly classified in

the estimation and test samples so that the number of firms is approximately equal in both

samples Thus our estimation data include firms from 28 European and 3 non-European

countries Fifth all failed firms that fulfill the above requirements are included in our samples

However if the number of non-failed firms in a country is very high a sample is randomly

25

selected from that country Finally the time span of fiscal years potentially available for this study

ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in

the database are from a financial period within 2007 and 2010 earlier years are also excluded

for non-failed firms for comparability All qualifying observations of non-failed firms from the

years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses

of failed firms to the last most recent financial statements available before failure The four

independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize

outliers

Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test

data by country The estimation sample includes data from 2602563 non-failed and 38215

failed firms from 28 European and 3 non-European countries The test sample is slightly larger

because it includes data from 31 European and 3 non-European countries From For the country

of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt

firms The available US data that were available consist only of listed (and delisted) firms From

China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are

included in the estimation sample although there are only 32 such firms altogether7 The Chinese

datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status

are separately analyzed separately only in for the test data8 ST firms are listed firms suffering

6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor

26

from serious financial difficulties Excluding the special US data and the two non-private Chinese

datasets 994 of the observations in the data are private firms

(Table 1 here)

52 Status of failed firms

ORBIS has five classes for potentially active firms (active default of payment receivership

dormant and branch) and seven classes for inactive firms that no longer carry out business

activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation

branch and no precision) From Among these classes only active is selected to represent non-

distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by

considering (with exceptions described below) a firm failed if its status in ORBIS is stated as

bankruptcy However because of the small number of bankrupt firms in some countries we also

consider receivership (active) firms as failed even if they are active These firms generally suffer

from serious financial distress However firms in liquidation are generally not included in the

sample of failed firms Firms in liquidation may depending on the country contain firms that

have ceased activities due to reasons other than failure (mergers discontinuing the operations

of a daughter company or of a foreign branch etc) Therefore for most countries we select only

firms that are coded as being bankrupt or under receivership However there are a number of

special cases where failed firms are coded under a different status heading These countries or

samples are the following

Country Status categories Bulgaria In liquidation Bankruptcy

27

Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the

study (for example Switzerland) If a country had only a very small number of failed firms it was

dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries

including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status

classes (including the bankruptcy category) are not completely homogenous within European

countries due to different legislations although there are obvious similarities in insolvency acts

(Philippe et al 2002) China is a special case because it includes samples with three different

criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are

two different samples (liquidation and receivership)

53 Statistical methods

In this study seven research hypotheses are drawn for statistical testing The statistical analysis

will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The

classification performance of the original model is assessed by the AUC (Area Under Curve)

measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close

connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random

28

model and 05 for a model with an average classification performance In SAS software is used

for all statistical analysess SAS software is used

The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is

tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical

method (multiple discriminant analysis or MDA) The problem is that the estimation sample

includes different numbers of failed and non-failed firms from 31 countries In the original Z-

Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from

the USUS Following the characteristics of these data we weight the firms so that the weights

for the failed and non-failed firms are equally In so doingthis way the non-proportional

sampling in from different countries does will not affect the re-estimated model The number of

firms from different countries however varies significantly leading to greater weights for larger

countries To avoid this problem the observations are also weighted so that each country has an

equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using

these weighted data and the resulting AUC is compared with that based on the original model

The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score

model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is

based on less- restrictive statistical assumptions than MDA In this estimation the dependent

variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA

does not require that independent variables be multivariate normal or that groups have equal

covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA

29

creates a score (logit) score L for every firm It is assumed that the independent variables are

linearly related to L This score or logit is used to determine the conditional probability of failure

as follows

= (eq 4)

where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the

original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing

the statistical significance of the difference between AUCs for this LR model and for the re-

estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it

is used as a benchmark for further statistical AUC comparisons because LR is applied as the

principal method in the testing of the remaining research hypotheses

The third hypothesis (H3) is associated with the performance effect of taking account of the

bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on

the following logit

(eq 5)

where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model

bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and

D1 = 1 when year = 2008 0 otherwise

D2 = 1 when year = 2009 0 otherwise

D3 = 1 when year = 2010 0 otherwise

)|1( XYp )( 441101

1

1

1XbXbbL ee

3

1

4

10

jjji

ii DcXbbL

30

The dummy variables do not directly refer to the bankruptcy year which that is not given in the

data basedatabase but rather to the last available year before bankruptcy For failed firms there

is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is

the base category If the AUC of this extended LR model statistically significantly exceeds the AUC

of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3

Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third

hypothesis above However for each hypothesis appropriate variables are used instead of the

year dummies Hypothesis H4 addresses the performance effect of taking size into account the

size and it will beis tested using by adding two additional variables measuring firm size in to the

LR -model In this LR model size is measured by the natural logarithm of total assets and its

squared form In this way the effect of logarithmic size can be reflected by in a function following

the second-order parabola Hypothesis H5 predicts that the classification performance improves

when the age of the firm is explicitly taken into account When testing this hypothesis the

category 6ndash14 years category is used as the base category and two dummy variables are

incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis

H6 looks at whether is associated with the classification performance is affected bythe effect of

the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt

is tested here using dummy variables for seven industries (D1 restaurants and hotels D2

construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and

water production D7 information technology) with all other industries acting as the base

category

31

Hypothesis H7 predicts that the explicit consideration of the country of origin will improves

classification performance This hypothesis is tested by using country risk measures instead ofnot

using dummy variables for countries but using country risk measures instead The country risk of

each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the

annual closing of accounts The rating is numerically recoded such that the best rating AAA

equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating

D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial

ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-

modelLR models with the original four financial ratios and the additional variables specified in

the hypotheses are estimated for all data In addition an LR model including all additional

variables is estimated for all data to assess the simultaneous effect of all variables Finally six of

the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not

included in this country- level testing hypothesis H7 is not tested because the additional variable

(country risk) is constant within the country

6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models

Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score

model for all data The variation in the ratios is significant as shown by the standard deviation

and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-

failed firms are close to each other indicating a symmetry of distributions However this is not

32

the case for the failed firms For the faileddistressed firms the median exceeds the mean for

these three ratios indicating negatively skewed distributions For X4 (BVETD) the means

significantly exceed the median for both failed and non-failed firms indicating a positively

skewed distribution For each of the four variables both the mean and the median are higher for

non-failed firms than for failed firms which is consistent with the expectations The difference

between the means of the non-failed and failed firms is larger in the original USUS data than in

our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is

approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of

EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may

indicate lower classification accuracy than in the original sample

(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates

(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the

large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model

Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same

statistical method or MDA The coefficients here are negative because the models are estimated

using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight

than in the original USUS data while the weights of WCTA and BVETD have proportionally

decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the

logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR

model These coefficients are directly comparable with those of the MDA model which isas

expected for the this exceptionally large sample The differences in the coefficients of the original

33

four variables among the eight LR-modelLR models (Models 1-8) are small For each model the

coefficient of BVETD is very close to zero This indicatinges that the original four variables and

the additional variables are quite independent of each other

(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-

modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate

that after 2007 (the base category) this risk of failure significantly decreased year by year The

base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due

to as a result of the global financial crisis The crisis played a significant role in the failure of key

businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The

effects were especially felt in Europe The coefficients of Model 4 for the size variables show that

the contribution of size on to the logit (risk measure) is atreaches its maximum value when

logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model

5 confirms the riskiness of young firms because the risk of failure is very high for newly founded

firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The

coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky

industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is

statistically significant (because of the large sample) but negative and very close to zero Finally

the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7

62 All data performance of the Zrsquorsquo-Score models

34

Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1

refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of

all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average

accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)

Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its

performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The

lower part of the table shows the AUCs for the countries included only in the test data The

performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)

samples It is also low and for the liquidation firms in the United Kingdom

The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5

presents the differences of these medians between the non-failed and failed firms by country

This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of

medians This correlation which is high for each financial ratio showing that the effects of the

ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large

except for EBITTA which This implies with the exceptionally high AUC that the ST firms

systematically differ from the non-ST firms although the differences are not extremely large The

differences between the medians are very large in Poland for each ratio justifying the high AUC

They are also large and in Finland and in the Czech Republic where the difference in EBITTA is

average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in

all four ratios are below the average a result thatwhich is obviously associated with a low AUC

35

In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even

negative In Iceland and Ireland the differences only in EBITTA are exceptionally small

(Table 4 here)

(Table 5 here)

Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by

MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)

supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is

at approximately the average level The re-estimation of the coefficients has led to improved

classification accuracy in a number of countries especially in Bosnia China (ST) Norway and

Greece However it has impaired the classification accuracy in the UK and China (delisted)

Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-

modelLR model) showing the effect of the estimation method For the benchmark model the

AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between

among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR

model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country

This result was expected because the coefficients of the models are directly comparable The

similar results for the models may also indicate that the independent variables conform to

multinormality This last latter result confirms what most researchers in the field of default

classification models have concluded that the accuracy levels of MDA and logistical regression

36

models are extremely similar despite any statistical violations such as normality between MDA

and logistical regression models

Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)

than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects

are not positive for all countries The effects are positive for example for Russia Estonia

Germany Ireland and Latvia but these effects are not statistically significant There are

statistically significant negative effects for China (ST) and Serbia There are notable negative

effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are

due to the exceptional annual distributions of failed firms in these countries For the countries

with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the

countries with positive effects it is only a few percent For each group non-failed firms are quite

equally distributed over years When the coefficient of D3 is very low (-0666) it strongly

decreases the risk estimates of most failed firms in the former countries but of only a few failed

firms in the latter countries This which leads to the observed effects H3 is supported by

the level of the whole sample However in some country samples (at the level of individual

levelies) H3 is not supported

Model 4 (LR-modelLR model with size variables) performs better than the benchmark model

which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads

and to significant improvements in the AUC for eg China (delisted) and Austria For China

(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC

37

also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs

in a few countries but thise decrease is not significant Model 4 is based on the four original

variables and the size effect following a second-order parabola This kind of size effect is very

small for micro firms but increases when approaching its maximum value in middle-sized firms

The countries with a positive change in AUC typically have data where in which the percent of

failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized

failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In

contrast the countries with a negative change in AUC have data where in which the percentage

of failed micro firms is exceptionally high (70-80)

Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly

the same AUC as the benchmark model (AUC = 0748) However the difference is positive and

statistically significant giving at least marginal support to H5 (age effect) For nearly all countries

the effect of the age on AUC is small For Austria however thise effect is however positive and

significant For Austria the percentage of non-failed young firms (less than 6 years) among non-

failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed

firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong

positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only

a few non-failed firms which This obviously leads to an improvement in AUC

Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC

supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its

38

AUC is notably higher than the benchmark AUC only in only a few countries such asfor example

France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg

restaurants and hotels and the information technology industryies but it has a positive effect

for on the construction and manufacturing industries For the countries with a positive effect on

AUC the percent of non-failed firms in restaurants and hotels and information technology is

high while that of failed firms is low For the risky industries (construction and manufacturing)

these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many

failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)

firms Therefore the AUC will be increasesd This situation is reversed for the countries with a

negative effect on AUC (Austria and Slovenia) The samples of these countries include a high

percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC

Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher

classification performance (AUC = 0749) than the benchmark model This result gives only very

weak support for H7 (country of origin effect) However for each country the resultant AUC is

nearly identical to that given by the benchmark model This result was expected due to the

negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7

This result implies that country risk hasdoes not have noany effect on the boundary between

bankrupt and non-bankrupt firms

Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional

variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to

39

the benchmark AUC in all test data However the effect on the AUCs largely varies and is either

negative or positive in different countries The positive effect is large in several countries such

as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative

effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show

that the inclusion of additional variables in the original model will usually increase the AUC but

not in every country

The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with

the USUS firms being unlike the majority of other firms in this study being listed or delisted

companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese

firm samples is associated with very small differences between the medians of the non-failed and

failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable

with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed

firms is very high Prior studies based on Chinese ST -firms have also demonstrated good

predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for

additional research and modeling work regarding unlisted and delisted Chinese firms

63 Country-level data performance of the Zrsquorsquo-Score models

The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform

all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the

different models estimated for each country separately (country-level models) In this table the

ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated

40

from the country data this benchmark will beis clearly outperformed by the resulting MDA

(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and

Sweden) However these results give only weak support for H1 at the country level because the

effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and

2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs

given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed

by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation

method)

Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC

than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3

leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United

StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in

nearly every country supporting H4 This improvement is significant however only in the United

StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark

model but for which it is only 0710 for the benchmark model It This improvement is also notable

in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age

category dummies) gives higher AUCs than the benchmark model for several countries but none

of the improvements are significant The positive effect is particularly strong especially in

Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only

weakly supports H5 because these negative effects are relatively small

41

Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects

on AUCs when compared with the benchmark However the only significant effect is the negative

effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the

industry dummies However there are notable positive effects on the AUC in for example

France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak

support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in

AUC compared with the benchmark model in Colombia Iceland and the Unites States It also

leads and to notable improvements in AUC at least in Belgium Denmark Estonia France

Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are

found only in Austria and Slovenia Thus as a conclusion we can concludestate that the

classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can

remarkably increases in most countries when different effects are taken into account by

additional variables This increase is found in most European countries and in Colombia and the

United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-

modelLR model and it can only be slightly improved only slightly by additional variables

(Table 6 here)

7 Summary of the study and a suggested extension

The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model

originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is

assessed using a very large international data set We test how the original version of the Zrsquorsquo-

42

Score model performs in different countries and how re-estimations using another statistical

method and different additional variables affect the classification performance when the data

are very heterogeneous For this kind of testing seven research hypotheses on classification

performance are extractedformulated These research hypotheses are tested for all data and

separately for country data (country-level analysis) The estimation data are from 31 countries

and the results are validated for 34 countries The countries are mainly from Europe but three

non-European countries are included (China Colombia and the United StatesUS) The statuses

used in the classification are mainly bankruptcyactive but receivership firms are also considered

to have failed In the Chinese data ST (special treatment) and delisted firms are also separately

analyzed as failed firms

The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very

satisfactorily in an international context The effects of the four financial ratios on performance

are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small

contribution in re-estimation The original model performs very well in several countries such as

Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only

marginally improved the classification performance thus weakly supporting the obsolescence

hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust

across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion

holds for the re-estimation of the model using LRA because the performance results are very

similar with to those of MDA (H2) The use of additional variables in the model generally

improvesd the classification accuracy of the original model but the results for countries are

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

2

information provided by additional variables helps boost the classification accuracy to a much

higher level

JEL codes G15 G32 G33

Keywords Z-Score bankruptcy failure default financial distress

3

1 Introduction

The first multivariate bankruptcy prediction model was developed by EI Altman (1968) in the

late 1960s After this pioneering work the multivariate approach to failure prediction spread

worldwide among researchers in finance banking and credit risk Failure prediction models are

important tools for bankers investors asset managers rating agencies and even the distressed

firms themselves The banking industry as the main provider of financing in the economy is

especially interested in minimizing the level of non-performing loans in order to maximize profit

on the credit activity and banks seek to reduce their own risk of default Another issue of interest

for bankers is capital adequacy and an the internal ratings-based approach encouraged by Basel

2 The Z-Score model has become a prototype for many of these models Asset managers and

investors need to have reliable tools for that can help them the select ion appropriate of

companies to be added forto their portfolios Financial distress of companies is on the one hand

detrimental to investor returns but on the other hand risk may provide opportunities for high

returns on short-sale strategies Rating agencies assess the risk of the entities and of securities

issues and thus they need to have a tool to predict default In addition Altman (1983 1993 and

2006) has suggested that the management of distressed firms can utilize the Z-Score model as a

guide to a financial turnaround

The approach used for bankruptcy prediction has been evolving evolved over time Beaver (1966

1968) used univariate analysis for selected ratios and found that some of them had very good

predictive power Altman (1968) moved significantly forwardmade strides by developing a

multiple discriminant analysis model (MDA) called the Z-Score model The next two decades

Commented [AFS1] Per the journal guidelines each citation must have a corresponding reference in the reference list Please note that there is no corresponding reference for this citation Please supply the missing information where indicated in the reference list at the end of the text

4

brought saw additional contributions to financial distress research For example such as Ohlson

(1980) with proposed a logit model1 Taffler (1984) with offered a Z-Score model for the UK and

Zmijewski (1984)2 who used a probit approach Dimitras et al (1996) reviewed 47 studies on

business prediction models They summarizinged the methods employed and the variety of

ratios used Discriminant analysis was the prevailing method and the most important financial

ratios came from the solvency category with profitability ratios being also being important

The next summary of different approaches to credit risk analysis was given by Altman and

Saunders (1998) Balcaen and Ooghe (2006) reviewed models of business failure prediction and

classified 43 models presented in the literature into four categories (univariate models 1 risk

index models 2 MDA models 21 and conditional probability models 19) However they

omitted the rapidly fast-growing type of number of models based on the option pricing theory

and contingent claims (eg Vassalou and Xing 2004 commercialized into the KMV model) and

they omittedas well as hazard models (eg Shumway 2001) Kumar and Ravi (2007) reviewed

128 statistical and artificial intelligence models for bank and firm bankruptcy predictions of banks

and firms with paying special attention paid to the techniques used in the different models

These authorsy noted that neural networks were the most popular intelligence technique In

their review Jackson and Wood (2013) presented the frequency of occurrence of specific

forecasting techniques in the prior literature The five most popular techniques were as follows

1 Altmanrsquos Z-score and Ohlsonrsquos O-score have beenwere compared by Dichev (1998) 2 A re-estimation of Ohlsonrsquos and Zmijewskirsquos models was presented by Grice and Dugan (2003)

Commented [S Ed2] Abbreviations and acronyms are often defined the first time they are used within the main text and then used throughout the remainder of the manuscript Please consider adhering to this convention

5

(1) multiple discriminant analysis (2) logit models (3) neural networks (4) contingent claims and

(5) univariate analysis

Recent reviews of the efficacy of these models have been delivered offered by Agarwal and

Taffler (2008) Das et al (2009) and Bauer and Agarwal (2014) These reviews takeing into

account the performance of accounting-based models market-based models and hazard

models These three model types of models prevail in the literature According to Agarwal and

Taffler (2008) there is little difference in the predictive accuracy of accounting-based and

market-based models however the usage of accounting-based models allows for a higher level

of risk-adjusted return on the credit activity Das et al (2009) showed that accounting-based

models perform comparably to the Merton structural market-based approach for CDS spread

estimation However the comprehensive model which used both sources of variables

outperformed both models In Bauer and Agarwal (2014) hazard models using accounting and

market information (Shumway 2001 and Campbell et al 2008) were compared with two other

approaches the original Tafflerrsquos (1984) Z-score model which that was tested in by Agarwal and

Taffler (2008) and a contingent claims model using Bharath and Shumwayrsquos (2008) approach

Using UK data tThe hazard models were superior in UK data in bankruptcy prediction accuracy

ROC (Receiver Operating Characteristic) ROC analysis and information content

Despite the vast amount of research on failure prediction the Z-Score model introduced by

Altman (1968) has been applied worldwide Thus although the Z-Score model has been in

existednce for more than 45 years it is still used as a main or supporting tool for bankruptcy or

6

financial distress prediction or and analysis both in research and in practice Therefore question

arises of why focus on the accounting-based model which is at least occasionally outperformed

by other models Most of the firms operating on the market are privately held and therefore

there are no market data (eg stock prices) are available Theose firms are usually financed by

banks which are obligated to assess their creditworthiness and monitor their performance In

the case of internationally active banks it is especially important to use a universal model across

the banking group for distress prediction for provisioning and for economic capital calculation

In following subsequent regulatory requirements they need to validate models and prove their

efficacy Thus we found it useful to analyze the performance of an accounting-based model in

an international context

In our study we use a large international sample of firms to assess the classification performance

of the Zrsquorsquo-Score model in bankruptcy prediction3 We analyze its the modelrsquos performance in for

firms from 31 European and three non-European countries (China Colombia and the USUS) T

these firms are mostly privately held and aA large number of firms are from non-manufacturing

industries Therefore we use the version of the model developed by Altman (1983) for private

and public manufacturing and non-manufacturing firms (Zrsquorsquo-Score model) Such an extensive

international analysis of the performance of the Z-Score modelrsquos performance has not been

presented thus farto date We regard our review and analysis as important contributions to the

economic literature

3 We use bankruptcy failure default and financial distress as equivalents

7

The paper is structured as follows In the introduction we present a brief summary of the

development of failure prediction research starting beginning with Beaver (1966) and Altman

(1968) In the second section we summarize the original Z-Score model (1968) and its extension

for private firms that is the Zrsquo-Score and Zrsquorsquo-Score models (1983) IThen in the third section we

present the results and conclusions from the literature review on these models The fourth

section presents seven research hypotheses on the performance of the Zrsquorsquo-Score model for

empirical analysis In the fifth section the empirical data and statistical methods are discussed

while the sixth section presents empirical findings Finally the seventh section summarizes the

study

2 Classic Z-Score models

21 Z-Score model for public firms

Altmanrsquos (1968) initial sample was composed of 66 corporations with 33 firms in each of the two

groups The bankrupt group (Group 1) consisted of manufacturers that filed a bankruptcy

petitions under Chapter X of the National Bankruptcy Act during the period 1946ndash1965 period

The mean asset size of these firms was 64 million USD with a ranginge of between 07 and 259

million USD Altman recognized that this group was not homogenous with respect to size and

industry although all firms were relatively small and from manufacturing industries Therefore

he attempted to make a carefully selection select of non-bankrupt firms (Group 2) Group 2

consisted of a paired sample of manufacturing firms chosen on a stratified random basis These

firms were stratified by industry and size with the asset size range restricted to 1ndash25 million USD

Altman eliminated both the small firms (less than 1 million US dollars in total assets) because of

8

a lack of data and the very large firms because of a lack of data because offor small firms and the

rarity of bankruptcies among these firms in that period of large firms He did not match the asset

size of the two groups exactly and therefore the firms in Group 2 are were slightly larger than

those in Group 1 The data collected for the firms in both groups were from the same years For

Group 1 the data were derived from financial statements one reporting period prior to

bankruptcy

Using financial statements Altman compiled a list of 22 potentially important financial ratios

based on financial statements for evaluation He classified these variables into five standard ratio

categories liquidity profitability leverage solvency and activity ratios These ratios were

chosen based on their popularity in the literature and their potential relevancey to the study The

final discriminant function estimated by Altman (1968) is as follows

Z = 0012X1 + 0014X2 + 0033X3 + 0006X4 + 0 999X5 (eq 1)

where X1 = Working CcapitalTotal Aassets X2 = Retained EarningsTotal Aassets X3 = Earnings before Iinterest and TtaxesTotal Aassets X4 = Market Vvalue of EequityBook Vvalue of Ttotal Lliabilities X5 = SalesTotal Aassets Z = Overall Index 22 Zrsquo-Score and Zrsquorsquo-Score models for private firms

The original Z-Score model was based on the market value of the firm and was thus applicable

only to publicly traded companies Altman (1983) emphasized that the Z-Score model is a

intended for publicly traded firms model and that ad hoc adjustments are not scientifically valid

9

Therefore Altman (1983) advocated a complete re-estimation of the model substituting the

book value of equity for the market value in X4 Using the same data Altman extracted the

following revised Zrsquo-Score model

Zrsquo = 0717X1 + 0847X2 + 3107X3 + 0420X4 + 0 998X5 (eq 2)

where X4 = Book value of equityBook value of total liabilities with the other variables being

the same as those in the original (1968) Z-Score model

DAltman ue to the lack of a private firm database Altman did not test the Zrsquo-Score model on a

secondary sample due to the lack of a private firm data base However he analyzed the accuracy

of a four-variable Zrsquorsquo-Score model that excludeding the SalesTotal assets ratio X5 from the

revised model because of a potential industry effect The industry effect that is more likely to

take place when this kind of industry-sensitive variable (asset turnover) is included in the model

Altman then estimated the following four-variable Zrsquorsquo-Score model (Altman 1983)

Zrsquorsquo = 325 + 656X1 + 326X2 + 672X3 + 105X4 (eq 3)

The EBITTotal assets ratio X3 made the highest contributioncontributed most to the

discrimination power in this version of the model The classification results for the Zrsquorsquo-Score

model were identical to the revised five-variable Zrsquo-Score model In the currentis study our

empirical analysis focuses on the performance of the Zrsquorsquo-Score model version in predicting

bankruptcy prediction wheredue to it has its widest scope that is because as it is intended for

both privately held and publicly listed firms and for both manufacturing and non-manufacturing

firms

10

3 Literature review

We searched for papers published from the year 2000 onward 4 in prominent international

journals or and books We selected 31 articles in which the Z-Score was either used either as a

failure prediction proxy or was assessed mostly in terms of predictive ability Of the 31 studies

Altmanrsquos Z-Score model5 was used in 16 cases as the measure of distress or of financial strength

In 13 studies Altmanrsquos original model was modified and (or) verified including re-estimation and

in 2 cases it was used solely for the robustness check As Pindado et al (2008) noted the Z-Score

was also used for other purposes such as the evaluation of the costs and benefits of covenants

in bonds the choice of the type of debt type (bank vs non-bank private or public) and the

relationship between investment and internal funds We focused on this part of the literature

that verified and (or) modified Altmanrsquos original model The wide broad usage of the Z-Score

model foras a measuringe of financial distress and performingas well as for the robustness checks

indicates that it is acceptabilityed as a reasonable simple and consistent measure of the

distressed firms

In the case of the modification of the The most common modification to the Z-Score model

model the most common change was the use of other estimation techniques or country-specific

data The use of Altmanrsquos ratios in combination with techniques other than MDA has improved

the its prediction capability TAdditionally the application of new data of on both US and non-US

4 Research devoted to the application of the Z-Score model before 2000 was reviewed by Grice and Ingram (2001) 5 Most of the models focused on stock- exchange- listed firms thus the Zrsquo-Score and Zrsquorsquo-Score models were not used

11

firms also improved model performance Kwak et al (2005) applied used Multiple Criteria Linear

Programming (MCLP) to model 5 Altman and 9 Ohlson variables They used with data on US

bankrupt US firms from 1992 to 1998 and nearly 6 times more matched US control firms The

MCLP approach performed better than Altmanrsquos original model and gave results similar to or

better than those of Ohlsonrsquos original model The original models were not recalculated and the

authors referred solely to their original prediction rates only Merkevicius et al (2006) using data

on US and Lithuanian firms developed a hybrid artificial -discriminantte model combining MDA

and an unsupervised learning artificial neural network (called the SOM-Altman model) with the

use of data on US and Lithuanian firms Thise hybrid SOM-Altman model reached a high

prediction rate of 9235 Xu and Zhang (2009) applied Altmanrsquos Z-score Ohlsonrsquos O-score and

Mertonrsquos distance-to-default (D-score) models to Japanese firms to check whether these models

are useful for bankruptcy prediction in Japan They also ldquomergedrdquo theose models into a new C-

score model They then introduced variables unique to Japan to check whether corporate

structure variables have any impact on the probability of bankruptcy t They called this the model

X-score model These two models were useful for Japanese firms in predicting bankruptcy

prediction but the market-based model was the most successful In summary the developed C-

score and X-score (with country-specific variables) models improved bankruptcy prediction

Tinoco and Wilson (2013) used the original Z-Score as one of the benchmarks to assess the

performance of their model developed for UK listed companies with combined accounting

market and macroeconomic data Altmanrsquos Z-Score presented very good classification accuracy

in the case of financially distressed firms (81 vs 87 for the new model) however it was less

correct for financial health prediction Another modification was introduced by Lyandres and

12

Zhdanov (2013) who posed the question of whether the inclusion of variables related to

investment opportunities improved the predictive power of three models (Altmanrsquos Z-score

model and Zmijewskirsquos and Shumwayrsquos models) They used three proxies for investment

opportunities (market-to-book value-to-book and RampD-to-assets) The measures of investment

opportunities were linked to the likelihood of default The inclusion of either of these measures

improved the out-of-sample forecasting ability of all three

The verification of Altmanrsquos model concentrated on its efficacy or on how it compares a

comparison with other accounting-based market-based or hazard models However although

the original Z-Score model was not solely an accounting-based approach because the market

value of equity was utilized we classify it here as an accounting -based approachbecause the

market value of equity was utilized Grice and Ingram (2001) used a new novel data set of US

firms and posed three questions about the efficacy of Altmanrsquos model They concludinged that

the prediction accuracy of Altmanrsquos model had declined over time and that the coefficients of

the model had significantly changed which means that the relation between the financial ratios

and the signs of financial distress had changed over time The model was sensitive to industry

classification (more efficient for manufacturing firms than for non-manufacturing firms) but was

not sensitive to the type of financial distress Similar conclusions were drawn by Grice and Dugan

(2003) regarding Ohlsonrsquos (1980) and Zmijewskirsquos (1984) models

Hillegeist et al (2004) compared Altmanrsquos Z-score and Ohlsonrsquos O-score (with original and

updated coefficients) with the model based on BSM option pricing (BSM-Prob) They used

13

relative information content tests to compare the out-of-sample performance BSM-Prob

outperformed accounting-based models The conclusions were robust to various modifications

of accounting-based models such as updated coefficients industry effects and the separation

of variables Chava and Jarrow (2004) employed an extended bankruptcy database of US listed

firms to prove the superiority of Shumwayrsquos model (2001) over Altmanrsquos (1968) and Zmijewskirsquos

(1984) models The authors re-estimated the models over the 1962ndash1990 period and forecasted

bankruptcies over the 1991ndash1999 period In the case of Shumwayrsquos model 744 (in the first

decile) of the bankruptcies were correctly identified for with Altmanrsquos model it was 632 and

for with Zmijewskirsquos model it was 432 Shumwayrsquos market-based model also outperformed

accounting-based models in terms of the ROC curve (091)

Reisz and Perlich (2007) used the barrier option for bankruptcy prediction and compared its

discriminatory power with other market-based models and Altmanrsquos Z-Score and Zrdquo-Score The

dataset covered nearly 6000 industrial firms over the period 1988ndash2002 period The authors

proved the domination superiority of Altmanrsquos Z-Score and Zrdquo-Score models for short-term (up

to 1 year) bankruptcy prediction For medium- and long-term bankruptcy prediction their barrier

option model outperformed the other models Pindado et al (2008) developed an ex -ante model

for the estimation of financial distress likelihood (FDL) using awith the use of panel data

methodology and they presented a financially (not legally) based definition of distress Their

sample covered 1583 US companies and 2250 companies from other G-7 countries for the

period 1990ndash2002 period They used a re-estimated Z-Score as a benchmark The FDL model

outperformed the Z-Score model in terms of stability and classification power for different

14

countries and time periods In the case of the re-estimated Z-Score model only profitability and

retained earnings kept maintained their significance for different years and countries

Wu et al (2010) evaluated the performance of five models (Altman 1968 Ohlson 1980

Zmijewski 1984 Shumway 2001 and Hillegeist et al 2004) using an up-to-date data set for

US listed firms Based on these models the authors built their own integrated model that is a

multi-period logit model with an expanded set of variables The integrated model which

combined accounting and market data as well as firmsrsquo characteristics outperformed the other

models Altmanrsquos Z-score performed poorly in compared withison to the other 4 models

Shumwayrsquos model performed best Hillegeist et alrsquos model performed adequately and Ohlsonrsquos

and Zmijewskirsquos models performed adequately although their performance deteriorated over

time

Jackson and Wood (2013) tested 13 different models of bankruptcy prediction and assessed their

efficacy using the ROC curve They selected three single- variable models three accounting-based

models (including Altmanrsquos Z-Score) in two versions (with up-dated coefficients and a neural

network approach) and four contingent claims models The contingent claims models and the

latter group outperformed the other models The best four best models were contingent claims

models based on European call and barrier options Although tThe predictive performance

improved withIn the case of the application of the neural network to accounting-based models

the predictive ability improved but it was still lower than within the case of the market-based

models

15

Acosta-Gonzaacutelez and Fernaacutendez-Rodriacuteguez (2014) used genetic algorithms with the Schwarz

information criterion (GASIC) for variable selection combined with the logit model for bankruptcy

prediction Altmanrsquos Z-Score model was used as one of two benchmarks for the authorsrsquo model

evaluation For one- step- ahead forecasting Altmanrsquos model was better in at predicting failed

firms but the type II error was high For two- and three- steps- ahead forecasts the performance

of the models was similar forin terms of failed firms but forin the case of non-failed firms and

the prediction accuracy of Altmanrsquos model was worse For four- steps- ahead forecasts the GASIC

model outperformed the other models forin the case of failed firms and but it performedwas

comparablye forin the case of non-failed firms

In general Altmanrsquos Z-Score model underperformed in compared withison to market-based

models but there was also evidence indicated that it performed well for short-term distress

prediction The question of whether market-based models are perform better than accounting-

based models has been raised many times (eg discussion in Das et al 2009 Bauer and d

Agarwal 2014) Our purpose is not to contribute to this strand of research but rather to focus on

the accounting-based approach In this study we analyze primarily analyze privately held firms

so there are by definition no market data for these firms In this case aAn accounting-based

approach is the only solution that could be appliedapplicable in this case by banks as lenders or

by investors holding debt securities of firms not listed on the stock exchange Thus far most of

the studies have concentrated on the US market only a few of them have used data from other

16

countries such as Japan the UK Lithuania or the G-7 countries Our analysis is a significant

extension of the previous research

4 Methodology and research hypotheses

The literature survey shows that the Z-Score model (publicly traded firms) the Zrsquo-Score model

(private manufacturing firms) and the Zrsquorsquo-Score model (private and publicly

traded manufacturing and non-manufacturing firms) have been adapted for different purposes

In this study we are first interested first in assessing the performance of the original Zrsquorsquo-Score

model in classifying bankrupt and non-bankrupt firms in an international context with special

focus on the European market However we also validate the results in a set of non-European

countries to generalize the results for circumstances outside Europe Second we will re-estimate

the model using extensive international data and then use the re-estimated Zrsquorsquo-Score model as a

benchmark for assessing the effects of different factors on the modelrsquos performance in terms of

classification accuracy We will assess the effects of the following five factors on this

performance year of bankruptcy size of firms age of firms industry and country of origin WIn

all we will test a set of hypotheses based on the effects of the model on performance on two

different levels First we will test a set of hypotheses on a pooled set of all firms and second on

data from each country individually CIn compared withison to the previous research our this

studyrsquos contribution is the focus on an international context not just simply the model

application or re-estimation for of a given countryrsquos data Because weour aim is focusing on the

performance of the Zrsquorsquo-Score mModel when using ain large body of international data the

research hypotheses are of a technical nature and are presented given as follows

17

H1 Obsolescence of the coefficients The Zrsquorsquo-Score mModel was originally estimated using the

same sample of firms that was used to develop the original Z-Score mModel The bankruptcies

in the estimation data occurred during the period 1946ndash-1965 period Thus the oldest

observations are from nearly seventy years ago during the post-war period Altman (1983)

recommended utilizing data as near to the present as possible when developing a bankruptcy

prediction model It is obvious that firmsrsquo the financial behavior of firms and their business

environment have significantly changed since then Thereforen potentially making the

importance of the financial ratios as reflected by the coefficients of the model may differ from

their original importance reflected by the coefficients of the model Therefore we suggest as

the first hypothesis (H1) that the re-estimation of the coefficients of the four original variables

of the Zrsquorsquo-Score model will improve the classification performance of the model in an

international context This hypothesis is supported by the previous research (eg Grice and

Ingram 2001) and by practice H1 aims to support this evidence on the international level and is

expressed as follows

H1 RThe re-estimatingon of the coefficients of the Zrsquorsquo-Score model improves the its

classification accuracy of the model

H2 Method of estimation The original Zrsquorsquo-Score model has been estimated using MDA However

MDA is based on the ordinary least squares (OLS) method and thus requires assumptions of

multinormality homoscedasticity and linearity which are not often met in empirical financial

ratio analysis Therefore we re-estimate the Zrsquorsquo-Score model using logistic regression analysis

(LRA) to assess the effect of the method of estimation method LRA does not require most of the

18

restricting assumptions of MDA In LRA the multivariate normality of the independent variables

is not required nor are homoscedasticity and linearity For the sake of OLS MDA can be more

useful than LRA for small samples such as the original sample of 66 firms used in the estimation

of the Zrsquorsquo-Score mModel However in a large sample LRA may potentially perform better In this

study we use large samples which is advantageous for LRA Therefore our second hypothesis

(H2) suggests is that the classification performance of the re-estimated Zrsquorsquo-Score model will be

improved when it is estimated using LRA instead of MDA The model re-estimated for the original

variables using LRA and all pooled data is called here the Zrsquorsquo-Score LR-modelLR model The

performance of this re-estimated model is used in this study as the benchmark for further

analyses Thus the second hypothesis is as follows

H2 The prediction accuracy of the logistic regression version of the Zrsquorsquo-Score model is higher

than that of the multiple discriminant analysis version

H3 Bankruptcy year The model based on the relationship between bankruptcy and financial

ratios is likely to be affected by the macroeconomic environment These effects may significantly

decrease the classification accuracy of the model If the model is estimated using data from one

year and it will be applied to data from another year the validity of the model can be questioned

Business cycles inIn terms of economic growth credit policy and interest rates business cycles

can ahave an effect on the boundary between bankrupt and non-bankrupt firms The original Zrsquorsquo-

Score model is estimated using data from the period 1946ndash1965 period which includes several

business cycles Therefore the model is not focused on any specific stage of a cycle and does not

explicitly take into account of the bankruptcy year Altman (1983) suggested gathering data from

19

firms for the last most recent few years when developing a prediction model In this study the

benchmark Zrsquorsquo-Score LR -model will beis estimated for a shorter period than in the original

estimation although it covers several recent years on for different stages of the business cycle in

different countries Therefore the third hypothesis (H3) assumes is that the classification

accuracy of the benchmark model can be increased by explicitly taking into account of the year

of bankruptcy in the estimation of the model estimation The third hypothesis is presented as

follows

H3 The prediction accuracy of the modelrsquos prediction accuracy is higher whenith the

effect of the year of bankruptcy is included is higher than that of the model without the

effect

H4 Size of the firm The boundary between bankrupt and non-bankrupt firms is different for small

and larger firms which decreases the performance of the model estimation when estimated for

data from one size category and isare applied to for data from another size category For the

bankrupt and non-bankrupt firms in the original data for Zrsquorsquo-Score model estimation the range

of asset sizes was ranged between approximately 1 and 25 million US dollars The data did not

include very small or very large firms Altman (1983) regarded the suitability of the original Z-

Score model (and likewisein the same way the Zrsquorsquo-Score model) for all firms as debatable

because of this omission it did not scrutinize very large or very small firms In the currentis study

the benchmark Zrsquorsquo-Score LR -model will beis estimated for data from many size categories from

very small firms to very large firms The fourth hypothesis (H4) assumes that the classification

performance of the uniform benchmark LR -model based on the original four financial variables

20

of the Zrsquorsquo-Score model is improved when the size category of the firm is explicitly taken into

account Thus we present the fourth hypothesis as follows

H4 The modelrsquos prediction accuracy of the modelis higher whenith the effect of size is

includede is higher than that of the model without this effect

H5 Age of the firm International insolvency statistics generally show that bankruptcy risk is a

function of the age of the firm Very young firms typically show a very high risk The original Zrsquorsquo-

Score model does not explicitly take age into account age However Altman (1983) noted that

the age of a firm is implicitly considered in the Retained EarningsTotal Assets ratio (X2) which

was regarded as a new ratio in the bankruptcy prediction context A relatively young firm will

probably show a low ratio because it has not had time to build up cumulative profits Thus a

young firm is to some degree discriminated against in the model and its likelihood of being

classified as bankrupt is relatively higher than that of an older firm The incidence of failure is

much higher in the early years of a firm AlthoughDespite that the age of the firm in this way is

implicitly taken into account in X2 we expect that an explicit consideration of the age will improve

the classification accuracy bydue to controlling for the age factor Therefore the fifth hypothesis

(H5) proposes that the performance of the uniform benchmark model based on the original four

financial variables of the Zrsquorsquo-Score model is increasesd when the age of the firm is explicitly taken

into account The fifth hypothesis is can be presented as follows

H5 The modelrsquos prediction accuracy of the model is higher when with the effect of firm

age is higher than that of the model without this effectincluded

21

H6 Industry of the firm The original Zrsquo-Score model was estimated only for manufacturing firms

Altman (1983) stated that it would be ideal to develop a bankruptcy prediction model utilizing a

homogenous group of bankrupt firms If we are interested in a particular industry grouping we

should gather data from bankrupt and non-bankrupt firms in these groupings Previous studies

show that financial distress analysis is influenced by the industry effect (Smith and Liou 2007)

Firms in different industries tend to report different levels of the same financial ratios which may

have an effect on the boundary between bankrupt and non-bankrupt firms This industry effect

may be present in the Zrsquo-Score model especially due to the SalesTotal Assets ratio (X5) This ratio

which showed the least significance on a univariate basis while making but it had made a very

significant contribution to the discriminant power of the multivariate model Altman (1983)

recognized the potential industry effect due to a the wide variation among industries in asset

turnover and specified the Zrsquorsquo-Score model without X5 However also the Zrsquorsquo-Score model was

also estimated using the original sample of manufacturing firms In our analysis the uniform

benchmark model based on the original four financial variables of the Zrsquorsquo-Score model is

estimated for a statistical sample representing different industries Therefore the sixth

hypothesis (H6) assumes that an explicit consideration of industry will improve the classification

accuracy of this benchmark model H6It can be expressed in the following form

H6 The modelrsquos prediction accuracy of the modelis higher whenith the effect of industry is

higher than that of the model without this effectincluded

H7 Country of origin The original Zrsquorsquo-Score model has been estimated only for USUS firms It

can be expected that the international applicability of the model to other countries is affected by

22

country-specific differences The eEconomic environment legislation culture financial markets

and accounting practices in a country may affect the financial behavior of firms and the boundary

between bankrupt and non-bankrupt firms These factors may potentially weaken the

classification performance of the model in for other countries outside other than that e one fore

country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the

seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm

into account will improve the classification accuracy of the benchmark model In our empirical

study the country effect will beis assessed by including a variable of for country risk Therefore

the seventh hypothesis is presented as follows

H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country

risk is higher than that of the model without this effectincluded

5 Empirical data and statistical methods

51 Sample of firms

The principal data of for this study are were extracted from the ORBIS databases of Bureau Van

Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the

moment of sampling contained administrative information on more than 50 million European

firms However income statement and balance sheet information was available for

approximately 8 million companies More than 99 of the companies covered in this database

are private companies from different various industries justifying the use of the Zrsquorsquo-Score model

instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all

industrial groupings and for both private and public entities (Altman 1983 124 Altman and

Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope

23

of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all

industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly

used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that

international comparability may be a problem when administrative firm-level data are

internationally pooled For administrative data Although the definition of variables is usually less

harmonized for administrative data tThis is less of a problem in the ORBIS database because of

the common international format of balance sheets For example although some discrepancies

in profitloss statements may arise because of differences in fiscal systems across countries

balance sheet variables largely adhere to international standards

AThere are a number of factors that influence the international applicability of bankruptcy

prediction models These factors include accounting legislation and practice creditor rights and

investor protection judicial efficiency corporate governance bankruptcy protection and

insolvency management and firm risk-taking These factors strongly differ between European

and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-

Score model outside Europe First it is especiallyparticularly important to include the USUS as

because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It

is include d in our study as well because it has the largest market capitalization in the world

Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it

applicable to emerging market companies Therefore we include firms from China and Colombia

which represent two very culturally and institutionally very dissimilar emerging market

countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt

24

firms ie the limit we set for European countries) from ORBIS World were not

available Thus the results are also estimated and tested for three non-European countries (the

United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-

Score modelrsquos performance The samples of firms from these countries are were extracted from

ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms

from around the world

SFor statistical sampling several requirements are set for the statistical sampling of the empirical

data First we require that the firm to be selected be an industrial (non-financial) company

Second its owners must have limited liability (so partnerships and sole proprietors are left out

of the study) Third we set a minimum requirement for the size of the firm Because the financial

ratios of very small firms are generally too unstable for a failure prediction model these firms

are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have

exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we

include in our estimation sample firms from all European countries and 3 pre-selected non-

European countries where the number of failed firms is more greater than 60 If the number of

failed firms for any European country is less than 60 the firms from this country are only included

only in the test sample For qualifying European countries the firms are randomly classified in

the estimation and test samples so that the number of firms is approximately equal in both

samples Thus our estimation data include firms from 28 European and 3 non-European

countries Fifth all failed firms that fulfill the above requirements are included in our samples

However if the number of non-failed firms in a country is very high a sample is randomly

25

selected from that country Finally the time span of fiscal years potentially available for this study

ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in

the database are from a financial period within 2007 and 2010 earlier years are also excluded

for non-failed firms for comparability All qualifying observations of non-failed firms from the

years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses

of failed firms to the last most recent financial statements available before failure The four

independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize

outliers

Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test

data by country The estimation sample includes data from 2602563 non-failed and 38215

failed firms from 28 European and 3 non-European countries The test sample is slightly larger

because it includes data from 31 European and 3 non-European countries From For the country

of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt

firms The available US data that were available consist only of listed (and delisted) firms From

China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are

included in the estimation sample although there are only 32 such firms altogether7 The Chinese

datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status

are separately analyzed separately only in for the test data8 ST firms are listed firms suffering

6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor

26

from serious financial difficulties Excluding the special US data and the two non-private Chinese

datasets 994 of the observations in the data are private firms

(Table 1 here)

52 Status of failed firms

ORBIS has five classes for potentially active firms (active default of payment receivership

dormant and branch) and seven classes for inactive firms that no longer carry out business

activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation

branch and no precision) From Among these classes only active is selected to represent non-

distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by

considering (with exceptions described below) a firm failed if its status in ORBIS is stated as

bankruptcy However because of the small number of bankrupt firms in some countries we also

consider receivership (active) firms as failed even if they are active These firms generally suffer

from serious financial distress However firms in liquidation are generally not included in the

sample of failed firms Firms in liquidation may depending on the country contain firms that

have ceased activities due to reasons other than failure (mergers discontinuing the operations

of a daughter company or of a foreign branch etc) Therefore for most countries we select only

firms that are coded as being bankrupt or under receivership However there are a number of

special cases where failed firms are coded under a different status heading These countries or

samples are the following

Country Status categories Bulgaria In liquidation Bankruptcy

27

Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the

study (for example Switzerland) If a country had only a very small number of failed firms it was

dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries

including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status

classes (including the bankruptcy category) are not completely homogenous within European

countries due to different legislations although there are obvious similarities in insolvency acts

(Philippe et al 2002) China is a special case because it includes samples with three different

criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are

two different samples (liquidation and receivership)

53 Statistical methods

In this study seven research hypotheses are drawn for statistical testing The statistical analysis

will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The

classification performance of the original model is assessed by the AUC (Area Under Curve)

measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close

connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random

28

model and 05 for a model with an average classification performance In SAS software is used

for all statistical analysess SAS software is used

The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is

tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical

method (multiple discriminant analysis or MDA) The problem is that the estimation sample

includes different numbers of failed and non-failed firms from 31 countries In the original Z-

Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from

the USUS Following the characteristics of these data we weight the firms so that the weights

for the failed and non-failed firms are equally In so doingthis way the non-proportional

sampling in from different countries does will not affect the re-estimated model The number of

firms from different countries however varies significantly leading to greater weights for larger

countries To avoid this problem the observations are also weighted so that each country has an

equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using

these weighted data and the resulting AUC is compared with that based on the original model

The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score

model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is

based on less- restrictive statistical assumptions than MDA In this estimation the dependent

variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA

does not require that independent variables be multivariate normal or that groups have equal

covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA

29

creates a score (logit) score L for every firm It is assumed that the independent variables are

linearly related to L This score or logit is used to determine the conditional probability of failure

as follows

= (eq 4)

where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the

original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing

the statistical significance of the difference between AUCs for this LR model and for the re-

estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it

is used as a benchmark for further statistical AUC comparisons because LR is applied as the

principal method in the testing of the remaining research hypotheses

The third hypothesis (H3) is associated with the performance effect of taking account of the

bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on

the following logit

(eq 5)

where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model

bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and

D1 = 1 when year = 2008 0 otherwise

D2 = 1 when year = 2009 0 otherwise

D3 = 1 when year = 2010 0 otherwise

)|1( XYp )( 441101

1

1

1XbXbbL ee

3

1

4

10

jjji

ii DcXbbL

30

The dummy variables do not directly refer to the bankruptcy year which that is not given in the

data basedatabase but rather to the last available year before bankruptcy For failed firms there

is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is

the base category If the AUC of this extended LR model statistically significantly exceeds the AUC

of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3

Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third

hypothesis above However for each hypothesis appropriate variables are used instead of the

year dummies Hypothesis H4 addresses the performance effect of taking size into account the

size and it will beis tested using by adding two additional variables measuring firm size in to the

LR -model In this LR model size is measured by the natural logarithm of total assets and its

squared form In this way the effect of logarithmic size can be reflected by in a function following

the second-order parabola Hypothesis H5 predicts that the classification performance improves

when the age of the firm is explicitly taken into account When testing this hypothesis the

category 6ndash14 years category is used as the base category and two dummy variables are

incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis

H6 looks at whether is associated with the classification performance is affected bythe effect of

the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt

is tested here using dummy variables for seven industries (D1 restaurants and hotels D2

construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and

water production D7 information technology) with all other industries acting as the base

category

31

Hypothesis H7 predicts that the explicit consideration of the country of origin will improves

classification performance This hypothesis is tested by using country risk measures instead ofnot

using dummy variables for countries but using country risk measures instead The country risk of

each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the

annual closing of accounts The rating is numerically recoded such that the best rating AAA

equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating

D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial

ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-

modelLR models with the original four financial ratios and the additional variables specified in

the hypotheses are estimated for all data In addition an LR model including all additional

variables is estimated for all data to assess the simultaneous effect of all variables Finally six of

the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not

included in this country- level testing hypothesis H7 is not tested because the additional variable

(country risk) is constant within the country

6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models

Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score

model for all data The variation in the ratios is significant as shown by the standard deviation

and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-

failed firms are close to each other indicating a symmetry of distributions However this is not

32

the case for the failed firms For the faileddistressed firms the median exceeds the mean for

these three ratios indicating negatively skewed distributions For X4 (BVETD) the means

significantly exceed the median for both failed and non-failed firms indicating a positively

skewed distribution For each of the four variables both the mean and the median are higher for

non-failed firms than for failed firms which is consistent with the expectations The difference

between the means of the non-failed and failed firms is larger in the original USUS data than in

our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is

approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of

EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may

indicate lower classification accuracy than in the original sample

(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates

(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the

large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model

Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same

statistical method or MDA The coefficients here are negative because the models are estimated

using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight

than in the original USUS data while the weights of WCTA and BVETD have proportionally

decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the

logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR

model These coefficients are directly comparable with those of the MDA model which isas

expected for the this exceptionally large sample The differences in the coefficients of the original

33

four variables among the eight LR-modelLR models (Models 1-8) are small For each model the

coefficient of BVETD is very close to zero This indicatinges that the original four variables and

the additional variables are quite independent of each other

(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-

modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate

that after 2007 (the base category) this risk of failure significantly decreased year by year The

base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due

to as a result of the global financial crisis The crisis played a significant role in the failure of key

businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The

effects were especially felt in Europe The coefficients of Model 4 for the size variables show that

the contribution of size on to the logit (risk measure) is atreaches its maximum value when

logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model

5 confirms the riskiness of young firms because the risk of failure is very high for newly founded

firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The

coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky

industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is

statistically significant (because of the large sample) but negative and very close to zero Finally

the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7

62 All data performance of the Zrsquorsquo-Score models

34

Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1

refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of

all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average

accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)

Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its

performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The

lower part of the table shows the AUCs for the countries included only in the test data The

performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)

samples It is also low and for the liquidation firms in the United Kingdom

The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5

presents the differences of these medians between the non-failed and failed firms by country

This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of

medians This correlation which is high for each financial ratio showing that the effects of the

ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large

except for EBITTA which This implies with the exceptionally high AUC that the ST firms

systematically differ from the non-ST firms although the differences are not extremely large The

differences between the medians are very large in Poland for each ratio justifying the high AUC

They are also large and in Finland and in the Czech Republic where the difference in EBITTA is

average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in

all four ratios are below the average a result thatwhich is obviously associated with a low AUC

35

In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even

negative In Iceland and Ireland the differences only in EBITTA are exceptionally small

(Table 4 here)

(Table 5 here)

Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by

MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)

supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is

at approximately the average level The re-estimation of the coefficients has led to improved

classification accuracy in a number of countries especially in Bosnia China (ST) Norway and

Greece However it has impaired the classification accuracy in the UK and China (delisted)

Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-

modelLR model) showing the effect of the estimation method For the benchmark model the

AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between

among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR

model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country

This result was expected because the coefficients of the models are directly comparable The

similar results for the models may also indicate that the independent variables conform to

multinormality This last latter result confirms what most researchers in the field of default

classification models have concluded that the accuracy levels of MDA and logistical regression

36

models are extremely similar despite any statistical violations such as normality between MDA

and logistical regression models

Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)

than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects

are not positive for all countries The effects are positive for example for Russia Estonia

Germany Ireland and Latvia but these effects are not statistically significant There are

statistically significant negative effects for China (ST) and Serbia There are notable negative

effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are

due to the exceptional annual distributions of failed firms in these countries For the countries

with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the

countries with positive effects it is only a few percent For each group non-failed firms are quite

equally distributed over years When the coefficient of D3 is very low (-0666) it strongly

decreases the risk estimates of most failed firms in the former countries but of only a few failed

firms in the latter countries This which leads to the observed effects H3 is supported by

the level of the whole sample However in some country samples (at the level of individual

levelies) H3 is not supported

Model 4 (LR-modelLR model with size variables) performs better than the benchmark model

which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads

and to significant improvements in the AUC for eg China (delisted) and Austria For China

(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC

37

also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs

in a few countries but thise decrease is not significant Model 4 is based on the four original

variables and the size effect following a second-order parabola This kind of size effect is very

small for micro firms but increases when approaching its maximum value in middle-sized firms

The countries with a positive change in AUC typically have data where in which the percent of

failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized

failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In

contrast the countries with a negative change in AUC have data where in which the percentage

of failed micro firms is exceptionally high (70-80)

Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly

the same AUC as the benchmark model (AUC = 0748) However the difference is positive and

statistically significant giving at least marginal support to H5 (age effect) For nearly all countries

the effect of the age on AUC is small For Austria however thise effect is however positive and

significant For Austria the percentage of non-failed young firms (less than 6 years) among non-

failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed

firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong

positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only

a few non-failed firms which This obviously leads to an improvement in AUC

Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC

supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its

38

AUC is notably higher than the benchmark AUC only in only a few countries such asfor example

France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg

restaurants and hotels and the information technology industryies but it has a positive effect

for on the construction and manufacturing industries For the countries with a positive effect on

AUC the percent of non-failed firms in restaurants and hotels and information technology is

high while that of failed firms is low For the risky industries (construction and manufacturing)

these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many

failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)

firms Therefore the AUC will be increasesd This situation is reversed for the countries with a

negative effect on AUC (Austria and Slovenia) The samples of these countries include a high

percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC

Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher

classification performance (AUC = 0749) than the benchmark model This result gives only very

weak support for H7 (country of origin effect) However for each country the resultant AUC is

nearly identical to that given by the benchmark model This result was expected due to the

negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7

This result implies that country risk hasdoes not have noany effect on the boundary between

bankrupt and non-bankrupt firms

Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional

variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to

39

the benchmark AUC in all test data However the effect on the AUCs largely varies and is either

negative or positive in different countries The positive effect is large in several countries such

as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative

effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show

that the inclusion of additional variables in the original model will usually increase the AUC but

not in every country

The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with

the USUS firms being unlike the majority of other firms in this study being listed or delisted

companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese

firm samples is associated with very small differences between the medians of the non-failed and

failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable

with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed

firms is very high Prior studies based on Chinese ST -firms have also demonstrated good

predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for

additional research and modeling work regarding unlisted and delisted Chinese firms

63 Country-level data performance of the Zrsquorsquo-Score models

The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform

all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the

different models estimated for each country separately (country-level models) In this table the

ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated

40

from the country data this benchmark will beis clearly outperformed by the resulting MDA

(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and

Sweden) However these results give only weak support for H1 at the country level because the

effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and

2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs

given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed

by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation

method)

Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC

than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3

leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United

StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in

nearly every country supporting H4 This improvement is significant however only in the United

StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark

model but for which it is only 0710 for the benchmark model It This improvement is also notable

in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age

category dummies) gives higher AUCs than the benchmark model for several countries but none

of the improvements are significant The positive effect is particularly strong especially in

Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only

weakly supports H5 because these negative effects are relatively small

41

Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects

on AUCs when compared with the benchmark However the only significant effect is the negative

effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the

industry dummies However there are notable positive effects on the AUC in for example

France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak

support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in

AUC compared with the benchmark model in Colombia Iceland and the Unites States It also

leads and to notable improvements in AUC at least in Belgium Denmark Estonia France

Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are

found only in Austria and Slovenia Thus as a conclusion we can concludestate that the

classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can

remarkably increases in most countries when different effects are taken into account by

additional variables This increase is found in most European countries and in Colombia and the

United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-

modelLR model and it can only be slightly improved only slightly by additional variables

(Table 6 here)

7 Summary of the study and a suggested extension

The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model

originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is

assessed using a very large international data set We test how the original version of the Zrsquorsquo-

42

Score model performs in different countries and how re-estimations using another statistical

method and different additional variables affect the classification performance when the data

are very heterogeneous For this kind of testing seven research hypotheses on classification

performance are extractedformulated These research hypotheses are tested for all data and

separately for country data (country-level analysis) The estimation data are from 31 countries

and the results are validated for 34 countries The countries are mainly from Europe but three

non-European countries are included (China Colombia and the United StatesUS) The statuses

used in the classification are mainly bankruptcyactive but receivership firms are also considered

to have failed In the Chinese data ST (special treatment) and delisted firms are also separately

analyzed as failed firms

The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very

satisfactorily in an international context The effects of the four financial ratios on performance

are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small

contribution in re-estimation The original model performs very well in several countries such as

Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only

marginally improved the classification performance thus weakly supporting the obsolescence

hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust

across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion

holds for the re-estimation of the model using LRA because the performance results are very

similar with to those of MDA (H2) The use of additional variables in the model generally

improvesd the classification accuracy of the original model but the results for countries are

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

3

1 Introduction

The first multivariate bankruptcy prediction model was developed by EI Altman (1968) in the

late 1960s After this pioneering work the multivariate approach to failure prediction spread

worldwide among researchers in finance banking and credit risk Failure prediction models are

important tools for bankers investors asset managers rating agencies and even the distressed

firms themselves The banking industry as the main provider of financing in the economy is

especially interested in minimizing the level of non-performing loans in order to maximize profit

on the credit activity and banks seek to reduce their own risk of default Another issue of interest

for bankers is capital adequacy and an the internal ratings-based approach encouraged by Basel

2 The Z-Score model has become a prototype for many of these models Asset managers and

investors need to have reliable tools for that can help them the select ion appropriate of

companies to be added forto their portfolios Financial distress of companies is on the one hand

detrimental to investor returns but on the other hand risk may provide opportunities for high

returns on short-sale strategies Rating agencies assess the risk of the entities and of securities

issues and thus they need to have a tool to predict default In addition Altman (1983 1993 and

2006) has suggested that the management of distressed firms can utilize the Z-Score model as a

guide to a financial turnaround

The approach used for bankruptcy prediction has been evolving evolved over time Beaver (1966

1968) used univariate analysis for selected ratios and found that some of them had very good

predictive power Altman (1968) moved significantly forwardmade strides by developing a

multiple discriminant analysis model (MDA) called the Z-Score model The next two decades

Commented [AFS1] Per the journal guidelines each citation must have a corresponding reference in the reference list Please note that there is no corresponding reference for this citation Please supply the missing information where indicated in the reference list at the end of the text

4

brought saw additional contributions to financial distress research For example such as Ohlson

(1980) with proposed a logit model1 Taffler (1984) with offered a Z-Score model for the UK and

Zmijewski (1984)2 who used a probit approach Dimitras et al (1996) reviewed 47 studies on

business prediction models They summarizinged the methods employed and the variety of

ratios used Discriminant analysis was the prevailing method and the most important financial

ratios came from the solvency category with profitability ratios being also being important

The next summary of different approaches to credit risk analysis was given by Altman and

Saunders (1998) Balcaen and Ooghe (2006) reviewed models of business failure prediction and

classified 43 models presented in the literature into four categories (univariate models 1 risk

index models 2 MDA models 21 and conditional probability models 19) However they

omitted the rapidly fast-growing type of number of models based on the option pricing theory

and contingent claims (eg Vassalou and Xing 2004 commercialized into the KMV model) and

they omittedas well as hazard models (eg Shumway 2001) Kumar and Ravi (2007) reviewed

128 statistical and artificial intelligence models for bank and firm bankruptcy predictions of banks

and firms with paying special attention paid to the techniques used in the different models

These authorsy noted that neural networks were the most popular intelligence technique In

their review Jackson and Wood (2013) presented the frequency of occurrence of specific

forecasting techniques in the prior literature The five most popular techniques were as follows

1 Altmanrsquos Z-score and Ohlsonrsquos O-score have beenwere compared by Dichev (1998) 2 A re-estimation of Ohlsonrsquos and Zmijewskirsquos models was presented by Grice and Dugan (2003)

Commented [S Ed2] Abbreviations and acronyms are often defined the first time they are used within the main text and then used throughout the remainder of the manuscript Please consider adhering to this convention

5

(1) multiple discriminant analysis (2) logit models (3) neural networks (4) contingent claims and

(5) univariate analysis

Recent reviews of the efficacy of these models have been delivered offered by Agarwal and

Taffler (2008) Das et al (2009) and Bauer and Agarwal (2014) These reviews takeing into

account the performance of accounting-based models market-based models and hazard

models These three model types of models prevail in the literature According to Agarwal and

Taffler (2008) there is little difference in the predictive accuracy of accounting-based and

market-based models however the usage of accounting-based models allows for a higher level

of risk-adjusted return on the credit activity Das et al (2009) showed that accounting-based

models perform comparably to the Merton structural market-based approach for CDS spread

estimation However the comprehensive model which used both sources of variables

outperformed both models In Bauer and Agarwal (2014) hazard models using accounting and

market information (Shumway 2001 and Campbell et al 2008) were compared with two other

approaches the original Tafflerrsquos (1984) Z-score model which that was tested in by Agarwal and

Taffler (2008) and a contingent claims model using Bharath and Shumwayrsquos (2008) approach

Using UK data tThe hazard models were superior in UK data in bankruptcy prediction accuracy

ROC (Receiver Operating Characteristic) ROC analysis and information content

Despite the vast amount of research on failure prediction the Z-Score model introduced by

Altman (1968) has been applied worldwide Thus although the Z-Score model has been in

existednce for more than 45 years it is still used as a main or supporting tool for bankruptcy or

6

financial distress prediction or and analysis both in research and in practice Therefore question

arises of why focus on the accounting-based model which is at least occasionally outperformed

by other models Most of the firms operating on the market are privately held and therefore

there are no market data (eg stock prices) are available Theose firms are usually financed by

banks which are obligated to assess their creditworthiness and monitor their performance In

the case of internationally active banks it is especially important to use a universal model across

the banking group for distress prediction for provisioning and for economic capital calculation

In following subsequent regulatory requirements they need to validate models and prove their

efficacy Thus we found it useful to analyze the performance of an accounting-based model in

an international context

In our study we use a large international sample of firms to assess the classification performance

of the Zrsquorsquo-Score model in bankruptcy prediction3 We analyze its the modelrsquos performance in for

firms from 31 European and three non-European countries (China Colombia and the USUS) T

these firms are mostly privately held and aA large number of firms are from non-manufacturing

industries Therefore we use the version of the model developed by Altman (1983) for private

and public manufacturing and non-manufacturing firms (Zrsquorsquo-Score model) Such an extensive

international analysis of the performance of the Z-Score modelrsquos performance has not been

presented thus farto date We regard our review and analysis as important contributions to the

economic literature

3 We use bankruptcy failure default and financial distress as equivalents

7

The paper is structured as follows In the introduction we present a brief summary of the

development of failure prediction research starting beginning with Beaver (1966) and Altman

(1968) In the second section we summarize the original Z-Score model (1968) and its extension

for private firms that is the Zrsquo-Score and Zrsquorsquo-Score models (1983) IThen in the third section we

present the results and conclusions from the literature review on these models The fourth

section presents seven research hypotheses on the performance of the Zrsquorsquo-Score model for

empirical analysis In the fifth section the empirical data and statistical methods are discussed

while the sixth section presents empirical findings Finally the seventh section summarizes the

study

2 Classic Z-Score models

21 Z-Score model for public firms

Altmanrsquos (1968) initial sample was composed of 66 corporations with 33 firms in each of the two

groups The bankrupt group (Group 1) consisted of manufacturers that filed a bankruptcy

petitions under Chapter X of the National Bankruptcy Act during the period 1946ndash1965 period

The mean asset size of these firms was 64 million USD with a ranginge of between 07 and 259

million USD Altman recognized that this group was not homogenous with respect to size and

industry although all firms were relatively small and from manufacturing industries Therefore

he attempted to make a carefully selection select of non-bankrupt firms (Group 2) Group 2

consisted of a paired sample of manufacturing firms chosen on a stratified random basis These

firms were stratified by industry and size with the asset size range restricted to 1ndash25 million USD

Altman eliminated both the small firms (less than 1 million US dollars in total assets) because of

8

a lack of data and the very large firms because of a lack of data because offor small firms and the

rarity of bankruptcies among these firms in that period of large firms He did not match the asset

size of the two groups exactly and therefore the firms in Group 2 are were slightly larger than

those in Group 1 The data collected for the firms in both groups were from the same years For

Group 1 the data were derived from financial statements one reporting period prior to

bankruptcy

Using financial statements Altman compiled a list of 22 potentially important financial ratios

based on financial statements for evaluation He classified these variables into five standard ratio

categories liquidity profitability leverage solvency and activity ratios These ratios were

chosen based on their popularity in the literature and their potential relevancey to the study The

final discriminant function estimated by Altman (1968) is as follows

Z = 0012X1 + 0014X2 + 0033X3 + 0006X4 + 0 999X5 (eq 1)

where X1 = Working CcapitalTotal Aassets X2 = Retained EarningsTotal Aassets X3 = Earnings before Iinterest and TtaxesTotal Aassets X4 = Market Vvalue of EequityBook Vvalue of Ttotal Lliabilities X5 = SalesTotal Aassets Z = Overall Index 22 Zrsquo-Score and Zrsquorsquo-Score models for private firms

The original Z-Score model was based on the market value of the firm and was thus applicable

only to publicly traded companies Altman (1983) emphasized that the Z-Score model is a

intended for publicly traded firms model and that ad hoc adjustments are not scientifically valid

9

Therefore Altman (1983) advocated a complete re-estimation of the model substituting the

book value of equity for the market value in X4 Using the same data Altman extracted the

following revised Zrsquo-Score model

Zrsquo = 0717X1 + 0847X2 + 3107X3 + 0420X4 + 0 998X5 (eq 2)

where X4 = Book value of equityBook value of total liabilities with the other variables being

the same as those in the original (1968) Z-Score model

DAltman ue to the lack of a private firm database Altman did not test the Zrsquo-Score model on a

secondary sample due to the lack of a private firm data base However he analyzed the accuracy

of a four-variable Zrsquorsquo-Score model that excludeding the SalesTotal assets ratio X5 from the

revised model because of a potential industry effect The industry effect that is more likely to

take place when this kind of industry-sensitive variable (asset turnover) is included in the model

Altman then estimated the following four-variable Zrsquorsquo-Score model (Altman 1983)

Zrsquorsquo = 325 + 656X1 + 326X2 + 672X3 + 105X4 (eq 3)

The EBITTotal assets ratio X3 made the highest contributioncontributed most to the

discrimination power in this version of the model The classification results for the Zrsquorsquo-Score

model were identical to the revised five-variable Zrsquo-Score model In the currentis study our

empirical analysis focuses on the performance of the Zrsquorsquo-Score model version in predicting

bankruptcy prediction wheredue to it has its widest scope that is because as it is intended for

both privately held and publicly listed firms and for both manufacturing and non-manufacturing

firms

10

3 Literature review

We searched for papers published from the year 2000 onward 4 in prominent international

journals or and books We selected 31 articles in which the Z-Score was either used either as a

failure prediction proxy or was assessed mostly in terms of predictive ability Of the 31 studies

Altmanrsquos Z-Score model5 was used in 16 cases as the measure of distress or of financial strength

In 13 studies Altmanrsquos original model was modified and (or) verified including re-estimation and

in 2 cases it was used solely for the robustness check As Pindado et al (2008) noted the Z-Score

was also used for other purposes such as the evaluation of the costs and benefits of covenants

in bonds the choice of the type of debt type (bank vs non-bank private or public) and the

relationship between investment and internal funds We focused on this part of the literature

that verified and (or) modified Altmanrsquos original model The wide broad usage of the Z-Score

model foras a measuringe of financial distress and performingas well as for the robustness checks

indicates that it is acceptabilityed as a reasonable simple and consistent measure of the

distressed firms

In the case of the modification of the The most common modification to the Z-Score model

model the most common change was the use of other estimation techniques or country-specific

data The use of Altmanrsquos ratios in combination with techniques other than MDA has improved

the its prediction capability TAdditionally the application of new data of on both US and non-US

4 Research devoted to the application of the Z-Score model before 2000 was reviewed by Grice and Ingram (2001) 5 Most of the models focused on stock- exchange- listed firms thus the Zrsquo-Score and Zrsquorsquo-Score models were not used

11

firms also improved model performance Kwak et al (2005) applied used Multiple Criteria Linear

Programming (MCLP) to model 5 Altman and 9 Ohlson variables They used with data on US

bankrupt US firms from 1992 to 1998 and nearly 6 times more matched US control firms The

MCLP approach performed better than Altmanrsquos original model and gave results similar to or

better than those of Ohlsonrsquos original model The original models were not recalculated and the

authors referred solely to their original prediction rates only Merkevicius et al (2006) using data

on US and Lithuanian firms developed a hybrid artificial -discriminantte model combining MDA

and an unsupervised learning artificial neural network (called the SOM-Altman model) with the

use of data on US and Lithuanian firms Thise hybrid SOM-Altman model reached a high

prediction rate of 9235 Xu and Zhang (2009) applied Altmanrsquos Z-score Ohlsonrsquos O-score and

Mertonrsquos distance-to-default (D-score) models to Japanese firms to check whether these models

are useful for bankruptcy prediction in Japan They also ldquomergedrdquo theose models into a new C-

score model They then introduced variables unique to Japan to check whether corporate

structure variables have any impact on the probability of bankruptcy t They called this the model

X-score model These two models were useful for Japanese firms in predicting bankruptcy

prediction but the market-based model was the most successful In summary the developed C-

score and X-score (with country-specific variables) models improved bankruptcy prediction

Tinoco and Wilson (2013) used the original Z-Score as one of the benchmarks to assess the

performance of their model developed for UK listed companies with combined accounting

market and macroeconomic data Altmanrsquos Z-Score presented very good classification accuracy

in the case of financially distressed firms (81 vs 87 for the new model) however it was less

correct for financial health prediction Another modification was introduced by Lyandres and

12

Zhdanov (2013) who posed the question of whether the inclusion of variables related to

investment opportunities improved the predictive power of three models (Altmanrsquos Z-score

model and Zmijewskirsquos and Shumwayrsquos models) They used three proxies for investment

opportunities (market-to-book value-to-book and RampD-to-assets) The measures of investment

opportunities were linked to the likelihood of default The inclusion of either of these measures

improved the out-of-sample forecasting ability of all three

The verification of Altmanrsquos model concentrated on its efficacy or on how it compares a

comparison with other accounting-based market-based or hazard models However although

the original Z-Score model was not solely an accounting-based approach because the market

value of equity was utilized we classify it here as an accounting -based approachbecause the

market value of equity was utilized Grice and Ingram (2001) used a new novel data set of US

firms and posed three questions about the efficacy of Altmanrsquos model They concludinged that

the prediction accuracy of Altmanrsquos model had declined over time and that the coefficients of

the model had significantly changed which means that the relation between the financial ratios

and the signs of financial distress had changed over time The model was sensitive to industry

classification (more efficient for manufacturing firms than for non-manufacturing firms) but was

not sensitive to the type of financial distress Similar conclusions were drawn by Grice and Dugan

(2003) regarding Ohlsonrsquos (1980) and Zmijewskirsquos (1984) models

Hillegeist et al (2004) compared Altmanrsquos Z-score and Ohlsonrsquos O-score (with original and

updated coefficients) with the model based on BSM option pricing (BSM-Prob) They used

13

relative information content tests to compare the out-of-sample performance BSM-Prob

outperformed accounting-based models The conclusions were robust to various modifications

of accounting-based models such as updated coefficients industry effects and the separation

of variables Chava and Jarrow (2004) employed an extended bankruptcy database of US listed

firms to prove the superiority of Shumwayrsquos model (2001) over Altmanrsquos (1968) and Zmijewskirsquos

(1984) models The authors re-estimated the models over the 1962ndash1990 period and forecasted

bankruptcies over the 1991ndash1999 period In the case of Shumwayrsquos model 744 (in the first

decile) of the bankruptcies were correctly identified for with Altmanrsquos model it was 632 and

for with Zmijewskirsquos model it was 432 Shumwayrsquos market-based model also outperformed

accounting-based models in terms of the ROC curve (091)

Reisz and Perlich (2007) used the barrier option for bankruptcy prediction and compared its

discriminatory power with other market-based models and Altmanrsquos Z-Score and Zrdquo-Score The

dataset covered nearly 6000 industrial firms over the period 1988ndash2002 period The authors

proved the domination superiority of Altmanrsquos Z-Score and Zrdquo-Score models for short-term (up

to 1 year) bankruptcy prediction For medium- and long-term bankruptcy prediction their barrier

option model outperformed the other models Pindado et al (2008) developed an ex -ante model

for the estimation of financial distress likelihood (FDL) using awith the use of panel data

methodology and they presented a financially (not legally) based definition of distress Their

sample covered 1583 US companies and 2250 companies from other G-7 countries for the

period 1990ndash2002 period They used a re-estimated Z-Score as a benchmark The FDL model

outperformed the Z-Score model in terms of stability and classification power for different

14

countries and time periods In the case of the re-estimated Z-Score model only profitability and

retained earnings kept maintained their significance for different years and countries

Wu et al (2010) evaluated the performance of five models (Altman 1968 Ohlson 1980

Zmijewski 1984 Shumway 2001 and Hillegeist et al 2004) using an up-to-date data set for

US listed firms Based on these models the authors built their own integrated model that is a

multi-period logit model with an expanded set of variables The integrated model which

combined accounting and market data as well as firmsrsquo characteristics outperformed the other

models Altmanrsquos Z-score performed poorly in compared withison to the other 4 models

Shumwayrsquos model performed best Hillegeist et alrsquos model performed adequately and Ohlsonrsquos

and Zmijewskirsquos models performed adequately although their performance deteriorated over

time

Jackson and Wood (2013) tested 13 different models of bankruptcy prediction and assessed their

efficacy using the ROC curve They selected three single- variable models three accounting-based

models (including Altmanrsquos Z-Score) in two versions (with up-dated coefficients and a neural

network approach) and four contingent claims models The contingent claims models and the

latter group outperformed the other models The best four best models were contingent claims

models based on European call and barrier options Although tThe predictive performance

improved withIn the case of the application of the neural network to accounting-based models

the predictive ability improved but it was still lower than within the case of the market-based

models

15

Acosta-Gonzaacutelez and Fernaacutendez-Rodriacuteguez (2014) used genetic algorithms with the Schwarz

information criterion (GASIC) for variable selection combined with the logit model for bankruptcy

prediction Altmanrsquos Z-Score model was used as one of two benchmarks for the authorsrsquo model

evaluation For one- step- ahead forecasting Altmanrsquos model was better in at predicting failed

firms but the type II error was high For two- and three- steps- ahead forecasts the performance

of the models was similar forin terms of failed firms but forin the case of non-failed firms and

the prediction accuracy of Altmanrsquos model was worse For four- steps- ahead forecasts the GASIC

model outperformed the other models forin the case of failed firms and but it performedwas

comparablye forin the case of non-failed firms

In general Altmanrsquos Z-Score model underperformed in compared withison to market-based

models but there was also evidence indicated that it performed well for short-term distress

prediction The question of whether market-based models are perform better than accounting-

based models has been raised many times (eg discussion in Das et al 2009 Bauer and d

Agarwal 2014) Our purpose is not to contribute to this strand of research but rather to focus on

the accounting-based approach In this study we analyze primarily analyze privately held firms

so there are by definition no market data for these firms In this case aAn accounting-based

approach is the only solution that could be appliedapplicable in this case by banks as lenders or

by investors holding debt securities of firms not listed on the stock exchange Thus far most of

the studies have concentrated on the US market only a few of them have used data from other

16

countries such as Japan the UK Lithuania or the G-7 countries Our analysis is a significant

extension of the previous research

4 Methodology and research hypotheses

The literature survey shows that the Z-Score model (publicly traded firms) the Zrsquo-Score model

(private manufacturing firms) and the Zrsquorsquo-Score model (private and publicly

traded manufacturing and non-manufacturing firms) have been adapted for different purposes

In this study we are first interested first in assessing the performance of the original Zrsquorsquo-Score

model in classifying bankrupt and non-bankrupt firms in an international context with special

focus on the European market However we also validate the results in a set of non-European

countries to generalize the results for circumstances outside Europe Second we will re-estimate

the model using extensive international data and then use the re-estimated Zrsquorsquo-Score model as a

benchmark for assessing the effects of different factors on the modelrsquos performance in terms of

classification accuracy We will assess the effects of the following five factors on this

performance year of bankruptcy size of firms age of firms industry and country of origin WIn

all we will test a set of hypotheses based on the effects of the model on performance on two

different levels First we will test a set of hypotheses on a pooled set of all firms and second on

data from each country individually CIn compared withison to the previous research our this

studyrsquos contribution is the focus on an international context not just simply the model

application or re-estimation for of a given countryrsquos data Because weour aim is focusing on the

performance of the Zrsquorsquo-Score mModel when using ain large body of international data the

research hypotheses are of a technical nature and are presented given as follows

17

H1 Obsolescence of the coefficients The Zrsquorsquo-Score mModel was originally estimated using the

same sample of firms that was used to develop the original Z-Score mModel The bankruptcies

in the estimation data occurred during the period 1946ndash-1965 period Thus the oldest

observations are from nearly seventy years ago during the post-war period Altman (1983)

recommended utilizing data as near to the present as possible when developing a bankruptcy

prediction model It is obvious that firmsrsquo the financial behavior of firms and their business

environment have significantly changed since then Thereforen potentially making the

importance of the financial ratios as reflected by the coefficients of the model may differ from

their original importance reflected by the coefficients of the model Therefore we suggest as

the first hypothesis (H1) that the re-estimation of the coefficients of the four original variables

of the Zrsquorsquo-Score model will improve the classification performance of the model in an

international context This hypothesis is supported by the previous research (eg Grice and

Ingram 2001) and by practice H1 aims to support this evidence on the international level and is

expressed as follows

H1 RThe re-estimatingon of the coefficients of the Zrsquorsquo-Score model improves the its

classification accuracy of the model

H2 Method of estimation The original Zrsquorsquo-Score model has been estimated using MDA However

MDA is based on the ordinary least squares (OLS) method and thus requires assumptions of

multinormality homoscedasticity and linearity which are not often met in empirical financial

ratio analysis Therefore we re-estimate the Zrsquorsquo-Score model using logistic regression analysis

(LRA) to assess the effect of the method of estimation method LRA does not require most of the

18

restricting assumptions of MDA In LRA the multivariate normality of the independent variables

is not required nor are homoscedasticity and linearity For the sake of OLS MDA can be more

useful than LRA for small samples such as the original sample of 66 firms used in the estimation

of the Zrsquorsquo-Score mModel However in a large sample LRA may potentially perform better In this

study we use large samples which is advantageous for LRA Therefore our second hypothesis

(H2) suggests is that the classification performance of the re-estimated Zrsquorsquo-Score model will be

improved when it is estimated using LRA instead of MDA The model re-estimated for the original

variables using LRA and all pooled data is called here the Zrsquorsquo-Score LR-modelLR model The

performance of this re-estimated model is used in this study as the benchmark for further

analyses Thus the second hypothesis is as follows

H2 The prediction accuracy of the logistic regression version of the Zrsquorsquo-Score model is higher

than that of the multiple discriminant analysis version

H3 Bankruptcy year The model based on the relationship between bankruptcy and financial

ratios is likely to be affected by the macroeconomic environment These effects may significantly

decrease the classification accuracy of the model If the model is estimated using data from one

year and it will be applied to data from another year the validity of the model can be questioned

Business cycles inIn terms of economic growth credit policy and interest rates business cycles

can ahave an effect on the boundary between bankrupt and non-bankrupt firms The original Zrsquorsquo-

Score model is estimated using data from the period 1946ndash1965 period which includes several

business cycles Therefore the model is not focused on any specific stage of a cycle and does not

explicitly take into account of the bankruptcy year Altman (1983) suggested gathering data from

19

firms for the last most recent few years when developing a prediction model In this study the

benchmark Zrsquorsquo-Score LR -model will beis estimated for a shorter period than in the original

estimation although it covers several recent years on for different stages of the business cycle in

different countries Therefore the third hypothesis (H3) assumes is that the classification

accuracy of the benchmark model can be increased by explicitly taking into account of the year

of bankruptcy in the estimation of the model estimation The third hypothesis is presented as

follows

H3 The prediction accuracy of the modelrsquos prediction accuracy is higher whenith the

effect of the year of bankruptcy is included is higher than that of the model without the

effect

H4 Size of the firm The boundary between bankrupt and non-bankrupt firms is different for small

and larger firms which decreases the performance of the model estimation when estimated for

data from one size category and isare applied to for data from another size category For the

bankrupt and non-bankrupt firms in the original data for Zrsquorsquo-Score model estimation the range

of asset sizes was ranged between approximately 1 and 25 million US dollars The data did not

include very small or very large firms Altman (1983) regarded the suitability of the original Z-

Score model (and likewisein the same way the Zrsquorsquo-Score model) for all firms as debatable

because of this omission it did not scrutinize very large or very small firms In the currentis study

the benchmark Zrsquorsquo-Score LR -model will beis estimated for data from many size categories from

very small firms to very large firms The fourth hypothesis (H4) assumes that the classification

performance of the uniform benchmark LR -model based on the original four financial variables

20

of the Zrsquorsquo-Score model is improved when the size category of the firm is explicitly taken into

account Thus we present the fourth hypothesis as follows

H4 The modelrsquos prediction accuracy of the modelis higher whenith the effect of size is

includede is higher than that of the model without this effect

H5 Age of the firm International insolvency statistics generally show that bankruptcy risk is a

function of the age of the firm Very young firms typically show a very high risk The original Zrsquorsquo-

Score model does not explicitly take age into account age However Altman (1983) noted that

the age of a firm is implicitly considered in the Retained EarningsTotal Assets ratio (X2) which

was regarded as a new ratio in the bankruptcy prediction context A relatively young firm will

probably show a low ratio because it has not had time to build up cumulative profits Thus a

young firm is to some degree discriminated against in the model and its likelihood of being

classified as bankrupt is relatively higher than that of an older firm The incidence of failure is

much higher in the early years of a firm AlthoughDespite that the age of the firm in this way is

implicitly taken into account in X2 we expect that an explicit consideration of the age will improve

the classification accuracy bydue to controlling for the age factor Therefore the fifth hypothesis

(H5) proposes that the performance of the uniform benchmark model based on the original four

financial variables of the Zrsquorsquo-Score model is increasesd when the age of the firm is explicitly taken

into account The fifth hypothesis is can be presented as follows

H5 The modelrsquos prediction accuracy of the model is higher when with the effect of firm

age is higher than that of the model without this effectincluded

21

H6 Industry of the firm The original Zrsquo-Score model was estimated only for manufacturing firms

Altman (1983) stated that it would be ideal to develop a bankruptcy prediction model utilizing a

homogenous group of bankrupt firms If we are interested in a particular industry grouping we

should gather data from bankrupt and non-bankrupt firms in these groupings Previous studies

show that financial distress analysis is influenced by the industry effect (Smith and Liou 2007)

Firms in different industries tend to report different levels of the same financial ratios which may

have an effect on the boundary between bankrupt and non-bankrupt firms This industry effect

may be present in the Zrsquo-Score model especially due to the SalesTotal Assets ratio (X5) This ratio

which showed the least significance on a univariate basis while making but it had made a very

significant contribution to the discriminant power of the multivariate model Altman (1983)

recognized the potential industry effect due to a the wide variation among industries in asset

turnover and specified the Zrsquorsquo-Score model without X5 However also the Zrsquorsquo-Score model was

also estimated using the original sample of manufacturing firms In our analysis the uniform

benchmark model based on the original four financial variables of the Zrsquorsquo-Score model is

estimated for a statistical sample representing different industries Therefore the sixth

hypothesis (H6) assumes that an explicit consideration of industry will improve the classification

accuracy of this benchmark model H6It can be expressed in the following form

H6 The modelrsquos prediction accuracy of the modelis higher whenith the effect of industry is

higher than that of the model without this effectincluded

H7 Country of origin The original Zrsquorsquo-Score model has been estimated only for USUS firms It

can be expected that the international applicability of the model to other countries is affected by

22

country-specific differences The eEconomic environment legislation culture financial markets

and accounting practices in a country may affect the financial behavior of firms and the boundary

between bankrupt and non-bankrupt firms These factors may potentially weaken the

classification performance of the model in for other countries outside other than that e one fore

country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the

seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm

into account will improve the classification accuracy of the benchmark model In our empirical

study the country effect will beis assessed by including a variable of for country risk Therefore

the seventh hypothesis is presented as follows

H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country

risk is higher than that of the model without this effectincluded

5 Empirical data and statistical methods

51 Sample of firms

The principal data of for this study are were extracted from the ORBIS databases of Bureau Van

Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the

moment of sampling contained administrative information on more than 50 million European

firms However income statement and balance sheet information was available for

approximately 8 million companies More than 99 of the companies covered in this database

are private companies from different various industries justifying the use of the Zrsquorsquo-Score model

instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all

industrial groupings and for both private and public entities (Altman 1983 124 Altman and

Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope

23

of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all

industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly

used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that

international comparability may be a problem when administrative firm-level data are

internationally pooled For administrative data Although the definition of variables is usually less

harmonized for administrative data tThis is less of a problem in the ORBIS database because of

the common international format of balance sheets For example although some discrepancies

in profitloss statements may arise because of differences in fiscal systems across countries

balance sheet variables largely adhere to international standards

AThere are a number of factors that influence the international applicability of bankruptcy

prediction models These factors include accounting legislation and practice creditor rights and

investor protection judicial efficiency corporate governance bankruptcy protection and

insolvency management and firm risk-taking These factors strongly differ between European

and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-

Score model outside Europe First it is especiallyparticularly important to include the USUS as

because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It

is include d in our study as well because it has the largest market capitalization in the world

Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it

applicable to emerging market companies Therefore we include firms from China and Colombia

which represent two very culturally and institutionally very dissimilar emerging market

countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt

24

firms ie the limit we set for European countries) from ORBIS World were not

available Thus the results are also estimated and tested for three non-European countries (the

United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-

Score modelrsquos performance The samples of firms from these countries are were extracted from

ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms

from around the world

SFor statistical sampling several requirements are set for the statistical sampling of the empirical

data First we require that the firm to be selected be an industrial (non-financial) company

Second its owners must have limited liability (so partnerships and sole proprietors are left out

of the study) Third we set a minimum requirement for the size of the firm Because the financial

ratios of very small firms are generally too unstable for a failure prediction model these firms

are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have

exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we

include in our estimation sample firms from all European countries and 3 pre-selected non-

European countries where the number of failed firms is more greater than 60 If the number of

failed firms for any European country is less than 60 the firms from this country are only included

only in the test sample For qualifying European countries the firms are randomly classified in

the estimation and test samples so that the number of firms is approximately equal in both

samples Thus our estimation data include firms from 28 European and 3 non-European

countries Fifth all failed firms that fulfill the above requirements are included in our samples

However if the number of non-failed firms in a country is very high a sample is randomly

25

selected from that country Finally the time span of fiscal years potentially available for this study

ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in

the database are from a financial period within 2007 and 2010 earlier years are also excluded

for non-failed firms for comparability All qualifying observations of non-failed firms from the

years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses

of failed firms to the last most recent financial statements available before failure The four

independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize

outliers

Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test

data by country The estimation sample includes data from 2602563 non-failed and 38215

failed firms from 28 European and 3 non-European countries The test sample is slightly larger

because it includes data from 31 European and 3 non-European countries From For the country

of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt

firms The available US data that were available consist only of listed (and delisted) firms From

China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are

included in the estimation sample although there are only 32 such firms altogether7 The Chinese

datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status

are separately analyzed separately only in for the test data8 ST firms are listed firms suffering

6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor

26

from serious financial difficulties Excluding the special US data and the two non-private Chinese

datasets 994 of the observations in the data are private firms

(Table 1 here)

52 Status of failed firms

ORBIS has five classes for potentially active firms (active default of payment receivership

dormant and branch) and seven classes for inactive firms that no longer carry out business

activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation

branch and no precision) From Among these classes only active is selected to represent non-

distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by

considering (with exceptions described below) a firm failed if its status in ORBIS is stated as

bankruptcy However because of the small number of bankrupt firms in some countries we also

consider receivership (active) firms as failed even if they are active These firms generally suffer

from serious financial distress However firms in liquidation are generally not included in the

sample of failed firms Firms in liquidation may depending on the country contain firms that

have ceased activities due to reasons other than failure (mergers discontinuing the operations

of a daughter company or of a foreign branch etc) Therefore for most countries we select only

firms that are coded as being bankrupt or under receivership However there are a number of

special cases where failed firms are coded under a different status heading These countries or

samples are the following

Country Status categories Bulgaria In liquidation Bankruptcy

27

Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the

study (for example Switzerland) If a country had only a very small number of failed firms it was

dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries

including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status

classes (including the bankruptcy category) are not completely homogenous within European

countries due to different legislations although there are obvious similarities in insolvency acts

(Philippe et al 2002) China is a special case because it includes samples with three different

criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are

two different samples (liquidation and receivership)

53 Statistical methods

In this study seven research hypotheses are drawn for statistical testing The statistical analysis

will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The

classification performance of the original model is assessed by the AUC (Area Under Curve)

measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close

connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random

28

model and 05 for a model with an average classification performance In SAS software is used

for all statistical analysess SAS software is used

The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is

tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical

method (multiple discriminant analysis or MDA) The problem is that the estimation sample

includes different numbers of failed and non-failed firms from 31 countries In the original Z-

Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from

the USUS Following the characteristics of these data we weight the firms so that the weights

for the failed and non-failed firms are equally In so doingthis way the non-proportional

sampling in from different countries does will not affect the re-estimated model The number of

firms from different countries however varies significantly leading to greater weights for larger

countries To avoid this problem the observations are also weighted so that each country has an

equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using

these weighted data and the resulting AUC is compared with that based on the original model

The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score

model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is

based on less- restrictive statistical assumptions than MDA In this estimation the dependent

variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA

does not require that independent variables be multivariate normal or that groups have equal

covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA

29

creates a score (logit) score L for every firm It is assumed that the independent variables are

linearly related to L This score or logit is used to determine the conditional probability of failure

as follows

= (eq 4)

where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the

original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing

the statistical significance of the difference between AUCs for this LR model and for the re-

estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it

is used as a benchmark for further statistical AUC comparisons because LR is applied as the

principal method in the testing of the remaining research hypotheses

The third hypothesis (H3) is associated with the performance effect of taking account of the

bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on

the following logit

(eq 5)

where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model

bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and

D1 = 1 when year = 2008 0 otherwise

D2 = 1 when year = 2009 0 otherwise

D3 = 1 when year = 2010 0 otherwise

)|1( XYp )( 441101

1

1

1XbXbbL ee

3

1

4

10

jjji

ii DcXbbL

30

The dummy variables do not directly refer to the bankruptcy year which that is not given in the

data basedatabase but rather to the last available year before bankruptcy For failed firms there

is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is

the base category If the AUC of this extended LR model statistically significantly exceeds the AUC

of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3

Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third

hypothesis above However for each hypothesis appropriate variables are used instead of the

year dummies Hypothesis H4 addresses the performance effect of taking size into account the

size and it will beis tested using by adding two additional variables measuring firm size in to the

LR -model In this LR model size is measured by the natural logarithm of total assets and its

squared form In this way the effect of logarithmic size can be reflected by in a function following

the second-order parabola Hypothesis H5 predicts that the classification performance improves

when the age of the firm is explicitly taken into account When testing this hypothesis the

category 6ndash14 years category is used as the base category and two dummy variables are

incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis

H6 looks at whether is associated with the classification performance is affected bythe effect of

the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt

is tested here using dummy variables for seven industries (D1 restaurants and hotels D2

construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and

water production D7 information technology) with all other industries acting as the base

category

31

Hypothesis H7 predicts that the explicit consideration of the country of origin will improves

classification performance This hypothesis is tested by using country risk measures instead ofnot

using dummy variables for countries but using country risk measures instead The country risk of

each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the

annual closing of accounts The rating is numerically recoded such that the best rating AAA

equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating

D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial

ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-

modelLR models with the original four financial ratios and the additional variables specified in

the hypotheses are estimated for all data In addition an LR model including all additional

variables is estimated for all data to assess the simultaneous effect of all variables Finally six of

the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not

included in this country- level testing hypothesis H7 is not tested because the additional variable

(country risk) is constant within the country

6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models

Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score

model for all data The variation in the ratios is significant as shown by the standard deviation

and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-

failed firms are close to each other indicating a symmetry of distributions However this is not

32

the case for the failed firms For the faileddistressed firms the median exceeds the mean for

these three ratios indicating negatively skewed distributions For X4 (BVETD) the means

significantly exceed the median for both failed and non-failed firms indicating a positively

skewed distribution For each of the four variables both the mean and the median are higher for

non-failed firms than for failed firms which is consistent with the expectations The difference

between the means of the non-failed and failed firms is larger in the original USUS data than in

our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is

approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of

EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may

indicate lower classification accuracy than in the original sample

(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates

(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the

large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model

Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same

statistical method or MDA The coefficients here are negative because the models are estimated

using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight

than in the original USUS data while the weights of WCTA and BVETD have proportionally

decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the

logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR

model These coefficients are directly comparable with those of the MDA model which isas

expected for the this exceptionally large sample The differences in the coefficients of the original

33

four variables among the eight LR-modelLR models (Models 1-8) are small For each model the

coefficient of BVETD is very close to zero This indicatinges that the original four variables and

the additional variables are quite independent of each other

(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-

modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate

that after 2007 (the base category) this risk of failure significantly decreased year by year The

base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due

to as a result of the global financial crisis The crisis played a significant role in the failure of key

businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The

effects were especially felt in Europe The coefficients of Model 4 for the size variables show that

the contribution of size on to the logit (risk measure) is atreaches its maximum value when

logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model

5 confirms the riskiness of young firms because the risk of failure is very high for newly founded

firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The

coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky

industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is

statistically significant (because of the large sample) but negative and very close to zero Finally

the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7

62 All data performance of the Zrsquorsquo-Score models

34

Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1

refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of

all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average

accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)

Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its

performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The

lower part of the table shows the AUCs for the countries included only in the test data The

performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)

samples It is also low and for the liquidation firms in the United Kingdom

The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5

presents the differences of these medians between the non-failed and failed firms by country

This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of

medians This correlation which is high for each financial ratio showing that the effects of the

ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large

except for EBITTA which This implies with the exceptionally high AUC that the ST firms

systematically differ from the non-ST firms although the differences are not extremely large The

differences between the medians are very large in Poland for each ratio justifying the high AUC

They are also large and in Finland and in the Czech Republic where the difference in EBITTA is

average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in

all four ratios are below the average a result thatwhich is obviously associated with a low AUC

35

In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even

negative In Iceland and Ireland the differences only in EBITTA are exceptionally small

(Table 4 here)

(Table 5 here)

Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by

MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)

supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is

at approximately the average level The re-estimation of the coefficients has led to improved

classification accuracy in a number of countries especially in Bosnia China (ST) Norway and

Greece However it has impaired the classification accuracy in the UK and China (delisted)

Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-

modelLR model) showing the effect of the estimation method For the benchmark model the

AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between

among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR

model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country

This result was expected because the coefficients of the models are directly comparable The

similar results for the models may also indicate that the independent variables conform to

multinormality This last latter result confirms what most researchers in the field of default

classification models have concluded that the accuracy levels of MDA and logistical regression

36

models are extremely similar despite any statistical violations such as normality between MDA

and logistical regression models

Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)

than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects

are not positive for all countries The effects are positive for example for Russia Estonia

Germany Ireland and Latvia but these effects are not statistically significant There are

statistically significant negative effects for China (ST) and Serbia There are notable negative

effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are

due to the exceptional annual distributions of failed firms in these countries For the countries

with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the

countries with positive effects it is only a few percent For each group non-failed firms are quite

equally distributed over years When the coefficient of D3 is very low (-0666) it strongly

decreases the risk estimates of most failed firms in the former countries but of only a few failed

firms in the latter countries This which leads to the observed effects H3 is supported by

the level of the whole sample However in some country samples (at the level of individual

levelies) H3 is not supported

Model 4 (LR-modelLR model with size variables) performs better than the benchmark model

which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads

and to significant improvements in the AUC for eg China (delisted) and Austria For China

(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC

37

also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs

in a few countries but thise decrease is not significant Model 4 is based on the four original

variables and the size effect following a second-order parabola This kind of size effect is very

small for micro firms but increases when approaching its maximum value in middle-sized firms

The countries with a positive change in AUC typically have data where in which the percent of

failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized

failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In

contrast the countries with a negative change in AUC have data where in which the percentage

of failed micro firms is exceptionally high (70-80)

Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly

the same AUC as the benchmark model (AUC = 0748) However the difference is positive and

statistically significant giving at least marginal support to H5 (age effect) For nearly all countries

the effect of the age on AUC is small For Austria however thise effect is however positive and

significant For Austria the percentage of non-failed young firms (less than 6 years) among non-

failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed

firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong

positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only

a few non-failed firms which This obviously leads to an improvement in AUC

Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC

supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its

38

AUC is notably higher than the benchmark AUC only in only a few countries such asfor example

France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg

restaurants and hotels and the information technology industryies but it has a positive effect

for on the construction and manufacturing industries For the countries with a positive effect on

AUC the percent of non-failed firms in restaurants and hotels and information technology is

high while that of failed firms is low For the risky industries (construction and manufacturing)

these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many

failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)

firms Therefore the AUC will be increasesd This situation is reversed for the countries with a

negative effect on AUC (Austria and Slovenia) The samples of these countries include a high

percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC

Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher

classification performance (AUC = 0749) than the benchmark model This result gives only very

weak support for H7 (country of origin effect) However for each country the resultant AUC is

nearly identical to that given by the benchmark model This result was expected due to the

negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7

This result implies that country risk hasdoes not have noany effect on the boundary between

bankrupt and non-bankrupt firms

Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional

variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to

39

the benchmark AUC in all test data However the effect on the AUCs largely varies and is either

negative or positive in different countries The positive effect is large in several countries such

as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative

effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show

that the inclusion of additional variables in the original model will usually increase the AUC but

not in every country

The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with

the USUS firms being unlike the majority of other firms in this study being listed or delisted

companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese

firm samples is associated with very small differences between the medians of the non-failed and

failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable

with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed

firms is very high Prior studies based on Chinese ST -firms have also demonstrated good

predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for

additional research and modeling work regarding unlisted and delisted Chinese firms

63 Country-level data performance of the Zrsquorsquo-Score models

The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform

all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the

different models estimated for each country separately (country-level models) In this table the

ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated

40

from the country data this benchmark will beis clearly outperformed by the resulting MDA

(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and

Sweden) However these results give only weak support for H1 at the country level because the

effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and

2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs

given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed

by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation

method)

Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC

than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3

leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United

StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in

nearly every country supporting H4 This improvement is significant however only in the United

StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark

model but for which it is only 0710 for the benchmark model It This improvement is also notable

in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age

category dummies) gives higher AUCs than the benchmark model for several countries but none

of the improvements are significant The positive effect is particularly strong especially in

Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only

weakly supports H5 because these negative effects are relatively small

41

Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects

on AUCs when compared with the benchmark However the only significant effect is the negative

effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the

industry dummies However there are notable positive effects on the AUC in for example

France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak

support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in

AUC compared with the benchmark model in Colombia Iceland and the Unites States It also

leads and to notable improvements in AUC at least in Belgium Denmark Estonia France

Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are

found only in Austria and Slovenia Thus as a conclusion we can concludestate that the

classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can

remarkably increases in most countries when different effects are taken into account by

additional variables This increase is found in most European countries and in Colombia and the

United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-

modelLR model and it can only be slightly improved only slightly by additional variables

(Table 6 here)

7 Summary of the study and a suggested extension

The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model

originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is

assessed using a very large international data set We test how the original version of the Zrsquorsquo-

42

Score model performs in different countries and how re-estimations using another statistical

method and different additional variables affect the classification performance when the data

are very heterogeneous For this kind of testing seven research hypotheses on classification

performance are extractedformulated These research hypotheses are tested for all data and

separately for country data (country-level analysis) The estimation data are from 31 countries

and the results are validated for 34 countries The countries are mainly from Europe but three

non-European countries are included (China Colombia and the United StatesUS) The statuses

used in the classification are mainly bankruptcyactive but receivership firms are also considered

to have failed In the Chinese data ST (special treatment) and delisted firms are also separately

analyzed as failed firms

The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very

satisfactorily in an international context The effects of the four financial ratios on performance

are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small

contribution in re-estimation The original model performs very well in several countries such as

Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only

marginally improved the classification performance thus weakly supporting the obsolescence

hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust

across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion

holds for the re-estimation of the model using LRA because the performance results are very

similar with to those of MDA (H2) The use of additional variables in the model generally

improvesd the classification accuracy of the original model but the results for countries are

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

4

brought saw additional contributions to financial distress research For example such as Ohlson

(1980) with proposed a logit model1 Taffler (1984) with offered a Z-Score model for the UK and

Zmijewski (1984)2 who used a probit approach Dimitras et al (1996) reviewed 47 studies on

business prediction models They summarizinged the methods employed and the variety of

ratios used Discriminant analysis was the prevailing method and the most important financial

ratios came from the solvency category with profitability ratios being also being important

The next summary of different approaches to credit risk analysis was given by Altman and

Saunders (1998) Balcaen and Ooghe (2006) reviewed models of business failure prediction and

classified 43 models presented in the literature into four categories (univariate models 1 risk

index models 2 MDA models 21 and conditional probability models 19) However they

omitted the rapidly fast-growing type of number of models based on the option pricing theory

and contingent claims (eg Vassalou and Xing 2004 commercialized into the KMV model) and

they omittedas well as hazard models (eg Shumway 2001) Kumar and Ravi (2007) reviewed

128 statistical and artificial intelligence models for bank and firm bankruptcy predictions of banks

and firms with paying special attention paid to the techniques used in the different models

These authorsy noted that neural networks were the most popular intelligence technique In

their review Jackson and Wood (2013) presented the frequency of occurrence of specific

forecasting techniques in the prior literature The five most popular techniques were as follows

1 Altmanrsquos Z-score and Ohlsonrsquos O-score have beenwere compared by Dichev (1998) 2 A re-estimation of Ohlsonrsquos and Zmijewskirsquos models was presented by Grice and Dugan (2003)

Commented [S Ed2] Abbreviations and acronyms are often defined the first time they are used within the main text and then used throughout the remainder of the manuscript Please consider adhering to this convention

5

(1) multiple discriminant analysis (2) logit models (3) neural networks (4) contingent claims and

(5) univariate analysis

Recent reviews of the efficacy of these models have been delivered offered by Agarwal and

Taffler (2008) Das et al (2009) and Bauer and Agarwal (2014) These reviews takeing into

account the performance of accounting-based models market-based models and hazard

models These three model types of models prevail in the literature According to Agarwal and

Taffler (2008) there is little difference in the predictive accuracy of accounting-based and

market-based models however the usage of accounting-based models allows for a higher level

of risk-adjusted return on the credit activity Das et al (2009) showed that accounting-based

models perform comparably to the Merton structural market-based approach for CDS spread

estimation However the comprehensive model which used both sources of variables

outperformed both models In Bauer and Agarwal (2014) hazard models using accounting and

market information (Shumway 2001 and Campbell et al 2008) were compared with two other

approaches the original Tafflerrsquos (1984) Z-score model which that was tested in by Agarwal and

Taffler (2008) and a contingent claims model using Bharath and Shumwayrsquos (2008) approach

Using UK data tThe hazard models were superior in UK data in bankruptcy prediction accuracy

ROC (Receiver Operating Characteristic) ROC analysis and information content

Despite the vast amount of research on failure prediction the Z-Score model introduced by

Altman (1968) has been applied worldwide Thus although the Z-Score model has been in

existednce for more than 45 years it is still used as a main or supporting tool for bankruptcy or

6

financial distress prediction or and analysis both in research and in practice Therefore question

arises of why focus on the accounting-based model which is at least occasionally outperformed

by other models Most of the firms operating on the market are privately held and therefore

there are no market data (eg stock prices) are available Theose firms are usually financed by

banks which are obligated to assess their creditworthiness and monitor their performance In

the case of internationally active banks it is especially important to use a universal model across

the banking group for distress prediction for provisioning and for economic capital calculation

In following subsequent regulatory requirements they need to validate models and prove their

efficacy Thus we found it useful to analyze the performance of an accounting-based model in

an international context

In our study we use a large international sample of firms to assess the classification performance

of the Zrsquorsquo-Score model in bankruptcy prediction3 We analyze its the modelrsquos performance in for

firms from 31 European and three non-European countries (China Colombia and the USUS) T

these firms are mostly privately held and aA large number of firms are from non-manufacturing

industries Therefore we use the version of the model developed by Altman (1983) for private

and public manufacturing and non-manufacturing firms (Zrsquorsquo-Score model) Such an extensive

international analysis of the performance of the Z-Score modelrsquos performance has not been

presented thus farto date We regard our review and analysis as important contributions to the

economic literature

3 We use bankruptcy failure default and financial distress as equivalents

7

The paper is structured as follows In the introduction we present a brief summary of the

development of failure prediction research starting beginning with Beaver (1966) and Altman

(1968) In the second section we summarize the original Z-Score model (1968) and its extension

for private firms that is the Zrsquo-Score and Zrsquorsquo-Score models (1983) IThen in the third section we

present the results and conclusions from the literature review on these models The fourth

section presents seven research hypotheses on the performance of the Zrsquorsquo-Score model for

empirical analysis In the fifth section the empirical data and statistical methods are discussed

while the sixth section presents empirical findings Finally the seventh section summarizes the

study

2 Classic Z-Score models

21 Z-Score model for public firms

Altmanrsquos (1968) initial sample was composed of 66 corporations with 33 firms in each of the two

groups The bankrupt group (Group 1) consisted of manufacturers that filed a bankruptcy

petitions under Chapter X of the National Bankruptcy Act during the period 1946ndash1965 period

The mean asset size of these firms was 64 million USD with a ranginge of between 07 and 259

million USD Altman recognized that this group was not homogenous with respect to size and

industry although all firms were relatively small and from manufacturing industries Therefore

he attempted to make a carefully selection select of non-bankrupt firms (Group 2) Group 2

consisted of a paired sample of manufacturing firms chosen on a stratified random basis These

firms were stratified by industry and size with the asset size range restricted to 1ndash25 million USD

Altman eliminated both the small firms (less than 1 million US dollars in total assets) because of

8

a lack of data and the very large firms because of a lack of data because offor small firms and the

rarity of bankruptcies among these firms in that period of large firms He did not match the asset

size of the two groups exactly and therefore the firms in Group 2 are were slightly larger than

those in Group 1 The data collected for the firms in both groups were from the same years For

Group 1 the data were derived from financial statements one reporting period prior to

bankruptcy

Using financial statements Altman compiled a list of 22 potentially important financial ratios

based on financial statements for evaluation He classified these variables into five standard ratio

categories liquidity profitability leverage solvency and activity ratios These ratios were

chosen based on their popularity in the literature and their potential relevancey to the study The

final discriminant function estimated by Altman (1968) is as follows

Z = 0012X1 + 0014X2 + 0033X3 + 0006X4 + 0 999X5 (eq 1)

where X1 = Working CcapitalTotal Aassets X2 = Retained EarningsTotal Aassets X3 = Earnings before Iinterest and TtaxesTotal Aassets X4 = Market Vvalue of EequityBook Vvalue of Ttotal Lliabilities X5 = SalesTotal Aassets Z = Overall Index 22 Zrsquo-Score and Zrsquorsquo-Score models for private firms

The original Z-Score model was based on the market value of the firm and was thus applicable

only to publicly traded companies Altman (1983) emphasized that the Z-Score model is a

intended for publicly traded firms model and that ad hoc adjustments are not scientifically valid

9

Therefore Altman (1983) advocated a complete re-estimation of the model substituting the

book value of equity for the market value in X4 Using the same data Altman extracted the

following revised Zrsquo-Score model

Zrsquo = 0717X1 + 0847X2 + 3107X3 + 0420X4 + 0 998X5 (eq 2)

where X4 = Book value of equityBook value of total liabilities with the other variables being

the same as those in the original (1968) Z-Score model

DAltman ue to the lack of a private firm database Altman did not test the Zrsquo-Score model on a

secondary sample due to the lack of a private firm data base However he analyzed the accuracy

of a four-variable Zrsquorsquo-Score model that excludeding the SalesTotal assets ratio X5 from the

revised model because of a potential industry effect The industry effect that is more likely to

take place when this kind of industry-sensitive variable (asset turnover) is included in the model

Altman then estimated the following four-variable Zrsquorsquo-Score model (Altman 1983)

Zrsquorsquo = 325 + 656X1 + 326X2 + 672X3 + 105X4 (eq 3)

The EBITTotal assets ratio X3 made the highest contributioncontributed most to the

discrimination power in this version of the model The classification results for the Zrsquorsquo-Score

model were identical to the revised five-variable Zrsquo-Score model In the currentis study our

empirical analysis focuses on the performance of the Zrsquorsquo-Score model version in predicting

bankruptcy prediction wheredue to it has its widest scope that is because as it is intended for

both privately held and publicly listed firms and for both manufacturing and non-manufacturing

firms

10

3 Literature review

We searched for papers published from the year 2000 onward 4 in prominent international

journals or and books We selected 31 articles in which the Z-Score was either used either as a

failure prediction proxy or was assessed mostly in terms of predictive ability Of the 31 studies

Altmanrsquos Z-Score model5 was used in 16 cases as the measure of distress or of financial strength

In 13 studies Altmanrsquos original model was modified and (or) verified including re-estimation and

in 2 cases it was used solely for the robustness check As Pindado et al (2008) noted the Z-Score

was also used for other purposes such as the evaluation of the costs and benefits of covenants

in bonds the choice of the type of debt type (bank vs non-bank private or public) and the

relationship between investment and internal funds We focused on this part of the literature

that verified and (or) modified Altmanrsquos original model The wide broad usage of the Z-Score

model foras a measuringe of financial distress and performingas well as for the robustness checks

indicates that it is acceptabilityed as a reasonable simple and consistent measure of the

distressed firms

In the case of the modification of the The most common modification to the Z-Score model

model the most common change was the use of other estimation techniques or country-specific

data The use of Altmanrsquos ratios in combination with techniques other than MDA has improved

the its prediction capability TAdditionally the application of new data of on both US and non-US

4 Research devoted to the application of the Z-Score model before 2000 was reviewed by Grice and Ingram (2001) 5 Most of the models focused on stock- exchange- listed firms thus the Zrsquo-Score and Zrsquorsquo-Score models were not used

11

firms also improved model performance Kwak et al (2005) applied used Multiple Criteria Linear

Programming (MCLP) to model 5 Altman and 9 Ohlson variables They used with data on US

bankrupt US firms from 1992 to 1998 and nearly 6 times more matched US control firms The

MCLP approach performed better than Altmanrsquos original model and gave results similar to or

better than those of Ohlsonrsquos original model The original models were not recalculated and the

authors referred solely to their original prediction rates only Merkevicius et al (2006) using data

on US and Lithuanian firms developed a hybrid artificial -discriminantte model combining MDA

and an unsupervised learning artificial neural network (called the SOM-Altman model) with the

use of data on US and Lithuanian firms Thise hybrid SOM-Altman model reached a high

prediction rate of 9235 Xu and Zhang (2009) applied Altmanrsquos Z-score Ohlsonrsquos O-score and

Mertonrsquos distance-to-default (D-score) models to Japanese firms to check whether these models

are useful for bankruptcy prediction in Japan They also ldquomergedrdquo theose models into a new C-

score model They then introduced variables unique to Japan to check whether corporate

structure variables have any impact on the probability of bankruptcy t They called this the model

X-score model These two models were useful for Japanese firms in predicting bankruptcy

prediction but the market-based model was the most successful In summary the developed C-

score and X-score (with country-specific variables) models improved bankruptcy prediction

Tinoco and Wilson (2013) used the original Z-Score as one of the benchmarks to assess the

performance of their model developed for UK listed companies with combined accounting

market and macroeconomic data Altmanrsquos Z-Score presented very good classification accuracy

in the case of financially distressed firms (81 vs 87 for the new model) however it was less

correct for financial health prediction Another modification was introduced by Lyandres and

12

Zhdanov (2013) who posed the question of whether the inclusion of variables related to

investment opportunities improved the predictive power of three models (Altmanrsquos Z-score

model and Zmijewskirsquos and Shumwayrsquos models) They used three proxies for investment

opportunities (market-to-book value-to-book and RampD-to-assets) The measures of investment

opportunities were linked to the likelihood of default The inclusion of either of these measures

improved the out-of-sample forecasting ability of all three

The verification of Altmanrsquos model concentrated on its efficacy or on how it compares a

comparison with other accounting-based market-based or hazard models However although

the original Z-Score model was not solely an accounting-based approach because the market

value of equity was utilized we classify it here as an accounting -based approachbecause the

market value of equity was utilized Grice and Ingram (2001) used a new novel data set of US

firms and posed three questions about the efficacy of Altmanrsquos model They concludinged that

the prediction accuracy of Altmanrsquos model had declined over time and that the coefficients of

the model had significantly changed which means that the relation between the financial ratios

and the signs of financial distress had changed over time The model was sensitive to industry

classification (more efficient for manufacturing firms than for non-manufacturing firms) but was

not sensitive to the type of financial distress Similar conclusions were drawn by Grice and Dugan

(2003) regarding Ohlsonrsquos (1980) and Zmijewskirsquos (1984) models

Hillegeist et al (2004) compared Altmanrsquos Z-score and Ohlsonrsquos O-score (with original and

updated coefficients) with the model based on BSM option pricing (BSM-Prob) They used

13

relative information content tests to compare the out-of-sample performance BSM-Prob

outperformed accounting-based models The conclusions were robust to various modifications

of accounting-based models such as updated coefficients industry effects and the separation

of variables Chava and Jarrow (2004) employed an extended bankruptcy database of US listed

firms to prove the superiority of Shumwayrsquos model (2001) over Altmanrsquos (1968) and Zmijewskirsquos

(1984) models The authors re-estimated the models over the 1962ndash1990 period and forecasted

bankruptcies over the 1991ndash1999 period In the case of Shumwayrsquos model 744 (in the first

decile) of the bankruptcies were correctly identified for with Altmanrsquos model it was 632 and

for with Zmijewskirsquos model it was 432 Shumwayrsquos market-based model also outperformed

accounting-based models in terms of the ROC curve (091)

Reisz and Perlich (2007) used the barrier option for bankruptcy prediction and compared its

discriminatory power with other market-based models and Altmanrsquos Z-Score and Zrdquo-Score The

dataset covered nearly 6000 industrial firms over the period 1988ndash2002 period The authors

proved the domination superiority of Altmanrsquos Z-Score and Zrdquo-Score models for short-term (up

to 1 year) bankruptcy prediction For medium- and long-term bankruptcy prediction their barrier

option model outperformed the other models Pindado et al (2008) developed an ex -ante model

for the estimation of financial distress likelihood (FDL) using awith the use of panel data

methodology and they presented a financially (not legally) based definition of distress Their

sample covered 1583 US companies and 2250 companies from other G-7 countries for the

period 1990ndash2002 period They used a re-estimated Z-Score as a benchmark The FDL model

outperformed the Z-Score model in terms of stability and classification power for different

14

countries and time periods In the case of the re-estimated Z-Score model only profitability and

retained earnings kept maintained their significance for different years and countries

Wu et al (2010) evaluated the performance of five models (Altman 1968 Ohlson 1980

Zmijewski 1984 Shumway 2001 and Hillegeist et al 2004) using an up-to-date data set for

US listed firms Based on these models the authors built their own integrated model that is a

multi-period logit model with an expanded set of variables The integrated model which

combined accounting and market data as well as firmsrsquo characteristics outperformed the other

models Altmanrsquos Z-score performed poorly in compared withison to the other 4 models

Shumwayrsquos model performed best Hillegeist et alrsquos model performed adequately and Ohlsonrsquos

and Zmijewskirsquos models performed adequately although their performance deteriorated over

time

Jackson and Wood (2013) tested 13 different models of bankruptcy prediction and assessed their

efficacy using the ROC curve They selected three single- variable models three accounting-based

models (including Altmanrsquos Z-Score) in two versions (with up-dated coefficients and a neural

network approach) and four contingent claims models The contingent claims models and the

latter group outperformed the other models The best four best models were contingent claims

models based on European call and barrier options Although tThe predictive performance

improved withIn the case of the application of the neural network to accounting-based models

the predictive ability improved but it was still lower than within the case of the market-based

models

15

Acosta-Gonzaacutelez and Fernaacutendez-Rodriacuteguez (2014) used genetic algorithms with the Schwarz

information criterion (GASIC) for variable selection combined with the logit model for bankruptcy

prediction Altmanrsquos Z-Score model was used as one of two benchmarks for the authorsrsquo model

evaluation For one- step- ahead forecasting Altmanrsquos model was better in at predicting failed

firms but the type II error was high For two- and three- steps- ahead forecasts the performance

of the models was similar forin terms of failed firms but forin the case of non-failed firms and

the prediction accuracy of Altmanrsquos model was worse For four- steps- ahead forecasts the GASIC

model outperformed the other models forin the case of failed firms and but it performedwas

comparablye forin the case of non-failed firms

In general Altmanrsquos Z-Score model underperformed in compared withison to market-based

models but there was also evidence indicated that it performed well for short-term distress

prediction The question of whether market-based models are perform better than accounting-

based models has been raised many times (eg discussion in Das et al 2009 Bauer and d

Agarwal 2014) Our purpose is not to contribute to this strand of research but rather to focus on

the accounting-based approach In this study we analyze primarily analyze privately held firms

so there are by definition no market data for these firms In this case aAn accounting-based

approach is the only solution that could be appliedapplicable in this case by banks as lenders or

by investors holding debt securities of firms not listed on the stock exchange Thus far most of

the studies have concentrated on the US market only a few of them have used data from other

16

countries such as Japan the UK Lithuania or the G-7 countries Our analysis is a significant

extension of the previous research

4 Methodology and research hypotheses

The literature survey shows that the Z-Score model (publicly traded firms) the Zrsquo-Score model

(private manufacturing firms) and the Zrsquorsquo-Score model (private and publicly

traded manufacturing and non-manufacturing firms) have been adapted for different purposes

In this study we are first interested first in assessing the performance of the original Zrsquorsquo-Score

model in classifying bankrupt and non-bankrupt firms in an international context with special

focus on the European market However we also validate the results in a set of non-European

countries to generalize the results for circumstances outside Europe Second we will re-estimate

the model using extensive international data and then use the re-estimated Zrsquorsquo-Score model as a

benchmark for assessing the effects of different factors on the modelrsquos performance in terms of

classification accuracy We will assess the effects of the following five factors on this

performance year of bankruptcy size of firms age of firms industry and country of origin WIn

all we will test a set of hypotheses based on the effects of the model on performance on two

different levels First we will test a set of hypotheses on a pooled set of all firms and second on

data from each country individually CIn compared withison to the previous research our this

studyrsquos contribution is the focus on an international context not just simply the model

application or re-estimation for of a given countryrsquos data Because weour aim is focusing on the

performance of the Zrsquorsquo-Score mModel when using ain large body of international data the

research hypotheses are of a technical nature and are presented given as follows

17

H1 Obsolescence of the coefficients The Zrsquorsquo-Score mModel was originally estimated using the

same sample of firms that was used to develop the original Z-Score mModel The bankruptcies

in the estimation data occurred during the period 1946ndash-1965 period Thus the oldest

observations are from nearly seventy years ago during the post-war period Altman (1983)

recommended utilizing data as near to the present as possible when developing a bankruptcy

prediction model It is obvious that firmsrsquo the financial behavior of firms and their business

environment have significantly changed since then Thereforen potentially making the

importance of the financial ratios as reflected by the coefficients of the model may differ from

their original importance reflected by the coefficients of the model Therefore we suggest as

the first hypothesis (H1) that the re-estimation of the coefficients of the four original variables

of the Zrsquorsquo-Score model will improve the classification performance of the model in an

international context This hypothesis is supported by the previous research (eg Grice and

Ingram 2001) and by practice H1 aims to support this evidence on the international level and is

expressed as follows

H1 RThe re-estimatingon of the coefficients of the Zrsquorsquo-Score model improves the its

classification accuracy of the model

H2 Method of estimation The original Zrsquorsquo-Score model has been estimated using MDA However

MDA is based on the ordinary least squares (OLS) method and thus requires assumptions of

multinormality homoscedasticity and linearity which are not often met in empirical financial

ratio analysis Therefore we re-estimate the Zrsquorsquo-Score model using logistic regression analysis

(LRA) to assess the effect of the method of estimation method LRA does not require most of the

18

restricting assumptions of MDA In LRA the multivariate normality of the independent variables

is not required nor are homoscedasticity and linearity For the sake of OLS MDA can be more

useful than LRA for small samples such as the original sample of 66 firms used in the estimation

of the Zrsquorsquo-Score mModel However in a large sample LRA may potentially perform better In this

study we use large samples which is advantageous for LRA Therefore our second hypothesis

(H2) suggests is that the classification performance of the re-estimated Zrsquorsquo-Score model will be

improved when it is estimated using LRA instead of MDA The model re-estimated for the original

variables using LRA and all pooled data is called here the Zrsquorsquo-Score LR-modelLR model The

performance of this re-estimated model is used in this study as the benchmark for further

analyses Thus the second hypothesis is as follows

H2 The prediction accuracy of the logistic regression version of the Zrsquorsquo-Score model is higher

than that of the multiple discriminant analysis version

H3 Bankruptcy year The model based on the relationship between bankruptcy and financial

ratios is likely to be affected by the macroeconomic environment These effects may significantly

decrease the classification accuracy of the model If the model is estimated using data from one

year and it will be applied to data from another year the validity of the model can be questioned

Business cycles inIn terms of economic growth credit policy and interest rates business cycles

can ahave an effect on the boundary between bankrupt and non-bankrupt firms The original Zrsquorsquo-

Score model is estimated using data from the period 1946ndash1965 period which includes several

business cycles Therefore the model is not focused on any specific stage of a cycle and does not

explicitly take into account of the bankruptcy year Altman (1983) suggested gathering data from

19

firms for the last most recent few years when developing a prediction model In this study the

benchmark Zrsquorsquo-Score LR -model will beis estimated for a shorter period than in the original

estimation although it covers several recent years on for different stages of the business cycle in

different countries Therefore the third hypothesis (H3) assumes is that the classification

accuracy of the benchmark model can be increased by explicitly taking into account of the year

of bankruptcy in the estimation of the model estimation The third hypothesis is presented as

follows

H3 The prediction accuracy of the modelrsquos prediction accuracy is higher whenith the

effect of the year of bankruptcy is included is higher than that of the model without the

effect

H4 Size of the firm The boundary between bankrupt and non-bankrupt firms is different for small

and larger firms which decreases the performance of the model estimation when estimated for

data from one size category and isare applied to for data from another size category For the

bankrupt and non-bankrupt firms in the original data for Zrsquorsquo-Score model estimation the range

of asset sizes was ranged between approximately 1 and 25 million US dollars The data did not

include very small or very large firms Altman (1983) regarded the suitability of the original Z-

Score model (and likewisein the same way the Zrsquorsquo-Score model) for all firms as debatable

because of this omission it did not scrutinize very large or very small firms In the currentis study

the benchmark Zrsquorsquo-Score LR -model will beis estimated for data from many size categories from

very small firms to very large firms The fourth hypothesis (H4) assumes that the classification

performance of the uniform benchmark LR -model based on the original four financial variables

20

of the Zrsquorsquo-Score model is improved when the size category of the firm is explicitly taken into

account Thus we present the fourth hypothesis as follows

H4 The modelrsquos prediction accuracy of the modelis higher whenith the effect of size is

includede is higher than that of the model without this effect

H5 Age of the firm International insolvency statistics generally show that bankruptcy risk is a

function of the age of the firm Very young firms typically show a very high risk The original Zrsquorsquo-

Score model does not explicitly take age into account age However Altman (1983) noted that

the age of a firm is implicitly considered in the Retained EarningsTotal Assets ratio (X2) which

was regarded as a new ratio in the bankruptcy prediction context A relatively young firm will

probably show a low ratio because it has not had time to build up cumulative profits Thus a

young firm is to some degree discriminated against in the model and its likelihood of being

classified as bankrupt is relatively higher than that of an older firm The incidence of failure is

much higher in the early years of a firm AlthoughDespite that the age of the firm in this way is

implicitly taken into account in X2 we expect that an explicit consideration of the age will improve

the classification accuracy bydue to controlling for the age factor Therefore the fifth hypothesis

(H5) proposes that the performance of the uniform benchmark model based on the original four

financial variables of the Zrsquorsquo-Score model is increasesd when the age of the firm is explicitly taken

into account The fifth hypothesis is can be presented as follows

H5 The modelrsquos prediction accuracy of the model is higher when with the effect of firm

age is higher than that of the model without this effectincluded

21

H6 Industry of the firm The original Zrsquo-Score model was estimated only for manufacturing firms

Altman (1983) stated that it would be ideal to develop a bankruptcy prediction model utilizing a

homogenous group of bankrupt firms If we are interested in a particular industry grouping we

should gather data from bankrupt and non-bankrupt firms in these groupings Previous studies

show that financial distress analysis is influenced by the industry effect (Smith and Liou 2007)

Firms in different industries tend to report different levels of the same financial ratios which may

have an effect on the boundary between bankrupt and non-bankrupt firms This industry effect

may be present in the Zrsquo-Score model especially due to the SalesTotal Assets ratio (X5) This ratio

which showed the least significance on a univariate basis while making but it had made a very

significant contribution to the discriminant power of the multivariate model Altman (1983)

recognized the potential industry effect due to a the wide variation among industries in asset

turnover and specified the Zrsquorsquo-Score model without X5 However also the Zrsquorsquo-Score model was

also estimated using the original sample of manufacturing firms In our analysis the uniform

benchmark model based on the original four financial variables of the Zrsquorsquo-Score model is

estimated for a statistical sample representing different industries Therefore the sixth

hypothesis (H6) assumes that an explicit consideration of industry will improve the classification

accuracy of this benchmark model H6It can be expressed in the following form

H6 The modelrsquos prediction accuracy of the modelis higher whenith the effect of industry is

higher than that of the model without this effectincluded

H7 Country of origin The original Zrsquorsquo-Score model has been estimated only for USUS firms It

can be expected that the international applicability of the model to other countries is affected by

22

country-specific differences The eEconomic environment legislation culture financial markets

and accounting practices in a country may affect the financial behavior of firms and the boundary

between bankrupt and non-bankrupt firms These factors may potentially weaken the

classification performance of the model in for other countries outside other than that e one fore

country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the

seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm

into account will improve the classification accuracy of the benchmark model In our empirical

study the country effect will beis assessed by including a variable of for country risk Therefore

the seventh hypothesis is presented as follows

H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country

risk is higher than that of the model without this effectincluded

5 Empirical data and statistical methods

51 Sample of firms

The principal data of for this study are were extracted from the ORBIS databases of Bureau Van

Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the

moment of sampling contained administrative information on more than 50 million European

firms However income statement and balance sheet information was available for

approximately 8 million companies More than 99 of the companies covered in this database

are private companies from different various industries justifying the use of the Zrsquorsquo-Score model

instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all

industrial groupings and for both private and public entities (Altman 1983 124 Altman and

Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope

23

of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all

industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly

used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that

international comparability may be a problem when administrative firm-level data are

internationally pooled For administrative data Although the definition of variables is usually less

harmonized for administrative data tThis is less of a problem in the ORBIS database because of

the common international format of balance sheets For example although some discrepancies

in profitloss statements may arise because of differences in fiscal systems across countries

balance sheet variables largely adhere to international standards

AThere are a number of factors that influence the international applicability of bankruptcy

prediction models These factors include accounting legislation and practice creditor rights and

investor protection judicial efficiency corporate governance bankruptcy protection and

insolvency management and firm risk-taking These factors strongly differ between European

and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-

Score model outside Europe First it is especiallyparticularly important to include the USUS as

because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It

is include d in our study as well because it has the largest market capitalization in the world

Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it

applicable to emerging market companies Therefore we include firms from China and Colombia

which represent two very culturally and institutionally very dissimilar emerging market

countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt

24

firms ie the limit we set for European countries) from ORBIS World were not

available Thus the results are also estimated and tested for three non-European countries (the

United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-

Score modelrsquos performance The samples of firms from these countries are were extracted from

ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms

from around the world

SFor statistical sampling several requirements are set for the statistical sampling of the empirical

data First we require that the firm to be selected be an industrial (non-financial) company

Second its owners must have limited liability (so partnerships and sole proprietors are left out

of the study) Third we set a minimum requirement for the size of the firm Because the financial

ratios of very small firms are generally too unstable for a failure prediction model these firms

are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have

exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we

include in our estimation sample firms from all European countries and 3 pre-selected non-

European countries where the number of failed firms is more greater than 60 If the number of

failed firms for any European country is less than 60 the firms from this country are only included

only in the test sample For qualifying European countries the firms are randomly classified in

the estimation and test samples so that the number of firms is approximately equal in both

samples Thus our estimation data include firms from 28 European and 3 non-European

countries Fifth all failed firms that fulfill the above requirements are included in our samples

However if the number of non-failed firms in a country is very high a sample is randomly

25

selected from that country Finally the time span of fiscal years potentially available for this study

ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in

the database are from a financial period within 2007 and 2010 earlier years are also excluded

for non-failed firms for comparability All qualifying observations of non-failed firms from the

years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses

of failed firms to the last most recent financial statements available before failure The four

independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize

outliers

Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test

data by country The estimation sample includes data from 2602563 non-failed and 38215

failed firms from 28 European and 3 non-European countries The test sample is slightly larger

because it includes data from 31 European and 3 non-European countries From For the country

of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt

firms The available US data that were available consist only of listed (and delisted) firms From

China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are

included in the estimation sample although there are only 32 such firms altogether7 The Chinese

datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status

are separately analyzed separately only in for the test data8 ST firms are listed firms suffering

6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor

26

from serious financial difficulties Excluding the special US data and the two non-private Chinese

datasets 994 of the observations in the data are private firms

(Table 1 here)

52 Status of failed firms

ORBIS has five classes for potentially active firms (active default of payment receivership

dormant and branch) and seven classes for inactive firms that no longer carry out business

activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation

branch and no precision) From Among these classes only active is selected to represent non-

distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by

considering (with exceptions described below) a firm failed if its status in ORBIS is stated as

bankruptcy However because of the small number of bankrupt firms in some countries we also

consider receivership (active) firms as failed even if they are active These firms generally suffer

from serious financial distress However firms in liquidation are generally not included in the

sample of failed firms Firms in liquidation may depending on the country contain firms that

have ceased activities due to reasons other than failure (mergers discontinuing the operations

of a daughter company or of a foreign branch etc) Therefore for most countries we select only

firms that are coded as being bankrupt or under receivership However there are a number of

special cases where failed firms are coded under a different status heading These countries or

samples are the following

Country Status categories Bulgaria In liquidation Bankruptcy

27

Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the

study (for example Switzerland) If a country had only a very small number of failed firms it was

dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries

including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status

classes (including the bankruptcy category) are not completely homogenous within European

countries due to different legislations although there are obvious similarities in insolvency acts

(Philippe et al 2002) China is a special case because it includes samples with three different

criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are

two different samples (liquidation and receivership)

53 Statistical methods

In this study seven research hypotheses are drawn for statistical testing The statistical analysis

will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The

classification performance of the original model is assessed by the AUC (Area Under Curve)

measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close

connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random

28

model and 05 for a model with an average classification performance In SAS software is used

for all statistical analysess SAS software is used

The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is

tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical

method (multiple discriminant analysis or MDA) The problem is that the estimation sample

includes different numbers of failed and non-failed firms from 31 countries In the original Z-

Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from

the USUS Following the characteristics of these data we weight the firms so that the weights

for the failed and non-failed firms are equally In so doingthis way the non-proportional

sampling in from different countries does will not affect the re-estimated model The number of

firms from different countries however varies significantly leading to greater weights for larger

countries To avoid this problem the observations are also weighted so that each country has an

equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using

these weighted data and the resulting AUC is compared with that based on the original model

The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score

model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is

based on less- restrictive statistical assumptions than MDA In this estimation the dependent

variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA

does not require that independent variables be multivariate normal or that groups have equal

covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA

29

creates a score (logit) score L for every firm It is assumed that the independent variables are

linearly related to L This score or logit is used to determine the conditional probability of failure

as follows

= (eq 4)

where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the

original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing

the statistical significance of the difference between AUCs for this LR model and for the re-

estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it

is used as a benchmark for further statistical AUC comparisons because LR is applied as the

principal method in the testing of the remaining research hypotheses

The third hypothesis (H3) is associated with the performance effect of taking account of the

bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on

the following logit

(eq 5)

where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model

bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and

D1 = 1 when year = 2008 0 otherwise

D2 = 1 when year = 2009 0 otherwise

D3 = 1 when year = 2010 0 otherwise

)|1( XYp )( 441101

1

1

1XbXbbL ee

3

1

4

10

jjji

ii DcXbbL

30

The dummy variables do not directly refer to the bankruptcy year which that is not given in the

data basedatabase but rather to the last available year before bankruptcy For failed firms there

is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is

the base category If the AUC of this extended LR model statistically significantly exceeds the AUC

of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3

Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third

hypothesis above However for each hypothesis appropriate variables are used instead of the

year dummies Hypothesis H4 addresses the performance effect of taking size into account the

size and it will beis tested using by adding two additional variables measuring firm size in to the

LR -model In this LR model size is measured by the natural logarithm of total assets and its

squared form In this way the effect of logarithmic size can be reflected by in a function following

the second-order parabola Hypothesis H5 predicts that the classification performance improves

when the age of the firm is explicitly taken into account When testing this hypothesis the

category 6ndash14 years category is used as the base category and two dummy variables are

incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis

H6 looks at whether is associated with the classification performance is affected bythe effect of

the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt

is tested here using dummy variables for seven industries (D1 restaurants and hotels D2

construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and

water production D7 information technology) with all other industries acting as the base

category

31

Hypothesis H7 predicts that the explicit consideration of the country of origin will improves

classification performance This hypothesis is tested by using country risk measures instead ofnot

using dummy variables for countries but using country risk measures instead The country risk of

each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the

annual closing of accounts The rating is numerically recoded such that the best rating AAA

equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating

D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial

ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-

modelLR models with the original four financial ratios and the additional variables specified in

the hypotheses are estimated for all data In addition an LR model including all additional

variables is estimated for all data to assess the simultaneous effect of all variables Finally six of

the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not

included in this country- level testing hypothesis H7 is not tested because the additional variable

(country risk) is constant within the country

6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models

Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score

model for all data The variation in the ratios is significant as shown by the standard deviation

and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-

failed firms are close to each other indicating a symmetry of distributions However this is not

32

the case for the failed firms For the faileddistressed firms the median exceeds the mean for

these three ratios indicating negatively skewed distributions For X4 (BVETD) the means

significantly exceed the median for both failed and non-failed firms indicating a positively

skewed distribution For each of the four variables both the mean and the median are higher for

non-failed firms than for failed firms which is consistent with the expectations The difference

between the means of the non-failed and failed firms is larger in the original USUS data than in

our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is

approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of

EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may

indicate lower classification accuracy than in the original sample

(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates

(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the

large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model

Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same

statistical method or MDA The coefficients here are negative because the models are estimated

using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight

than in the original USUS data while the weights of WCTA and BVETD have proportionally

decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the

logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR

model These coefficients are directly comparable with those of the MDA model which isas

expected for the this exceptionally large sample The differences in the coefficients of the original

33

four variables among the eight LR-modelLR models (Models 1-8) are small For each model the

coefficient of BVETD is very close to zero This indicatinges that the original four variables and

the additional variables are quite independent of each other

(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-

modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate

that after 2007 (the base category) this risk of failure significantly decreased year by year The

base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due

to as a result of the global financial crisis The crisis played a significant role in the failure of key

businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The

effects were especially felt in Europe The coefficients of Model 4 for the size variables show that

the contribution of size on to the logit (risk measure) is atreaches its maximum value when

logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model

5 confirms the riskiness of young firms because the risk of failure is very high for newly founded

firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The

coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky

industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is

statistically significant (because of the large sample) but negative and very close to zero Finally

the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7

62 All data performance of the Zrsquorsquo-Score models

34

Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1

refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of

all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average

accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)

Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its

performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The

lower part of the table shows the AUCs for the countries included only in the test data The

performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)

samples It is also low and for the liquidation firms in the United Kingdom

The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5

presents the differences of these medians between the non-failed and failed firms by country

This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of

medians This correlation which is high for each financial ratio showing that the effects of the

ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large

except for EBITTA which This implies with the exceptionally high AUC that the ST firms

systematically differ from the non-ST firms although the differences are not extremely large The

differences between the medians are very large in Poland for each ratio justifying the high AUC

They are also large and in Finland and in the Czech Republic where the difference in EBITTA is

average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in

all four ratios are below the average a result thatwhich is obviously associated with a low AUC

35

In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even

negative In Iceland and Ireland the differences only in EBITTA are exceptionally small

(Table 4 here)

(Table 5 here)

Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by

MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)

supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is

at approximately the average level The re-estimation of the coefficients has led to improved

classification accuracy in a number of countries especially in Bosnia China (ST) Norway and

Greece However it has impaired the classification accuracy in the UK and China (delisted)

Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-

modelLR model) showing the effect of the estimation method For the benchmark model the

AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between

among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR

model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country

This result was expected because the coefficients of the models are directly comparable The

similar results for the models may also indicate that the independent variables conform to

multinormality This last latter result confirms what most researchers in the field of default

classification models have concluded that the accuracy levels of MDA and logistical regression

36

models are extremely similar despite any statistical violations such as normality between MDA

and logistical regression models

Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)

than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects

are not positive for all countries The effects are positive for example for Russia Estonia

Germany Ireland and Latvia but these effects are not statistically significant There are

statistically significant negative effects for China (ST) and Serbia There are notable negative

effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are

due to the exceptional annual distributions of failed firms in these countries For the countries

with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the

countries with positive effects it is only a few percent For each group non-failed firms are quite

equally distributed over years When the coefficient of D3 is very low (-0666) it strongly

decreases the risk estimates of most failed firms in the former countries but of only a few failed

firms in the latter countries This which leads to the observed effects H3 is supported by

the level of the whole sample However in some country samples (at the level of individual

levelies) H3 is not supported

Model 4 (LR-modelLR model with size variables) performs better than the benchmark model

which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads

and to significant improvements in the AUC for eg China (delisted) and Austria For China

(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC

37

also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs

in a few countries but thise decrease is not significant Model 4 is based on the four original

variables and the size effect following a second-order parabola This kind of size effect is very

small for micro firms but increases when approaching its maximum value in middle-sized firms

The countries with a positive change in AUC typically have data where in which the percent of

failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized

failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In

contrast the countries with a negative change in AUC have data where in which the percentage

of failed micro firms is exceptionally high (70-80)

Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly

the same AUC as the benchmark model (AUC = 0748) However the difference is positive and

statistically significant giving at least marginal support to H5 (age effect) For nearly all countries

the effect of the age on AUC is small For Austria however thise effect is however positive and

significant For Austria the percentage of non-failed young firms (less than 6 years) among non-

failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed

firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong

positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only

a few non-failed firms which This obviously leads to an improvement in AUC

Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC

supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its

38

AUC is notably higher than the benchmark AUC only in only a few countries such asfor example

France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg

restaurants and hotels and the information technology industryies but it has a positive effect

for on the construction and manufacturing industries For the countries with a positive effect on

AUC the percent of non-failed firms in restaurants and hotels and information technology is

high while that of failed firms is low For the risky industries (construction and manufacturing)

these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many

failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)

firms Therefore the AUC will be increasesd This situation is reversed for the countries with a

negative effect on AUC (Austria and Slovenia) The samples of these countries include a high

percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC

Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher

classification performance (AUC = 0749) than the benchmark model This result gives only very

weak support for H7 (country of origin effect) However for each country the resultant AUC is

nearly identical to that given by the benchmark model This result was expected due to the

negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7

This result implies that country risk hasdoes not have noany effect on the boundary between

bankrupt and non-bankrupt firms

Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional

variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to

39

the benchmark AUC in all test data However the effect on the AUCs largely varies and is either

negative or positive in different countries The positive effect is large in several countries such

as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative

effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show

that the inclusion of additional variables in the original model will usually increase the AUC but

not in every country

The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with

the USUS firms being unlike the majority of other firms in this study being listed or delisted

companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese

firm samples is associated with very small differences between the medians of the non-failed and

failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable

with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed

firms is very high Prior studies based on Chinese ST -firms have also demonstrated good

predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for

additional research and modeling work regarding unlisted and delisted Chinese firms

63 Country-level data performance of the Zrsquorsquo-Score models

The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform

all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the

different models estimated for each country separately (country-level models) In this table the

ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated

40

from the country data this benchmark will beis clearly outperformed by the resulting MDA

(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and

Sweden) However these results give only weak support for H1 at the country level because the

effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and

2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs

given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed

by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation

method)

Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC

than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3

leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United

StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in

nearly every country supporting H4 This improvement is significant however only in the United

StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark

model but for which it is only 0710 for the benchmark model It This improvement is also notable

in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age

category dummies) gives higher AUCs than the benchmark model for several countries but none

of the improvements are significant The positive effect is particularly strong especially in

Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only

weakly supports H5 because these negative effects are relatively small

41

Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects

on AUCs when compared with the benchmark However the only significant effect is the negative

effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the

industry dummies However there are notable positive effects on the AUC in for example

France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak

support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in

AUC compared with the benchmark model in Colombia Iceland and the Unites States It also

leads and to notable improvements in AUC at least in Belgium Denmark Estonia France

Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are

found only in Austria and Slovenia Thus as a conclusion we can concludestate that the

classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can

remarkably increases in most countries when different effects are taken into account by

additional variables This increase is found in most European countries and in Colombia and the

United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-

modelLR model and it can only be slightly improved only slightly by additional variables

(Table 6 here)

7 Summary of the study and a suggested extension

The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model

originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is

assessed using a very large international data set We test how the original version of the Zrsquorsquo-

42

Score model performs in different countries and how re-estimations using another statistical

method and different additional variables affect the classification performance when the data

are very heterogeneous For this kind of testing seven research hypotheses on classification

performance are extractedformulated These research hypotheses are tested for all data and

separately for country data (country-level analysis) The estimation data are from 31 countries

and the results are validated for 34 countries The countries are mainly from Europe but three

non-European countries are included (China Colombia and the United StatesUS) The statuses

used in the classification are mainly bankruptcyactive but receivership firms are also considered

to have failed In the Chinese data ST (special treatment) and delisted firms are also separately

analyzed as failed firms

The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very

satisfactorily in an international context The effects of the four financial ratios on performance

are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small

contribution in re-estimation The original model performs very well in several countries such as

Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only

marginally improved the classification performance thus weakly supporting the obsolescence

hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust

across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion

holds for the re-estimation of the model using LRA because the performance results are very

similar with to those of MDA (H2) The use of additional variables in the model generally

improvesd the classification accuracy of the original model but the results for countries are

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

5

(1) multiple discriminant analysis (2) logit models (3) neural networks (4) contingent claims and

(5) univariate analysis

Recent reviews of the efficacy of these models have been delivered offered by Agarwal and

Taffler (2008) Das et al (2009) and Bauer and Agarwal (2014) These reviews takeing into

account the performance of accounting-based models market-based models and hazard

models These three model types of models prevail in the literature According to Agarwal and

Taffler (2008) there is little difference in the predictive accuracy of accounting-based and

market-based models however the usage of accounting-based models allows for a higher level

of risk-adjusted return on the credit activity Das et al (2009) showed that accounting-based

models perform comparably to the Merton structural market-based approach for CDS spread

estimation However the comprehensive model which used both sources of variables

outperformed both models In Bauer and Agarwal (2014) hazard models using accounting and

market information (Shumway 2001 and Campbell et al 2008) were compared with two other

approaches the original Tafflerrsquos (1984) Z-score model which that was tested in by Agarwal and

Taffler (2008) and a contingent claims model using Bharath and Shumwayrsquos (2008) approach

Using UK data tThe hazard models were superior in UK data in bankruptcy prediction accuracy

ROC (Receiver Operating Characteristic) ROC analysis and information content

Despite the vast amount of research on failure prediction the Z-Score model introduced by

Altman (1968) has been applied worldwide Thus although the Z-Score model has been in

existednce for more than 45 years it is still used as a main or supporting tool for bankruptcy or

6

financial distress prediction or and analysis both in research and in practice Therefore question

arises of why focus on the accounting-based model which is at least occasionally outperformed

by other models Most of the firms operating on the market are privately held and therefore

there are no market data (eg stock prices) are available Theose firms are usually financed by

banks which are obligated to assess their creditworthiness and monitor their performance In

the case of internationally active banks it is especially important to use a universal model across

the banking group for distress prediction for provisioning and for economic capital calculation

In following subsequent regulatory requirements they need to validate models and prove their

efficacy Thus we found it useful to analyze the performance of an accounting-based model in

an international context

In our study we use a large international sample of firms to assess the classification performance

of the Zrsquorsquo-Score model in bankruptcy prediction3 We analyze its the modelrsquos performance in for

firms from 31 European and three non-European countries (China Colombia and the USUS) T

these firms are mostly privately held and aA large number of firms are from non-manufacturing

industries Therefore we use the version of the model developed by Altman (1983) for private

and public manufacturing and non-manufacturing firms (Zrsquorsquo-Score model) Such an extensive

international analysis of the performance of the Z-Score modelrsquos performance has not been

presented thus farto date We regard our review and analysis as important contributions to the

economic literature

3 We use bankruptcy failure default and financial distress as equivalents

7

The paper is structured as follows In the introduction we present a brief summary of the

development of failure prediction research starting beginning with Beaver (1966) and Altman

(1968) In the second section we summarize the original Z-Score model (1968) and its extension

for private firms that is the Zrsquo-Score and Zrsquorsquo-Score models (1983) IThen in the third section we

present the results and conclusions from the literature review on these models The fourth

section presents seven research hypotheses on the performance of the Zrsquorsquo-Score model for

empirical analysis In the fifth section the empirical data and statistical methods are discussed

while the sixth section presents empirical findings Finally the seventh section summarizes the

study

2 Classic Z-Score models

21 Z-Score model for public firms

Altmanrsquos (1968) initial sample was composed of 66 corporations with 33 firms in each of the two

groups The bankrupt group (Group 1) consisted of manufacturers that filed a bankruptcy

petitions under Chapter X of the National Bankruptcy Act during the period 1946ndash1965 period

The mean asset size of these firms was 64 million USD with a ranginge of between 07 and 259

million USD Altman recognized that this group was not homogenous with respect to size and

industry although all firms were relatively small and from manufacturing industries Therefore

he attempted to make a carefully selection select of non-bankrupt firms (Group 2) Group 2

consisted of a paired sample of manufacturing firms chosen on a stratified random basis These

firms were stratified by industry and size with the asset size range restricted to 1ndash25 million USD

Altman eliminated both the small firms (less than 1 million US dollars in total assets) because of

8

a lack of data and the very large firms because of a lack of data because offor small firms and the

rarity of bankruptcies among these firms in that period of large firms He did not match the asset

size of the two groups exactly and therefore the firms in Group 2 are were slightly larger than

those in Group 1 The data collected for the firms in both groups were from the same years For

Group 1 the data were derived from financial statements one reporting period prior to

bankruptcy

Using financial statements Altman compiled a list of 22 potentially important financial ratios

based on financial statements for evaluation He classified these variables into five standard ratio

categories liquidity profitability leverage solvency and activity ratios These ratios were

chosen based on their popularity in the literature and their potential relevancey to the study The

final discriminant function estimated by Altman (1968) is as follows

Z = 0012X1 + 0014X2 + 0033X3 + 0006X4 + 0 999X5 (eq 1)

where X1 = Working CcapitalTotal Aassets X2 = Retained EarningsTotal Aassets X3 = Earnings before Iinterest and TtaxesTotal Aassets X4 = Market Vvalue of EequityBook Vvalue of Ttotal Lliabilities X5 = SalesTotal Aassets Z = Overall Index 22 Zrsquo-Score and Zrsquorsquo-Score models for private firms

The original Z-Score model was based on the market value of the firm and was thus applicable

only to publicly traded companies Altman (1983) emphasized that the Z-Score model is a

intended for publicly traded firms model and that ad hoc adjustments are not scientifically valid

9

Therefore Altman (1983) advocated a complete re-estimation of the model substituting the

book value of equity for the market value in X4 Using the same data Altman extracted the

following revised Zrsquo-Score model

Zrsquo = 0717X1 + 0847X2 + 3107X3 + 0420X4 + 0 998X5 (eq 2)

where X4 = Book value of equityBook value of total liabilities with the other variables being

the same as those in the original (1968) Z-Score model

DAltman ue to the lack of a private firm database Altman did not test the Zrsquo-Score model on a

secondary sample due to the lack of a private firm data base However he analyzed the accuracy

of a four-variable Zrsquorsquo-Score model that excludeding the SalesTotal assets ratio X5 from the

revised model because of a potential industry effect The industry effect that is more likely to

take place when this kind of industry-sensitive variable (asset turnover) is included in the model

Altman then estimated the following four-variable Zrsquorsquo-Score model (Altman 1983)

Zrsquorsquo = 325 + 656X1 + 326X2 + 672X3 + 105X4 (eq 3)

The EBITTotal assets ratio X3 made the highest contributioncontributed most to the

discrimination power in this version of the model The classification results for the Zrsquorsquo-Score

model were identical to the revised five-variable Zrsquo-Score model In the currentis study our

empirical analysis focuses on the performance of the Zrsquorsquo-Score model version in predicting

bankruptcy prediction wheredue to it has its widest scope that is because as it is intended for

both privately held and publicly listed firms and for both manufacturing and non-manufacturing

firms

10

3 Literature review

We searched for papers published from the year 2000 onward 4 in prominent international

journals or and books We selected 31 articles in which the Z-Score was either used either as a

failure prediction proxy or was assessed mostly in terms of predictive ability Of the 31 studies

Altmanrsquos Z-Score model5 was used in 16 cases as the measure of distress or of financial strength

In 13 studies Altmanrsquos original model was modified and (or) verified including re-estimation and

in 2 cases it was used solely for the robustness check As Pindado et al (2008) noted the Z-Score

was also used for other purposes such as the evaluation of the costs and benefits of covenants

in bonds the choice of the type of debt type (bank vs non-bank private or public) and the

relationship between investment and internal funds We focused on this part of the literature

that verified and (or) modified Altmanrsquos original model The wide broad usage of the Z-Score

model foras a measuringe of financial distress and performingas well as for the robustness checks

indicates that it is acceptabilityed as a reasonable simple and consistent measure of the

distressed firms

In the case of the modification of the The most common modification to the Z-Score model

model the most common change was the use of other estimation techniques or country-specific

data The use of Altmanrsquos ratios in combination with techniques other than MDA has improved

the its prediction capability TAdditionally the application of new data of on both US and non-US

4 Research devoted to the application of the Z-Score model before 2000 was reviewed by Grice and Ingram (2001) 5 Most of the models focused on stock- exchange- listed firms thus the Zrsquo-Score and Zrsquorsquo-Score models were not used

11

firms also improved model performance Kwak et al (2005) applied used Multiple Criteria Linear

Programming (MCLP) to model 5 Altman and 9 Ohlson variables They used with data on US

bankrupt US firms from 1992 to 1998 and nearly 6 times more matched US control firms The

MCLP approach performed better than Altmanrsquos original model and gave results similar to or

better than those of Ohlsonrsquos original model The original models were not recalculated and the

authors referred solely to their original prediction rates only Merkevicius et al (2006) using data

on US and Lithuanian firms developed a hybrid artificial -discriminantte model combining MDA

and an unsupervised learning artificial neural network (called the SOM-Altman model) with the

use of data on US and Lithuanian firms Thise hybrid SOM-Altman model reached a high

prediction rate of 9235 Xu and Zhang (2009) applied Altmanrsquos Z-score Ohlsonrsquos O-score and

Mertonrsquos distance-to-default (D-score) models to Japanese firms to check whether these models

are useful for bankruptcy prediction in Japan They also ldquomergedrdquo theose models into a new C-

score model They then introduced variables unique to Japan to check whether corporate

structure variables have any impact on the probability of bankruptcy t They called this the model

X-score model These two models were useful for Japanese firms in predicting bankruptcy

prediction but the market-based model was the most successful In summary the developed C-

score and X-score (with country-specific variables) models improved bankruptcy prediction

Tinoco and Wilson (2013) used the original Z-Score as one of the benchmarks to assess the

performance of their model developed for UK listed companies with combined accounting

market and macroeconomic data Altmanrsquos Z-Score presented very good classification accuracy

in the case of financially distressed firms (81 vs 87 for the new model) however it was less

correct for financial health prediction Another modification was introduced by Lyandres and

12

Zhdanov (2013) who posed the question of whether the inclusion of variables related to

investment opportunities improved the predictive power of three models (Altmanrsquos Z-score

model and Zmijewskirsquos and Shumwayrsquos models) They used three proxies for investment

opportunities (market-to-book value-to-book and RampD-to-assets) The measures of investment

opportunities were linked to the likelihood of default The inclusion of either of these measures

improved the out-of-sample forecasting ability of all three

The verification of Altmanrsquos model concentrated on its efficacy or on how it compares a

comparison with other accounting-based market-based or hazard models However although

the original Z-Score model was not solely an accounting-based approach because the market

value of equity was utilized we classify it here as an accounting -based approachbecause the

market value of equity was utilized Grice and Ingram (2001) used a new novel data set of US

firms and posed three questions about the efficacy of Altmanrsquos model They concludinged that

the prediction accuracy of Altmanrsquos model had declined over time and that the coefficients of

the model had significantly changed which means that the relation between the financial ratios

and the signs of financial distress had changed over time The model was sensitive to industry

classification (more efficient for manufacturing firms than for non-manufacturing firms) but was

not sensitive to the type of financial distress Similar conclusions were drawn by Grice and Dugan

(2003) regarding Ohlsonrsquos (1980) and Zmijewskirsquos (1984) models

Hillegeist et al (2004) compared Altmanrsquos Z-score and Ohlsonrsquos O-score (with original and

updated coefficients) with the model based on BSM option pricing (BSM-Prob) They used

13

relative information content tests to compare the out-of-sample performance BSM-Prob

outperformed accounting-based models The conclusions were robust to various modifications

of accounting-based models such as updated coefficients industry effects and the separation

of variables Chava and Jarrow (2004) employed an extended bankruptcy database of US listed

firms to prove the superiority of Shumwayrsquos model (2001) over Altmanrsquos (1968) and Zmijewskirsquos

(1984) models The authors re-estimated the models over the 1962ndash1990 period and forecasted

bankruptcies over the 1991ndash1999 period In the case of Shumwayrsquos model 744 (in the first

decile) of the bankruptcies were correctly identified for with Altmanrsquos model it was 632 and

for with Zmijewskirsquos model it was 432 Shumwayrsquos market-based model also outperformed

accounting-based models in terms of the ROC curve (091)

Reisz and Perlich (2007) used the barrier option for bankruptcy prediction and compared its

discriminatory power with other market-based models and Altmanrsquos Z-Score and Zrdquo-Score The

dataset covered nearly 6000 industrial firms over the period 1988ndash2002 period The authors

proved the domination superiority of Altmanrsquos Z-Score and Zrdquo-Score models for short-term (up

to 1 year) bankruptcy prediction For medium- and long-term bankruptcy prediction their barrier

option model outperformed the other models Pindado et al (2008) developed an ex -ante model

for the estimation of financial distress likelihood (FDL) using awith the use of panel data

methodology and they presented a financially (not legally) based definition of distress Their

sample covered 1583 US companies and 2250 companies from other G-7 countries for the

period 1990ndash2002 period They used a re-estimated Z-Score as a benchmark The FDL model

outperformed the Z-Score model in terms of stability and classification power for different

14

countries and time periods In the case of the re-estimated Z-Score model only profitability and

retained earnings kept maintained their significance for different years and countries

Wu et al (2010) evaluated the performance of five models (Altman 1968 Ohlson 1980

Zmijewski 1984 Shumway 2001 and Hillegeist et al 2004) using an up-to-date data set for

US listed firms Based on these models the authors built their own integrated model that is a

multi-period logit model with an expanded set of variables The integrated model which

combined accounting and market data as well as firmsrsquo characteristics outperformed the other

models Altmanrsquos Z-score performed poorly in compared withison to the other 4 models

Shumwayrsquos model performed best Hillegeist et alrsquos model performed adequately and Ohlsonrsquos

and Zmijewskirsquos models performed adequately although their performance deteriorated over

time

Jackson and Wood (2013) tested 13 different models of bankruptcy prediction and assessed their

efficacy using the ROC curve They selected three single- variable models three accounting-based

models (including Altmanrsquos Z-Score) in two versions (with up-dated coefficients and a neural

network approach) and four contingent claims models The contingent claims models and the

latter group outperformed the other models The best four best models were contingent claims

models based on European call and barrier options Although tThe predictive performance

improved withIn the case of the application of the neural network to accounting-based models

the predictive ability improved but it was still lower than within the case of the market-based

models

15

Acosta-Gonzaacutelez and Fernaacutendez-Rodriacuteguez (2014) used genetic algorithms with the Schwarz

information criterion (GASIC) for variable selection combined with the logit model for bankruptcy

prediction Altmanrsquos Z-Score model was used as one of two benchmarks for the authorsrsquo model

evaluation For one- step- ahead forecasting Altmanrsquos model was better in at predicting failed

firms but the type II error was high For two- and three- steps- ahead forecasts the performance

of the models was similar forin terms of failed firms but forin the case of non-failed firms and

the prediction accuracy of Altmanrsquos model was worse For four- steps- ahead forecasts the GASIC

model outperformed the other models forin the case of failed firms and but it performedwas

comparablye forin the case of non-failed firms

In general Altmanrsquos Z-Score model underperformed in compared withison to market-based

models but there was also evidence indicated that it performed well for short-term distress

prediction The question of whether market-based models are perform better than accounting-

based models has been raised many times (eg discussion in Das et al 2009 Bauer and d

Agarwal 2014) Our purpose is not to contribute to this strand of research but rather to focus on

the accounting-based approach In this study we analyze primarily analyze privately held firms

so there are by definition no market data for these firms In this case aAn accounting-based

approach is the only solution that could be appliedapplicable in this case by banks as lenders or

by investors holding debt securities of firms not listed on the stock exchange Thus far most of

the studies have concentrated on the US market only a few of them have used data from other

16

countries such as Japan the UK Lithuania or the G-7 countries Our analysis is a significant

extension of the previous research

4 Methodology and research hypotheses

The literature survey shows that the Z-Score model (publicly traded firms) the Zrsquo-Score model

(private manufacturing firms) and the Zrsquorsquo-Score model (private and publicly

traded manufacturing and non-manufacturing firms) have been adapted for different purposes

In this study we are first interested first in assessing the performance of the original Zrsquorsquo-Score

model in classifying bankrupt and non-bankrupt firms in an international context with special

focus on the European market However we also validate the results in a set of non-European

countries to generalize the results for circumstances outside Europe Second we will re-estimate

the model using extensive international data and then use the re-estimated Zrsquorsquo-Score model as a

benchmark for assessing the effects of different factors on the modelrsquos performance in terms of

classification accuracy We will assess the effects of the following five factors on this

performance year of bankruptcy size of firms age of firms industry and country of origin WIn

all we will test a set of hypotheses based on the effects of the model on performance on two

different levels First we will test a set of hypotheses on a pooled set of all firms and second on

data from each country individually CIn compared withison to the previous research our this

studyrsquos contribution is the focus on an international context not just simply the model

application or re-estimation for of a given countryrsquos data Because weour aim is focusing on the

performance of the Zrsquorsquo-Score mModel when using ain large body of international data the

research hypotheses are of a technical nature and are presented given as follows

17

H1 Obsolescence of the coefficients The Zrsquorsquo-Score mModel was originally estimated using the

same sample of firms that was used to develop the original Z-Score mModel The bankruptcies

in the estimation data occurred during the period 1946ndash-1965 period Thus the oldest

observations are from nearly seventy years ago during the post-war period Altman (1983)

recommended utilizing data as near to the present as possible when developing a bankruptcy

prediction model It is obvious that firmsrsquo the financial behavior of firms and their business

environment have significantly changed since then Thereforen potentially making the

importance of the financial ratios as reflected by the coefficients of the model may differ from

their original importance reflected by the coefficients of the model Therefore we suggest as

the first hypothesis (H1) that the re-estimation of the coefficients of the four original variables

of the Zrsquorsquo-Score model will improve the classification performance of the model in an

international context This hypothesis is supported by the previous research (eg Grice and

Ingram 2001) and by practice H1 aims to support this evidence on the international level and is

expressed as follows

H1 RThe re-estimatingon of the coefficients of the Zrsquorsquo-Score model improves the its

classification accuracy of the model

H2 Method of estimation The original Zrsquorsquo-Score model has been estimated using MDA However

MDA is based on the ordinary least squares (OLS) method and thus requires assumptions of

multinormality homoscedasticity and linearity which are not often met in empirical financial

ratio analysis Therefore we re-estimate the Zrsquorsquo-Score model using logistic regression analysis

(LRA) to assess the effect of the method of estimation method LRA does not require most of the

18

restricting assumptions of MDA In LRA the multivariate normality of the independent variables

is not required nor are homoscedasticity and linearity For the sake of OLS MDA can be more

useful than LRA for small samples such as the original sample of 66 firms used in the estimation

of the Zrsquorsquo-Score mModel However in a large sample LRA may potentially perform better In this

study we use large samples which is advantageous for LRA Therefore our second hypothesis

(H2) suggests is that the classification performance of the re-estimated Zrsquorsquo-Score model will be

improved when it is estimated using LRA instead of MDA The model re-estimated for the original

variables using LRA and all pooled data is called here the Zrsquorsquo-Score LR-modelLR model The

performance of this re-estimated model is used in this study as the benchmark for further

analyses Thus the second hypothesis is as follows

H2 The prediction accuracy of the logistic regression version of the Zrsquorsquo-Score model is higher

than that of the multiple discriminant analysis version

H3 Bankruptcy year The model based on the relationship between bankruptcy and financial

ratios is likely to be affected by the macroeconomic environment These effects may significantly

decrease the classification accuracy of the model If the model is estimated using data from one

year and it will be applied to data from another year the validity of the model can be questioned

Business cycles inIn terms of economic growth credit policy and interest rates business cycles

can ahave an effect on the boundary between bankrupt and non-bankrupt firms The original Zrsquorsquo-

Score model is estimated using data from the period 1946ndash1965 period which includes several

business cycles Therefore the model is not focused on any specific stage of a cycle and does not

explicitly take into account of the bankruptcy year Altman (1983) suggested gathering data from

19

firms for the last most recent few years when developing a prediction model In this study the

benchmark Zrsquorsquo-Score LR -model will beis estimated for a shorter period than in the original

estimation although it covers several recent years on for different stages of the business cycle in

different countries Therefore the third hypothesis (H3) assumes is that the classification

accuracy of the benchmark model can be increased by explicitly taking into account of the year

of bankruptcy in the estimation of the model estimation The third hypothesis is presented as

follows

H3 The prediction accuracy of the modelrsquos prediction accuracy is higher whenith the

effect of the year of bankruptcy is included is higher than that of the model without the

effect

H4 Size of the firm The boundary between bankrupt and non-bankrupt firms is different for small

and larger firms which decreases the performance of the model estimation when estimated for

data from one size category and isare applied to for data from another size category For the

bankrupt and non-bankrupt firms in the original data for Zrsquorsquo-Score model estimation the range

of asset sizes was ranged between approximately 1 and 25 million US dollars The data did not

include very small or very large firms Altman (1983) regarded the suitability of the original Z-

Score model (and likewisein the same way the Zrsquorsquo-Score model) for all firms as debatable

because of this omission it did not scrutinize very large or very small firms In the currentis study

the benchmark Zrsquorsquo-Score LR -model will beis estimated for data from many size categories from

very small firms to very large firms The fourth hypothesis (H4) assumes that the classification

performance of the uniform benchmark LR -model based on the original four financial variables

20

of the Zrsquorsquo-Score model is improved when the size category of the firm is explicitly taken into

account Thus we present the fourth hypothesis as follows

H4 The modelrsquos prediction accuracy of the modelis higher whenith the effect of size is

includede is higher than that of the model without this effect

H5 Age of the firm International insolvency statistics generally show that bankruptcy risk is a

function of the age of the firm Very young firms typically show a very high risk The original Zrsquorsquo-

Score model does not explicitly take age into account age However Altman (1983) noted that

the age of a firm is implicitly considered in the Retained EarningsTotal Assets ratio (X2) which

was regarded as a new ratio in the bankruptcy prediction context A relatively young firm will

probably show a low ratio because it has not had time to build up cumulative profits Thus a

young firm is to some degree discriminated against in the model and its likelihood of being

classified as bankrupt is relatively higher than that of an older firm The incidence of failure is

much higher in the early years of a firm AlthoughDespite that the age of the firm in this way is

implicitly taken into account in X2 we expect that an explicit consideration of the age will improve

the classification accuracy bydue to controlling for the age factor Therefore the fifth hypothesis

(H5) proposes that the performance of the uniform benchmark model based on the original four

financial variables of the Zrsquorsquo-Score model is increasesd when the age of the firm is explicitly taken

into account The fifth hypothesis is can be presented as follows

H5 The modelrsquos prediction accuracy of the model is higher when with the effect of firm

age is higher than that of the model without this effectincluded

21

H6 Industry of the firm The original Zrsquo-Score model was estimated only for manufacturing firms

Altman (1983) stated that it would be ideal to develop a bankruptcy prediction model utilizing a

homogenous group of bankrupt firms If we are interested in a particular industry grouping we

should gather data from bankrupt and non-bankrupt firms in these groupings Previous studies

show that financial distress analysis is influenced by the industry effect (Smith and Liou 2007)

Firms in different industries tend to report different levels of the same financial ratios which may

have an effect on the boundary between bankrupt and non-bankrupt firms This industry effect

may be present in the Zrsquo-Score model especially due to the SalesTotal Assets ratio (X5) This ratio

which showed the least significance on a univariate basis while making but it had made a very

significant contribution to the discriminant power of the multivariate model Altman (1983)

recognized the potential industry effect due to a the wide variation among industries in asset

turnover and specified the Zrsquorsquo-Score model without X5 However also the Zrsquorsquo-Score model was

also estimated using the original sample of manufacturing firms In our analysis the uniform

benchmark model based on the original four financial variables of the Zrsquorsquo-Score model is

estimated for a statistical sample representing different industries Therefore the sixth

hypothesis (H6) assumes that an explicit consideration of industry will improve the classification

accuracy of this benchmark model H6It can be expressed in the following form

H6 The modelrsquos prediction accuracy of the modelis higher whenith the effect of industry is

higher than that of the model without this effectincluded

H7 Country of origin The original Zrsquorsquo-Score model has been estimated only for USUS firms It

can be expected that the international applicability of the model to other countries is affected by

22

country-specific differences The eEconomic environment legislation culture financial markets

and accounting practices in a country may affect the financial behavior of firms and the boundary

between bankrupt and non-bankrupt firms These factors may potentially weaken the

classification performance of the model in for other countries outside other than that e one fore

country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the

seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm

into account will improve the classification accuracy of the benchmark model In our empirical

study the country effect will beis assessed by including a variable of for country risk Therefore

the seventh hypothesis is presented as follows

H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country

risk is higher than that of the model without this effectincluded

5 Empirical data and statistical methods

51 Sample of firms

The principal data of for this study are were extracted from the ORBIS databases of Bureau Van

Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the

moment of sampling contained administrative information on more than 50 million European

firms However income statement and balance sheet information was available for

approximately 8 million companies More than 99 of the companies covered in this database

are private companies from different various industries justifying the use of the Zrsquorsquo-Score model

instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all

industrial groupings and for both private and public entities (Altman 1983 124 Altman and

Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope

23

of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all

industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly

used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that

international comparability may be a problem when administrative firm-level data are

internationally pooled For administrative data Although the definition of variables is usually less

harmonized for administrative data tThis is less of a problem in the ORBIS database because of

the common international format of balance sheets For example although some discrepancies

in profitloss statements may arise because of differences in fiscal systems across countries

balance sheet variables largely adhere to international standards

AThere are a number of factors that influence the international applicability of bankruptcy

prediction models These factors include accounting legislation and practice creditor rights and

investor protection judicial efficiency corporate governance bankruptcy protection and

insolvency management and firm risk-taking These factors strongly differ between European

and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-

Score model outside Europe First it is especiallyparticularly important to include the USUS as

because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It

is include d in our study as well because it has the largest market capitalization in the world

Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it

applicable to emerging market companies Therefore we include firms from China and Colombia

which represent two very culturally and institutionally very dissimilar emerging market

countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt

24

firms ie the limit we set for European countries) from ORBIS World were not

available Thus the results are also estimated and tested for three non-European countries (the

United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-

Score modelrsquos performance The samples of firms from these countries are were extracted from

ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms

from around the world

SFor statistical sampling several requirements are set for the statistical sampling of the empirical

data First we require that the firm to be selected be an industrial (non-financial) company

Second its owners must have limited liability (so partnerships and sole proprietors are left out

of the study) Third we set a minimum requirement for the size of the firm Because the financial

ratios of very small firms are generally too unstable for a failure prediction model these firms

are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have

exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we

include in our estimation sample firms from all European countries and 3 pre-selected non-

European countries where the number of failed firms is more greater than 60 If the number of

failed firms for any European country is less than 60 the firms from this country are only included

only in the test sample For qualifying European countries the firms are randomly classified in

the estimation and test samples so that the number of firms is approximately equal in both

samples Thus our estimation data include firms from 28 European and 3 non-European

countries Fifth all failed firms that fulfill the above requirements are included in our samples

However if the number of non-failed firms in a country is very high a sample is randomly

25

selected from that country Finally the time span of fiscal years potentially available for this study

ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in

the database are from a financial period within 2007 and 2010 earlier years are also excluded

for non-failed firms for comparability All qualifying observations of non-failed firms from the

years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses

of failed firms to the last most recent financial statements available before failure The four

independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize

outliers

Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test

data by country The estimation sample includes data from 2602563 non-failed and 38215

failed firms from 28 European and 3 non-European countries The test sample is slightly larger

because it includes data from 31 European and 3 non-European countries From For the country

of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt

firms The available US data that were available consist only of listed (and delisted) firms From

China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are

included in the estimation sample although there are only 32 such firms altogether7 The Chinese

datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status

are separately analyzed separately only in for the test data8 ST firms are listed firms suffering

6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor

26

from serious financial difficulties Excluding the special US data and the two non-private Chinese

datasets 994 of the observations in the data are private firms

(Table 1 here)

52 Status of failed firms

ORBIS has five classes for potentially active firms (active default of payment receivership

dormant and branch) and seven classes for inactive firms that no longer carry out business

activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation

branch and no precision) From Among these classes only active is selected to represent non-

distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by

considering (with exceptions described below) a firm failed if its status in ORBIS is stated as

bankruptcy However because of the small number of bankrupt firms in some countries we also

consider receivership (active) firms as failed even if they are active These firms generally suffer

from serious financial distress However firms in liquidation are generally not included in the

sample of failed firms Firms in liquidation may depending on the country contain firms that

have ceased activities due to reasons other than failure (mergers discontinuing the operations

of a daughter company or of a foreign branch etc) Therefore for most countries we select only

firms that are coded as being bankrupt or under receivership However there are a number of

special cases where failed firms are coded under a different status heading These countries or

samples are the following

Country Status categories Bulgaria In liquidation Bankruptcy

27

Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the

study (for example Switzerland) If a country had only a very small number of failed firms it was

dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries

including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status

classes (including the bankruptcy category) are not completely homogenous within European

countries due to different legislations although there are obvious similarities in insolvency acts

(Philippe et al 2002) China is a special case because it includes samples with three different

criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are

two different samples (liquidation and receivership)

53 Statistical methods

In this study seven research hypotheses are drawn for statistical testing The statistical analysis

will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The

classification performance of the original model is assessed by the AUC (Area Under Curve)

measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close

connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random

28

model and 05 for a model with an average classification performance In SAS software is used

for all statistical analysess SAS software is used

The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is

tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical

method (multiple discriminant analysis or MDA) The problem is that the estimation sample

includes different numbers of failed and non-failed firms from 31 countries In the original Z-

Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from

the USUS Following the characteristics of these data we weight the firms so that the weights

for the failed and non-failed firms are equally In so doingthis way the non-proportional

sampling in from different countries does will not affect the re-estimated model The number of

firms from different countries however varies significantly leading to greater weights for larger

countries To avoid this problem the observations are also weighted so that each country has an

equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using

these weighted data and the resulting AUC is compared with that based on the original model

The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score

model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is

based on less- restrictive statistical assumptions than MDA In this estimation the dependent

variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA

does not require that independent variables be multivariate normal or that groups have equal

covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA

29

creates a score (logit) score L for every firm It is assumed that the independent variables are

linearly related to L This score or logit is used to determine the conditional probability of failure

as follows

= (eq 4)

where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the

original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing

the statistical significance of the difference between AUCs for this LR model and for the re-

estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it

is used as a benchmark for further statistical AUC comparisons because LR is applied as the

principal method in the testing of the remaining research hypotheses

The third hypothesis (H3) is associated with the performance effect of taking account of the

bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on

the following logit

(eq 5)

where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model

bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and

D1 = 1 when year = 2008 0 otherwise

D2 = 1 when year = 2009 0 otherwise

D3 = 1 when year = 2010 0 otherwise

)|1( XYp )( 441101

1

1

1XbXbbL ee

3

1

4

10

jjji

ii DcXbbL

30

The dummy variables do not directly refer to the bankruptcy year which that is not given in the

data basedatabase but rather to the last available year before bankruptcy For failed firms there

is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is

the base category If the AUC of this extended LR model statistically significantly exceeds the AUC

of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3

Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third

hypothesis above However for each hypothesis appropriate variables are used instead of the

year dummies Hypothesis H4 addresses the performance effect of taking size into account the

size and it will beis tested using by adding two additional variables measuring firm size in to the

LR -model In this LR model size is measured by the natural logarithm of total assets and its

squared form In this way the effect of logarithmic size can be reflected by in a function following

the second-order parabola Hypothesis H5 predicts that the classification performance improves

when the age of the firm is explicitly taken into account When testing this hypothesis the

category 6ndash14 years category is used as the base category and two dummy variables are

incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis

H6 looks at whether is associated with the classification performance is affected bythe effect of

the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt

is tested here using dummy variables for seven industries (D1 restaurants and hotels D2

construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and

water production D7 information technology) with all other industries acting as the base

category

31

Hypothesis H7 predicts that the explicit consideration of the country of origin will improves

classification performance This hypothesis is tested by using country risk measures instead ofnot

using dummy variables for countries but using country risk measures instead The country risk of

each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the

annual closing of accounts The rating is numerically recoded such that the best rating AAA

equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating

D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial

ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-

modelLR models with the original four financial ratios and the additional variables specified in

the hypotheses are estimated for all data In addition an LR model including all additional

variables is estimated for all data to assess the simultaneous effect of all variables Finally six of

the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not

included in this country- level testing hypothesis H7 is not tested because the additional variable

(country risk) is constant within the country

6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models

Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score

model for all data The variation in the ratios is significant as shown by the standard deviation

and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-

failed firms are close to each other indicating a symmetry of distributions However this is not

32

the case for the failed firms For the faileddistressed firms the median exceeds the mean for

these three ratios indicating negatively skewed distributions For X4 (BVETD) the means

significantly exceed the median for both failed and non-failed firms indicating a positively

skewed distribution For each of the four variables both the mean and the median are higher for

non-failed firms than for failed firms which is consistent with the expectations The difference

between the means of the non-failed and failed firms is larger in the original USUS data than in

our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is

approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of

EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may

indicate lower classification accuracy than in the original sample

(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates

(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the

large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model

Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same

statistical method or MDA The coefficients here are negative because the models are estimated

using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight

than in the original USUS data while the weights of WCTA and BVETD have proportionally

decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the

logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR

model These coefficients are directly comparable with those of the MDA model which isas

expected for the this exceptionally large sample The differences in the coefficients of the original

33

four variables among the eight LR-modelLR models (Models 1-8) are small For each model the

coefficient of BVETD is very close to zero This indicatinges that the original four variables and

the additional variables are quite independent of each other

(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-

modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate

that after 2007 (the base category) this risk of failure significantly decreased year by year The

base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due

to as a result of the global financial crisis The crisis played a significant role in the failure of key

businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The

effects were especially felt in Europe The coefficients of Model 4 for the size variables show that

the contribution of size on to the logit (risk measure) is atreaches its maximum value when

logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model

5 confirms the riskiness of young firms because the risk of failure is very high for newly founded

firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The

coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky

industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is

statistically significant (because of the large sample) but negative and very close to zero Finally

the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7

62 All data performance of the Zrsquorsquo-Score models

34

Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1

refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of

all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average

accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)

Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its

performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The

lower part of the table shows the AUCs for the countries included only in the test data The

performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)

samples It is also low and for the liquidation firms in the United Kingdom

The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5

presents the differences of these medians between the non-failed and failed firms by country

This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of

medians This correlation which is high for each financial ratio showing that the effects of the

ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large

except for EBITTA which This implies with the exceptionally high AUC that the ST firms

systematically differ from the non-ST firms although the differences are not extremely large The

differences between the medians are very large in Poland for each ratio justifying the high AUC

They are also large and in Finland and in the Czech Republic where the difference in EBITTA is

average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in

all four ratios are below the average a result thatwhich is obviously associated with a low AUC

35

In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even

negative In Iceland and Ireland the differences only in EBITTA are exceptionally small

(Table 4 here)

(Table 5 here)

Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by

MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)

supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is

at approximately the average level The re-estimation of the coefficients has led to improved

classification accuracy in a number of countries especially in Bosnia China (ST) Norway and

Greece However it has impaired the classification accuracy in the UK and China (delisted)

Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-

modelLR model) showing the effect of the estimation method For the benchmark model the

AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between

among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR

model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country

This result was expected because the coefficients of the models are directly comparable The

similar results for the models may also indicate that the independent variables conform to

multinormality This last latter result confirms what most researchers in the field of default

classification models have concluded that the accuracy levels of MDA and logistical regression

36

models are extremely similar despite any statistical violations such as normality between MDA

and logistical regression models

Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)

than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects

are not positive for all countries The effects are positive for example for Russia Estonia

Germany Ireland and Latvia but these effects are not statistically significant There are

statistically significant negative effects for China (ST) and Serbia There are notable negative

effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are

due to the exceptional annual distributions of failed firms in these countries For the countries

with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the

countries with positive effects it is only a few percent For each group non-failed firms are quite

equally distributed over years When the coefficient of D3 is very low (-0666) it strongly

decreases the risk estimates of most failed firms in the former countries but of only a few failed

firms in the latter countries This which leads to the observed effects H3 is supported by

the level of the whole sample However in some country samples (at the level of individual

levelies) H3 is not supported

Model 4 (LR-modelLR model with size variables) performs better than the benchmark model

which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads

and to significant improvements in the AUC for eg China (delisted) and Austria For China

(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC

37

also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs

in a few countries but thise decrease is not significant Model 4 is based on the four original

variables and the size effect following a second-order parabola This kind of size effect is very

small for micro firms but increases when approaching its maximum value in middle-sized firms

The countries with a positive change in AUC typically have data where in which the percent of

failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized

failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In

contrast the countries with a negative change in AUC have data where in which the percentage

of failed micro firms is exceptionally high (70-80)

Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly

the same AUC as the benchmark model (AUC = 0748) However the difference is positive and

statistically significant giving at least marginal support to H5 (age effect) For nearly all countries

the effect of the age on AUC is small For Austria however thise effect is however positive and

significant For Austria the percentage of non-failed young firms (less than 6 years) among non-

failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed

firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong

positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only

a few non-failed firms which This obviously leads to an improvement in AUC

Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC

supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its

38

AUC is notably higher than the benchmark AUC only in only a few countries such asfor example

France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg

restaurants and hotels and the information technology industryies but it has a positive effect

for on the construction and manufacturing industries For the countries with a positive effect on

AUC the percent of non-failed firms in restaurants and hotels and information technology is

high while that of failed firms is low For the risky industries (construction and manufacturing)

these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many

failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)

firms Therefore the AUC will be increasesd This situation is reversed for the countries with a

negative effect on AUC (Austria and Slovenia) The samples of these countries include a high

percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC

Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher

classification performance (AUC = 0749) than the benchmark model This result gives only very

weak support for H7 (country of origin effect) However for each country the resultant AUC is

nearly identical to that given by the benchmark model This result was expected due to the

negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7

This result implies that country risk hasdoes not have noany effect on the boundary between

bankrupt and non-bankrupt firms

Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional

variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to

39

the benchmark AUC in all test data However the effect on the AUCs largely varies and is either

negative or positive in different countries The positive effect is large in several countries such

as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative

effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show

that the inclusion of additional variables in the original model will usually increase the AUC but

not in every country

The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with

the USUS firms being unlike the majority of other firms in this study being listed or delisted

companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese

firm samples is associated with very small differences between the medians of the non-failed and

failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable

with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed

firms is very high Prior studies based on Chinese ST -firms have also demonstrated good

predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for

additional research and modeling work regarding unlisted and delisted Chinese firms

63 Country-level data performance of the Zrsquorsquo-Score models

The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform

all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the

different models estimated for each country separately (country-level models) In this table the

ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated

40

from the country data this benchmark will beis clearly outperformed by the resulting MDA

(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and

Sweden) However these results give only weak support for H1 at the country level because the

effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and

2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs

given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed

by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation

method)

Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC

than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3

leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United

StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in

nearly every country supporting H4 This improvement is significant however only in the United

StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark

model but for which it is only 0710 for the benchmark model It This improvement is also notable

in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age

category dummies) gives higher AUCs than the benchmark model for several countries but none

of the improvements are significant The positive effect is particularly strong especially in

Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only

weakly supports H5 because these negative effects are relatively small

41

Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects

on AUCs when compared with the benchmark However the only significant effect is the negative

effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the

industry dummies However there are notable positive effects on the AUC in for example

France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak

support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in

AUC compared with the benchmark model in Colombia Iceland and the Unites States It also

leads and to notable improvements in AUC at least in Belgium Denmark Estonia France

Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are

found only in Austria and Slovenia Thus as a conclusion we can concludestate that the

classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can

remarkably increases in most countries when different effects are taken into account by

additional variables This increase is found in most European countries and in Colombia and the

United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-

modelLR model and it can only be slightly improved only slightly by additional variables

(Table 6 here)

7 Summary of the study and a suggested extension

The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model

originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is

assessed using a very large international data set We test how the original version of the Zrsquorsquo-

42

Score model performs in different countries and how re-estimations using another statistical

method and different additional variables affect the classification performance when the data

are very heterogeneous For this kind of testing seven research hypotheses on classification

performance are extractedformulated These research hypotheses are tested for all data and

separately for country data (country-level analysis) The estimation data are from 31 countries

and the results are validated for 34 countries The countries are mainly from Europe but three

non-European countries are included (China Colombia and the United StatesUS) The statuses

used in the classification are mainly bankruptcyactive but receivership firms are also considered

to have failed In the Chinese data ST (special treatment) and delisted firms are also separately

analyzed as failed firms

The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very

satisfactorily in an international context The effects of the four financial ratios on performance

are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small

contribution in re-estimation The original model performs very well in several countries such as

Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only

marginally improved the classification performance thus weakly supporting the obsolescence

hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust

across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion

holds for the re-estimation of the model using LRA because the performance results are very

similar with to those of MDA (H2) The use of additional variables in the model generally

improvesd the classification accuracy of the original model but the results for countries are

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

6

financial distress prediction or and analysis both in research and in practice Therefore question

arises of why focus on the accounting-based model which is at least occasionally outperformed

by other models Most of the firms operating on the market are privately held and therefore

there are no market data (eg stock prices) are available Theose firms are usually financed by

banks which are obligated to assess their creditworthiness and monitor their performance In

the case of internationally active banks it is especially important to use a universal model across

the banking group for distress prediction for provisioning and for economic capital calculation

In following subsequent regulatory requirements they need to validate models and prove their

efficacy Thus we found it useful to analyze the performance of an accounting-based model in

an international context

In our study we use a large international sample of firms to assess the classification performance

of the Zrsquorsquo-Score model in bankruptcy prediction3 We analyze its the modelrsquos performance in for

firms from 31 European and three non-European countries (China Colombia and the USUS) T

these firms are mostly privately held and aA large number of firms are from non-manufacturing

industries Therefore we use the version of the model developed by Altman (1983) for private

and public manufacturing and non-manufacturing firms (Zrsquorsquo-Score model) Such an extensive

international analysis of the performance of the Z-Score modelrsquos performance has not been

presented thus farto date We regard our review and analysis as important contributions to the

economic literature

3 We use bankruptcy failure default and financial distress as equivalents

7

The paper is structured as follows In the introduction we present a brief summary of the

development of failure prediction research starting beginning with Beaver (1966) and Altman

(1968) In the second section we summarize the original Z-Score model (1968) and its extension

for private firms that is the Zrsquo-Score and Zrsquorsquo-Score models (1983) IThen in the third section we

present the results and conclusions from the literature review on these models The fourth

section presents seven research hypotheses on the performance of the Zrsquorsquo-Score model for

empirical analysis In the fifth section the empirical data and statistical methods are discussed

while the sixth section presents empirical findings Finally the seventh section summarizes the

study

2 Classic Z-Score models

21 Z-Score model for public firms

Altmanrsquos (1968) initial sample was composed of 66 corporations with 33 firms in each of the two

groups The bankrupt group (Group 1) consisted of manufacturers that filed a bankruptcy

petitions under Chapter X of the National Bankruptcy Act during the period 1946ndash1965 period

The mean asset size of these firms was 64 million USD with a ranginge of between 07 and 259

million USD Altman recognized that this group was not homogenous with respect to size and

industry although all firms were relatively small and from manufacturing industries Therefore

he attempted to make a carefully selection select of non-bankrupt firms (Group 2) Group 2

consisted of a paired sample of manufacturing firms chosen on a stratified random basis These

firms were stratified by industry and size with the asset size range restricted to 1ndash25 million USD

Altman eliminated both the small firms (less than 1 million US dollars in total assets) because of

8

a lack of data and the very large firms because of a lack of data because offor small firms and the

rarity of bankruptcies among these firms in that period of large firms He did not match the asset

size of the two groups exactly and therefore the firms in Group 2 are were slightly larger than

those in Group 1 The data collected for the firms in both groups were from the same years For

Group 1 the data were derived from financial statements one reporting period prior to

bankruptcy

Using financial statements Altman compiled a list of 22 potentially important financial ratios

based on financial statements for evaluation He classified these variables into five standard ratio

categories liquidity profitability leverage solvency and activity ratios These ratios were

chosen based on their popularity in the literature and their potential relevancey to the study The

final discriminant function estimated by Altman (1968) is as follows

Z = 0012X1 + 0014X2 + 0033X3 + 0006X4 + 0 999X5 (eq 1)

where X1 = Working CcapitalTotal Aassets X2 = Retained EarningsTotal Aassets X3 = Earnings before Iinterest and TtaxesTotal Aassets X4 = Market Vvalue of EequityBook Vvalue of Ttotal Lliabilities X5 = SalesTotal Aassets Z = Overall Index 22 Zrsquo-Score and Zrsquorsquo-Score models for private firms

The original Z-Score model was based on the market value of the firm and was thus applicable

only to publicly traded companies Altman (1983) emphasized that the Z-Score model is a

intended for publicly traded firms model and that ad hoc adjustments are not scientifically valid

9

Therefore Altman (1983) advocated a complete re-estimation of the model substituting the

book value of equity for the market value in X4 Using the same data Altman extracted the

following revised Zrsquo-Score model

Zrsquo = 0717X1 + 0847X2 + 3107X3 + 0420X4 + 0 998X5 (eq 2)

where X4 = Book value of equityBook value of total liabilities with the other variables being

the same as those in the original (1968) Z-Score model

DAltman ue to the lack of a private firm database Altman did not test the Zrsquo-Score model on a

secondary sample due to the lack of a private firm data base However he analyzed the accuracy

of a four-variable Zrsquorsquo-Score model that excludeding the SalesTotal assets ratio X5 from the

revised model because of a potential industry effect The industry effect that is more likely to

take place when this kind of industry-sensitive variable (asset turnover) is included in the model

Altman then estimated the following four-variable Zrsquorsquo-Score model (Altman 1983)

Zrsquorsquo = 325 + 656X1 + 326X2 + 672X3 + 105X4 (eq 3)

The EBITTotal assets ratio X3 made the highest contributioncontributed most to the

discrimination power in this version of the model The classification results for the Zrsquorsquo-Score

model were identical to the revised five-variable Zrsquo-Score model In the currentis study our

empirical analysis focuses on the performance of the Zrsquorsquo-Score model version in predicting

bankruptcy prediction wheredue to it has its widest scope that is because as it is intended for

both privately held and publicly listed firms and for both manufacturing and non-manufacturing

firms

10

3 Literature review

We searched for papers published from the year 2000 onward 4 in prominent international

journals or and books We selected 31 articles in which the Z-Score was either used either as a

failure prediction proxy or was assessed mostly in terms of predictive ability Of the 31 studies

Altmanrsquos Z-Score model5 was used in 16 cases as the measure of distress or of financial strength

In 13 studies Altmanrsquos original model was modified and (or) verified including re-estimation and

in 2 cases it was used solely for the robustness check As Pindado et al (2008) noted the Z-Score

was also used for other purposes such as the evaluation of the costs and benefits of covenants

in bonds the choice of the type of debt type (bank vs non-bank private or public) and the

relationship between investment and internal funds We focused on this part of the literature

that verified and (or) modified Altmanrsquos original model The wide broad usage of the Z-Score

model foras a measuringe of financial distress and performingas well as for the robustness checks

indicates that it is acceptabilityed as a reasonable simple and consistent measure of the

distressed firms

In the case of the modification of the The most common modification to the Z-Score model

model the most common change was the use of other estimation techniques or country-specific

data The use of Altmanrsquos ratios in combination with techniques other than MDA has improved

the its prediction capability TAdditionally the application of new data of on both US and non-US

4 Research devoted to the application of the Z-Score model before 2000 was reviewed by Grice and Ingram (2001) 5 Most of the models focused on stock- exchange- listed firms thus the Zrsquo-Score and Zrsquorsquo-Score models were not used

11

firms also improved model performance Kwak et al (2005) applied used Multiple Criteria Linear

Programming (MCLP) to model 5 Altman and 9 Ohlson variables They used with data on US

bankrupt US firms from 1992 to 1998 and nearly 6 times more matched US control firms The

MCLP approach performed better than Altmanrsquos original model and gave results similar to or

better than those of Ohlsonrsquos original model The original models were not recalculated and the

authors referred solely to their original prediction rates only Merkevicius et al (2006) using data

on US and Lithuanian firms developed a hybrid artificial -discriminantte model combining MDA

and an unsupervised learning artificial neural network (called the SOM-Altman model) with the

use of data on US and Lithuanian firms Thise hybrid SOM-Altman model reached a high

prediction rate of 9235 Xu and Zhang (2009) applied Altmanrsquos Z-score Ohlsonrsquos O-score and

Mertonrsquos distance-to-default (D-score) models to Japanese firms to check whether these models

are useful for bankruptcy prediction in Japan They also ldquomergedrdquo theose models into a new C-

score model They then introduced variables unique to Japan to check whether corporate

structure variables have any impact on the probability of bankruptcy t They called this the model

X-score model These two models were useful for Japanese firms in predicting bankruptcy

prediction but the market-based model was the most successful In summary the developed C-

score and X-score (with country-specific variables) models improved bankruptcy prediction

Tinoco and Wilson (2013) used the original Z-Score as one of the benchmarks to assess the

performance of their model developed for UK listed companies with combined accounting

market and macroeconomic data Altmanrsquos Z-Score presented very good classification accuracy

in the case of financially distressed firms (81 vs 87 for the new model) however it was less

correct for financial health prediction Another modification was introduced by Lyandres and

12

Zhdanov (2013) who posed the question of whether the inclusion of variables related to

investment opportunities improved the predictive power of three models (Altmanrsquos Z-score

model and Zmijewskirsquos and Shumwayrsquos models) They used three proxies for investment

opportunities (market-to-book value-to-book and RampD-to-assets) The measures of investment

opportunities were linked to the likelihood of default The inclusion of either of these measures

improved the out-of-sample forecasting ability of all three

The verification of Altmanrsquos model concentrated on its efficacy or on how it compares a

comparison with other accounting-based market-based or hazard models However although

the original Z-Score model was not solely an accounting-based approach because the market

value of equity was utilized we classify it here as an accounting -based approachbecause the

market value of equity was utilized Grice and Ingram (2001) used a new novel data set of US

firms and posed three questions about the efficacy of Altmanrsquos model They concludinged that

the prediction accuracy of Altmanrsquos model had declined over time and that the coefficients of

the model had significantly changed which means that the relation between the financial ratios

and the signs of financial distress had changed over time The model was sensitive to industry

classification (more efficient for manufacturing firms than for non-manufacturing firms) but was

not sensitive to the type of financial distress Similar conclusions were drawn by Grice and Dugan

(2003) regarding Ohlsonrsquos (1980) and Zmijewskirsquos (1984) models

Hillegeist et al (2004) compared Altmanrsquos Z-score and Ohlsonrsquos O-score (with original and

updated coefficients) with the model based on BSM option pricing (BSM-Prob) They used

13

relative information content tests to compare the out-of-sample performance BSM-Prob

outperformed accounting-based models The conclusions were robust to various modifications

of accounting-based models such as updated coefficients industry effects and the separation

of variables Chava and Jarrow (2004) employed an extended bankruptcy database of US listed

firms to prove the superiority of Shumwayrsquos model (2001) over Altmanrsquos (1968) and Zmijewskirsquos

(1984) models The authors re-estimated the models over the 1962ndash1990 period and forecasted

bankruptcies over the 1991ndash1999 period In the case of Shumwayrsquos model 744 (in the first

decile) of the bankruptcies were correctly identified for with Altmanrsquos model it was 632 and

for with Zmijewskirsquos model it was 432 Shumwayrsquos market-based model also outperformed

accounting-based models in terms of the ROC curve (091)

Reisz and Perlich (2007) used the barrier option for bankruptcy prediction and compared its

discriminatory power with other market-based models and Altmanrsquos Z-Score and Zrdquo-Score The

dataset covered nearly 6000 industrial firms over the period 1988ndash2002 period The authors

proved the domination superiority of Altmanrsquos Z-Score and Zrdquo-Score models for short-term (up

to 1 year) bankruptcy prediction For medium- and long-term bankruptcy prediction their barrier

option model outperformed the other models Pindado et al (2008) developed an ex -ante model

for the estimation of financial distress likelihood (FDL) using awith the use of panel data

methodology and they presented a financially (not legally) based definition of distress Their

sample covered 1583 US companies and 2250 companies from other G-7 countries for the

period 1990ndash2002 period They used a re-estimated Z-Score as a benchmark The FDL model

outperformed the Z-Score model in terms of stability and classification power for different

14

countries and time periods In the case of the re-estimated Z-Score model only profitability and

retained earnings kept maintained their significance for different years and countries

Wu et al (2010) evaluated the performance of five models (Altman 1968 Ohlson 1980

Zmijewski 1984 Shumway 2001 and Hillegeist et al 2004) using an up-to-date data set for

US listed firms Based on these models the authors built their own integrated model that is a

multi-period logit model with an expanded set of variables The integrated model which

combined accounting and market data as well as firmsrsquo characteristics outperformed the other

models Altmanrsquos Z-score performed poorly in compared withison to the other 4 models

Shumwayrsquos model performed best Hillegeist et alrsquos model performed adequately and Ohlsonrsquos

and Zmijewskirsquos models performed adequately although their performance deteriorated over

time

Jackson and Wood (2013) tested 13 different models of bankruptcy prediction and assessed their

efficacy using the ROC curve They selected three single- variable models three accounting-based

models (including Altmanrsquos Z-Score) in two versions (with up-dated coefficients and a neural

network approach) and four contingent claims models The contingent claims models and the

latter group outperformed the other models The best four best models were contingent claims

models based on European call and barrier options Although tThe predictive performance

improved withIn the case of the application of the neural network to accounting-based models

the predictive ability improved but it was still lower than within the case of the market-based

models

15

Acosta-Gonzaacutelez and Fernaacutendez-Rodriacuteguez (2014) used genetic algorithms with the Schwarz

information criterion (GASIC) for variable selection combined with the logit model for bankruptcy

prediction Altmanrsquos Z-Score model was used as one of two benchmarks for the authorsrsquo model

evaluation For one- step- ahead forecasting Altmanrsquos model was better in at predicting failed

firms but the type II error was high For two- and three- steps- ahead forecasts the performance

of the models was similar forin terms of failed firms but forin the case of non-failed firms and

the prediction accuracy of Altmanrsquos model was worse For four- steps- ahead forecasts the GASIC

model outperformed the other models forin the case of failed firms and but it performedwas

comparablye forin the case of non-failed firms

In general Altmanrsquos Z-Score model underperformed in compared withison to market-based

models but there was also evidence indicated that it performed well for short-term distress

prediction The question of whether market-based models are perform better than accounting-

based models has been raised many times (eg discussion in Das et al 2009 Bauer and d

Agarwal 2014) Our purpose is not to contribute to this strand of research but rather to focus on

the accounting-based approach In this study we analyze primarily analyze privately held firms

so there are by definition no market data for these firms In this case aAn accounting-based

approach is the only solution that could be appliedapplicable in this case by banks as lenders or

by investors holding debt securities of firms not listed on the stock exchange Thus far most of

the studies have concentrated on the US market only a few of them have used data from other

16

countries such as Japan the UK Lithuania or the G-7 countries Our analysis is a significant

extension of the previous research

4 Methodology and research hypotheses

The literature survey shows that the Z-Score model (publicly traded firms) the Zrsquo-Score model

(private manufacturing firms) and the Zrsquorsquo-Score model (private and publicly

traded manufacturing and non-manufacturing firms) have been adapted for different purposes

In this study we are first interested first in assessing the performance of the original Zrsquorsquo-Score

model in classifying bankrupt and non-bankrupt firms in an international context with special

focus on the European market However we also validate the results in a set of non-European

countries to generalize the results for circumstances outside Europe Second we will re-estimate

the model using extensive international data and then use the re-estimated Zrsquorsquo-Score model as a

benchmark for assessing the effects of different factors on the modelrsquos performance in terms of

classification accuracy We will assess the effects of the following five factors on this

performance year of bankruptcy size of firms age of firms industry and country of origin WIn

all we will test a set of hypotheses based on the effects of the model on performance on two

different levels First we will test a set of hypotheses on a pooled set of all firms and second on

data from each country individually CIn compared withison to the previous research our this

studyrsquos contribution is the focus on an international context not just simply the model

application or re-estimation for of a given countryrsquos data Because weour aim is focusing on the

performance of the Zrsquorsquo-Score mModel when using ain large body of international data the

research hypotheses are of a technical nature and are presented given as follows

17

H1 Obsolescence of the coefficients The Zrsquorsquo-Score mModel was originally estimated using the

same sample of firms that was used to develop the original Z-Score mModel The bankruptcies

in the estimation data occurred during the period 1946ndash-1965 period Thus the oldest

observations are from nearly seventy years ago during the post-war period Altman (1983)

recommended utilizing data as near to the present as possible when developing a bankruptcy

prediction model It is obvious that firmsrsquo the financial behavior of firms and their business

environment have significantly changed since then Thereforen potentially making the

importance of the financial ratios as reflected by the coefficients of the model may differ from

their original importance reflected by the coefficients of the model Therefore we suggest as

the first hypothesis (H1) that the re-estimation of the coefficients of the four original variables

of the Zrsquorsquo-Score model will improve the classification performance of the model in an

international context This hypothesis is supported by the previous research (eg Grice and

Ingram 2001) and by practice H1 aims to support this evidence on the international level and is

expressed as follows

H1 RThe re-estimatingon of the coefficients of the Zrsquorsquo-Score model improves the its

classification accuracy of the model

H2 Method of estimation The original Zrsquorsquo-Score model has been estimated using MDA However

MDA is based on the ordinary least squares (OLS) method and thus requires assumptions of

multinormality homoscedasticity and linearity which are not often met in empirical financial

ratio analysis Therefore we re-estimate the Zrsquorsquo-Score model using logistic regression analysis

(LRA) to assess the effect of the method of estimation method LRA does not require most of the

18

restricting assumptions of MDA In LRA the multivariate normality of the independent variables

is not required nor are homoscedasticity and linearity For the sake of OLS MDA can be more

useful than LRA for small samples such as the original sample of 66 firms used in the estimation

of the Zrsquorsquo-Score mModel However in a large sample LRA may potentially perform better In this

study we use large samples which is advantageous for LRA Therefore our second hypothesis

(H2) suggests is that the classification performance of the re-estimated Zrsquorsquo-Score model will be

improved when it is estimated using LRA instead of MDA The model re-estimated for the original

variables using LRA and all pooled data is called here the Zrsquorsquo-Score LR-modelLR model The

performance of this re-estimated model is used in this study as the benchmark for further

analyses Thus the second hypothesis is as follows

H2 The prediction accuracy of the logistic regression version of the Zrsquorsquo-Score model is higher

than that of the multiple discriminant analysis version

H3 Bankruptcy year The model based on the relationship between bankruptcy and financial

ratios is likely to be affected by the macroeconomic environment These effects may significantly

decrease the classification accuracy of the model If the model is estimated using data from one

year and it will be applied to data from another year the validity of the model can be questioned

Business cycles inIn terms of economic growth credit policy and interest rates business cycles

can ahave an effect on the boundary between bankrupt and non-bankrupt firms The original Zrsquorsquo-

Score model is estimated using data from the period 1946ndash1965 period which includes several

business cycles Therefore the model is not focused on any specific stage of a cycle and does not

explicitly take into account of the bankruptcy year Altman (1983) suggested gathering data from

19

firms for the last most recent few years when developing a prediction model In this study the

benchmark Zrsquorsquo-Score LR -model will beis estimated for a shorter period than in the original

estimation although it covers several recent years on for different stages of the business cycle in

different countries Therefore the third hypothesis (H3) assumes is that the classification

accuracy of the benchmark model can be increased by explicitly taking into account of the year

of bankruptcy in the estimation of the model estimation The third hypothesis is presented as

follows

H3 The prediction accuracy of the modelrsquos prediction accuracy is higher whenith the

effect of the year of bankruptcy is included is higher than that of the model without the

effect

H4 Size of the firm The boundary between bankrupt and non-bankrupt firms is different for small

and larger firms which decreases the performance of the model estimation when estimated for

data from one size category and isare applied to for data from another size category For the

bankrupt and non-bankrupt firms in the original data for Zrsquorsquo-Score model estimation the range

of asset sizes was ranged between approximately 1 and 25 million US dollars The data did not

include very small or very large firms Altman (1983) regarded the suitability of the original Z-

Score model (and likewisein the same way the Zrsquorsquo-Score model) for all firms as debatable

because of this omission it did not scrutinize very large or very small firms In the currentis study

the benchmark Zrsquorsquo-Score LR -model will beis estimated for data from many size categories from

very small firms to very large firms The fourth hypothesis (H4) assumes that the classification

performance of the uniform benchmark LR -model based on the original four financial variables

20

of the Zrsquorsquo-Score model is improved when the size category of the firm is explicitly taken into

account Thus we present the fourth hypothesis as follows

H4 The modelrsquos prediction accuracy of the modelis higher whenith the effect of size is

includede is higher than that of the model without this effect

H5 Age of the firm International insolvency statistics generally show that bankruptcy risk is a

function of the age of the firm Very young firms typically show a very high risk The original Zrsquorsquo-

Score model does not explicitly take age into account age However Altman (1983) noted that

the age of a firm is implicitly considered in the Retained EarningsTotal Assets ratio (X2) which

was regarded as a new ratio in the bankruptcy prediction context A relatively young firm will

probably show a low ratio because it has not had time to build up cumulative profits Thus a

young firm is to some degree discriminated against in the model and its likelihood of being

classified as bankrupt is relatively higher than that of an older firm The incidence of failure is

much higher in the early years of a firm AlthoughDespite that the age of the firm in this way is

implicitly taken into account in X2 we expect that an explicit consideration of the age will improve

the classification accuracy bydue to controlling for the age factor Therefore the fifth hypothesis

(H5) proposes that the performance of the uniform benchmark model based on the original four

financial variables of the Zrsquorsquo-Score model is increasesd when the age of the firm is explicitly taken

into account The fifth hypothesis is can be presented as follows

H5 The modelrsquos prediction accuracy of the model is higher when with the effect of firm

age is higher than that of the model without this effectincluded

21

H6 Industry of the firm The original Zrsquo-Score model was estimated only for manufacturing firms

Altman (1983) stated that it would be ideal to develop a bankruptcy prediction model utilizing a

homogenous group of bankrupt firms If we are interested in a particular industry grouping we

should gather data from bankrupt and non-bankrupt firms in these groupings Previous studies

show that financial distress analysis is influenced by the industry effect (Smith and Liou 2007)

Firms in different industries tend to report different levels of the same financial ratios which may

have an effect on the boundary between bankrupt and non-bankrupt firms This industry effect

may be present in the Zrsquo-Score model especially due to the SalesTotal Assets ratio (X5) This ratio

which showed the least significance on a univariate basis while making but it had made a very

significant contribution to the discriminant power of the multivariate model Altman (1983)

recognized the potential industry effect due to a the wide variation among industries in asset

turnover and specified the Zrsquorsquo-Score model without X5 However also the Zrsquorsquo-Score model was

also estimated using the original sample of manufacturing firms In our analysis the uniform

benchmark model based on the original four financial variables of the Zrsquorsquo-Score model is

estimated for a statistical sample representing different industries Therefore the sixth

hypothesis (H6) assumes that an explicit consideration of industry will improve the classification

accuracy of this benchmark model H6It can be expressed in the following form

H6 The modelrsquos prediction accuracy of the modelis higher whenith the effect of industry is

higher than that of the model without this effectincluded

H7 Country of origin The original Zrsquorsquo-Score model has been estimated only for USUS firms It

can be expected that the international applicability of the model to other countries is affected by

22

country-specific differences The eEconomic environment legislation culture financial markets

and accounting practices in a country may affect the financial behavior of firms and the boundary

between bankrupt and non-bankrupt firms These factors may potentially weaken the

classification performance of the model in for other countries outside other than that e one fore

country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the

seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm

into account will improve the classification accuracy of the benchmark model In our empirical

study the country effect will beis assessed by including a variable of for country risk Therefore

the seventh hypothesis is presented as follows

H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country

risk is higher than that of the model without this effectincluded

5 Empirical data and statistical methods

51 Sample of firms

The principal data of for this study are were extracted from the ORBIS databases of Bureau Van

Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the

moment of sampling contained administrative information on more than 50 million European

firms However income statement and balance sheet information was available for

approximately 8 million companies More than 99 of the companies covered in this database

are private companies from different various industries justifying the use of the Zrsquorsquo-Score model

instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all

industrial groupings and for both private and public entities (Altman 1983 124 Altman and

Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope

23

of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all

industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly

used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that

international comparability may be a problem when administrative firm-level data are

internationally pooled For administrative data Although the definition of variables is usually less

harmonized for administrative data tThis is less of a problem in the ORBIS database because of

the common international format of balance sheets For example although some discrepancies

in profitloss statements may arise because of differences in fiscal systems across countries

balance sheet variables largely adhere to international standards

AThere are a number of factors that influence the international applicability of bankruptcy

prediction models These factors include accounting legislation and practice creditor rights and

investor protection judicial efficiency corporate governance bankruptcy protection and

insolvency management and firm risk-taking These factors strongly differ between European

and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-

Score model outside Europe First it is especiallyparticularly important to include the USUS as

because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It

is include d in our study as well because it has the largest market capitalization in the world

Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it

applicable to emerging market companies Therefore we include firms from China and Colombia

which represent two very culturally and institutionally very dissimilar emerging market

countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt

24

firms ie the limit we set for European countries) from ORBIS World were not

available Thus the results are also estimated and tested for three non-European countries (the

United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-

Score modelrsquos performance The samples of firms from these countries are were extracted from

ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms

from around the world

SFor statistical sampling several requirements are set for the statistical sampling of the empirical

data First we require that the firm to be selected be an industrial (non-financial) company

Second its owners must have limited liability (so partnerships and sole proprietors are left out

of the study) Third we set a minimum requirement for the size of the firm Because the financial

ratios of very small firms are generally too unstable for a failure prediction model these firms

are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have

exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we

include in our estimation sample firms from all European countries and 3 pre-selected non-

European countries where the number of failed firms is more greater than 60 If the number of

failed firms for any European country is less than 60 the firms from this country are only included

only in the test sample For qualifying European countries the firms are randomly classified in

the estimation and test samples so that the number of firms is approximately equal in both

samples Thus our estimation data include firms from 28 European and 3 non-European

countries Fifth all failed firms that fulfill the above requirements are included in our samples

However if the number of non-failed firms in a country is very high a sample is randomly

25

selected from that country Finally the time span of fiscal years potentially available for this study

ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in

the database are from a financial period within 2007 and 2010 earlier years are also excluded

for non-failed firms for comparability All qualifying observations of non-failed firms from the

years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses

of failed firms to the last most recent financial statements available before failure The four

independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize

outliers

Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test

data by country The estimation sample includes data from 2602563 non-failed and 38215

failed firms from 28 European and 3 non-European countries The test sample is slightly larger

because it includes data from 31 European and 3 non-European countries From For the country

of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt

firms The available US data that were available consist only of listed (and delisted) firms From

China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are

included in the estimation sample although there are only 32 such firms altogether7 The Chinese

datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status

are separately analyzed separately only in for the test data8 ST firms are listed firms suffering

6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor

26

from serious financial difficulties Excluding the special US data and the two non-private Chinese

datasets 994 of the observations in the data are private firms

(Table 1 here)

52 Status of failed firms

ORBIS has five classes for potentially active firms (active default of payment receivership

dormant and branch) and seven classes for inactive firms that no longer carry out business

activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation

branch and no precision) From Among these classes only active is selected to represent non-

distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by

considering (with exceptions described below) a firm failed if its status in ORBIS is stated as

bankruptcy However because of the small number of bankrupt firms in some countries we also

consider receivership (active) firms as failed even if they are active These firms generally suffer

from serious financial distress However firms in liquidation are generally not included in the

sample of failed firms Firms in liquidation may depending on the country contain firms that

have ceased activities due to reasons other than failure (mergers discontinuing the operations

of a daughter company or of a foreign branch etc) Therefore for most countries we select only

firms that are coded as being bankrupt or under receivership However there are a number of

special cases where failed firms are coded under a different status heading These countries or

samples are the following

Country Status categories Bulgaria In liquidation Bankruptcy

27

Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the

study (for example Switzerland) If a country had only a very small number of failed firms it was

dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries

including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status

classes (including the bankruptcy category) are not completely homogenous within European

countries due to different legislations although there are obvious similarities in insolvency acts

(Philippe et al 2002) China is a special case because it includes samples with three different

criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are

two different samples (liquidation and receivership)

53 Statistical methods

In this study seven research hypotheses are drawn for statistical testing The statistical analysis

will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The

classification performance of the original model is assessed by the AUC (Area Under Curve)

measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close

connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random

28

model and 05 for a model with an average classification performance In SAS software is used

for all statistical analysess SAS software is used

The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is

tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical

method (multiple discriminant analysis or MDA) The problem is that the estimation sample

includes different numbers of failed and non-failed firms from 31 countries In the original Z-

Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from

the USUS Following the characteristics of these data we weight the firms so that the weights

for the failed and non-failed firms are equally In so doingthis way the non-proportional

sampling in from different countries does will not affect the re-estimated model The number of

firms from different countries however varies significantly leading to greater weights for larger

countries To avoid this problem the observations are also weighted so that each country has an

equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using

these weighted data and the resulting AUC is compared with that based on the original model

The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score

model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is

based on less- restrictive statistical assumptions than MDA In this estimation the dependent

variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA

does not require that independent variables be multivariate normal or that groups have equal

covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA

29

creates a score (logit) score L for every firm It is assumed that the independent variables are

linearly related to L This score or logit is used to determine the conditional probability of failure

as follows

= (eq 4)

where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the

original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing

the statistical significance of the difference between AUCs for this LR model and for the re-

estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it

is used as a benchmark for further statistical AUC comparisons because LR is applied as the

principal method in the testing of the remaining research hypotheses

The third hypothesis (H3) is associated with the performance effect of taking account of the

bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on

the following logit

(eq 5)

where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model

bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and

D1 = 1 when year = 2008 0 otherwise

D2 = 1 when year = 2009 0 otherwise

D3 = 1 when year = 2010 0 otherwise

)|1( XYp )( 441101

1

1

1XbXbbL ee

3

1

4

10

jjji

ii DcXbbL

30

The dummy variables do not directly refer to the bankruptcy year which that is not given in the

data basedatabase but rather to the last available year before bankruptcy For failed firms there

is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is

the base category If the AUC of this extended LR model statistically significantly exceeds the AUC

of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3

Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third

hypothesis above However for each hypothesis appropriate variables are used instead of the

year dummies Hypothesis H4 addresses the performance effect of taking size into account the

size and it will beis tested using by adding two additional variables measuring firm size in to the

LR -model In this LR model size is measured by the natural logarithm of total assets and its

squared form In this way the effect of logarithmic size can be reflected by in a function following

the second-order parabola Hypothesis H5 predicts that the classification performance improves

when the age of the firm is explicitly taken into account When testing this hypothesis the

category 6ndash14 years category is used as the base category and two dummy variables are

incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis

H6 looks at whether is associated with the classification performance is affected bythe effect of

the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt

is tested here using dummy variables for seven industries (D1 restaurants and hotels D2

construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and

water production D7 information technology) with all other industries acting as the base

category

31

Hypothesis H7 predicts that the explicit consideration of the country of origin will improves

classification performance This hypothesis is tested by using country risk measures instead ofnot

using dummy variables for countries but using country risk measures instead The country risk of

each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the

annual closing of accounts The rating is numerically recoded such that the best rating AAA

equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating

D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial

ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-

modelLR models with the original four financial ratios and the additional variables specified in

the hypotheses are estimated for all data In addition an LR model including all additional

variables is estimated for all data to assess the simultaneous effect of all variables Finally six of

the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not

included in this country- level testing hypothesis H7 is not tested because the additional variable

(country risk) is constant within the country

6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models

Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score

model for all data The variation in the ratios is significant as shown by the standard deviation

and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-

failed firms are close to each other indicating a symmetry of distributions However this is not

32

the case for the failed firms For the faileddistressed firms the median exceeds the mean for

these three ratios indicating negatively skewed distributions For X4 (BVETD) the means

significantly exceed the median for both failed and non-failed firms indicating a positively

skewed distribution For each of the four variables both the mean and the median are higher for

non-failed firms than for failed firms which is consistent with the expectations The difference

between the means of the non-failed and failed firms is larger in the original USUS data than in

our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is

approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of

EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may

indicate lower classification accuracy than in the original sample

(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates

(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the

large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model

Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same

statistical method or MDA The coefficients here are negative because the models are estimated

using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight

than in the original USUS data while the weights of WCTA and BVETD have proportionally

decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the

logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR

model These coefficients are directly comparable with those of the MDA model which isas

expected for the this exceptionally large sample The differences in the coefficients of the original

33

four variables among the eight LR-modelLR models (Models 1-8) are small For each model the

coefficient of BVETD is very close to zero This indicatinges that the original four variables and

the additional variables are quite independent of each other

(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-

modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate

that after 2007 (the base category) this risk of failure significantly decreased year by year The

base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due

to as a result of the global financial crisis The crisis played a significant role in the failure of key

businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The

effects were especially felt in Europe The coefficients of Model 4 for the size variables show that

the contribution of size on to the logit (risk measure) is atreaches its maximum value when

logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model

5 confirms the riskiness of young firms because the risk of failure is very high for newly founded

firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The

coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky

industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is

statistically significant (because of the large sample) but negative and very close to zero Finally

the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7

62 All data performance of the Zrsquorsquo-Score models

34

Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1

refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of

all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average

accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)

Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its

performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The

lower part of the table shows the AUCs for the countries included only in the test data The

performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)

samples It is also low and for the liquidation firms in the United Kingdom

The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5

presents the differences of these medians between the non-failed and failed firms by country

This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of

medians This correlation which is high for each financial ratio showing that the effects of the

ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large

except for EBITTA which This implies with the exceptionally high AUC that the ST firms

systematically differ from the non-ST firms although the differences are not extremely large The

differences between the medians are very large in Poland for each ratio justifying the high AUC

They are also large and in Finland and in the Czech Republic where the difference in EBITTA is

average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in

all four ratios are below the average a result thatwhich is obviously associated with a low AUC

35

In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even

negative In Iceland and Ireland the differences only in EBITTA are exceptionally small

(Table 4 here)

(Table 5 here)

Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by

MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)

supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is

at approximately the average level The re-estimation of the coefficients has led to improved

classification accuracy in a number of countries especially in Bosnia China (ST) Norway and

Greece However it has impaired the classification accuracy in the UK and China (delisted)

Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-

modelLR model) showing the effect of the estimation method For the benchmark model the

AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between

among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR

model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country

This result was expected because the coefficients of the models are directly comparable The

similar results for the models may also indicate that the independent variables conform to

multinormality This last latter result confirms what most researchers in the field of default

classification models have concluded that the accuracy levels of MDA and logistical regression

36

models are extremely similar despite any statistical violations such as normality between MDA

and logistical regression models

Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)

than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects

are not positive for all countries The effects are positive for example for Russia Estonia

Germany Ireland and Latvia but these effects are not statistically significant There are

statistically significant negative effects for China (ST) and Serbia There are notable negative

effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are

due to the exceptional annual distributions of failed firms in these countries For the countries

with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the

countries with positive effects it is only a few percent For each group non-failed firms are quite

equally distributed over years When the coefficient of D3 is very low (-0666) it strongly

decreases the risk estimates of most failed firms in the former countries but of only a few failed

firms in the latter countries This which leads to the observed effects H3 is supported by

the level of the whole sample However in some country samples (at the level of individual

levelies) H3 is not supported

Model 4 (LR-modelLR model with size variables) performs better than the benchmark model

which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads

and to significant improvements in the AUC for eg China (delisted) and Austria For China

(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC

37

also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs

in a few countries but thise decrease is not significant Model 4 is based on the four original

variables and the size effect following a second-order parabola This kind of size effect is very

small for micro firms but increases when approaching its maximum value in middle-sized firms

The countries with a positive change in AUC typically have data where in which the percent of

failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized

failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In

contrast the countries with a negative change in AUC have data where in which the percentage

of failed micro firms is exceptionally high (70-80)

Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly

the same AUC as the benchmark model (AUC = 0748) However the difference is positive and

statistically significant giving at least marginal support to H5 (age effect) For nearly all countries

the effect of the age on AUC is small For Austria however thise effect is however positive and

significant For Austria the percentage of non-failed young firms (less than 6 years) among non-

failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed

firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong

positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only

a few non-failed firms which This obviously leads to an improvement in AUC

Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC

supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its

38

AUC is notably higher than the benchmark AUC only in only a few countries such asfor example

France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg

restaurants and hotels and the information technology industryies but it has a positive effect

for on the construction and manufacturing industries For the countries with a positive effect on

AUC the percent of non-failed firms in restaurants and hotels and information technology is

high while that of failed firms is low For the risky industries (construction and manufacturing)

these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many

failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)

firms Therefore the AUC will be increasesd This situation is reversed for the countries with a

negative effect on AUC (Austria and Slovenia) The samples of these countries include a high

percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC

Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher

classification performance (AUC = 0749) than the benchmark model This result gives only very

weak support for H7 (country of origin effect) However for each country the resultant AUC is

nearly identical to that given by the benchmark model This result was expected due to the

negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7

This result implies that country risk hasdoes not have noany effect on the boundary between

bankrupt and non-bankrupt firms

Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional

variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to

39

the benchmark AUC in all test data However the effect on the AUCs largely varies and is either

negative or positive in different countries The positive effect is large in several countries such

as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative

effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show

that the inclusion of additional variables in the original model will usually increase the AUC but

not in every country

The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with

the USUS firms being unlike the majority of other firms in this study being listed or delisted

companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese

firm samples is associated with very small differences between the medians of the non-failed and

failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable

with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed

firms is very high Prior studies based on Chinese ST -firms have also demonstrated good

predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for

additional research and modeling work regarding unlisted and delisted Chinese firms

63 Country-level data performance of the Zrsquorsquo-Score models

The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform

all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the

different models estimated for each country separately (country-level models) In this table the

ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated

40

from the country data this benchmark will beis clearly outperformed by the resulting MDA

(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and

Sweden) However these results give only weak support for H1 at the country level because the

effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and

2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs

given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed

by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation

method)

Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC

than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3

leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United

StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in

nearly every country supporting H4 This improvement is significant however only in the United

StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark

model but for which it is only 0710 for the benchmark model It This improvement is also notable

in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age

category dummies) gives higher AUCs than the benchmark model for several countries but none

of the improvements are significant The positive effect is particularly strong especially in

Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only

weakly supports H5 because these negative effects are relatively small

41

Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects

on AUCs when compared with the benchmark However the only significant effect is the negative

effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the

industry dummies However there are notable positive effects on the AUC in for example

France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak

support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in

AUC compared with the benchmark model in Colombia Iceland and the Unites States It also

leads and to notable improvements in AUC at least in Belgium Denmark Estonia France

Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are

found only in Austria and Slovenia Thus as a conclusion we can concludestate that the

classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can

remarkably increases in most countries when different effects are taken into account by

additional variables This increase is found in most European countries and in Colombia and the

United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-

modelLR model and it can only be slightly improved only slightly by additional variables

(Table 6 here)

7 Summary of the study and a suggested extension

The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model

originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is

assessed using a very large international data set We test how the original version of the Zrsquorsquo-

42

Score model performs in different countries and how re-estimations using another statistical

method and different additional variables affect the classification performance when the data

are very heterogeneous For this kind of testing seven research hypotheses on classification

performance are extractedformulated These research hypotheses are tested for all data and

separately for country data (country-level analysis) The estimation data are from 31 countries

and the results are validated for 34 countries The countries are mainly from Europe but three

non-European countries are included (China Colombia and the United StatesUS) The statuses

used in the classification are mainly bankruptcyactive but receivership firms are also considered

to have failed In the Chinese data ST (special treatment) and delisted firms are also separately

analyzed as failed firms

The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very

satisfactorily in an international context The effects of the four financial ratios on performance

are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small

contribution in re-estimation The original model performs very well in several countries such as

Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only

marginally improved the classification performance thus weakly supporting the obsolescence

hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust

across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion

holds for the re-estimation of the model using LRA because the performance results are very

similar with to those of MDA (H2) The use of additional variables in the model generally

improvesd the classification accuracy of the original model but the results for countries are

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

7

The paper is structured as follows In the introduction we present a brief summary of the

development of failure prediction research starting beginning with Beaver (1966) and Altman

(1968) In the second section we summarize the original Z-Score model (1968) and its extension

for private firms that is the Zrsquo-Score and Zrsquorsquo-Score models (1983) IThen in the third section we

present the results and conclusions from the literature review on these models The fourth

section presents seven research hypotheses on the performance of the Zrsquorsquo-Score model for

empirical analysis In the fifth section the empirical data and statistical methods are discussed

while the sixth section presents empirical findings Finally the seventh section summarizes the

study

2 Classic Z-Score models

21 Z-Score model for public firms

Altmanrsquos (1968) initial sample was composed of 66 corporations with 33 firms in each of the two

groups The bankrupt group (Group 1) consisted of manufacturers that filed a bankruptcy

petitions under Chapter X of the National Bankruptcy Act during the period 1946ndash1965 period

The mean asset size of these firms was 64 million USD with a ranginge of between 07 and 259

million USD Altman recognized that this group was not homogenous with respect to size and

industry although all firms were relatively small and from manufacturing industries Therefore

he attempted to make a carefully selection select of non-bankrupt firms (Group 2) Group 2

consisted of a paired sample of manufacturing firms chosen on a stratified random basis These

firms were stratified by industry and size with the asset size range restricted to 1ndash25 million USD

Altman eliminated both the small firms (less than 1 million US dollars in total assets) because of

8

a lack of data and the very large firms because of a lack of data because offor small firms and the

rarity of bankruptcies among these firms in that period of large firms He did not match the asset

size of the two groups exactly and therefore the firms in Group 2 are were slightly larger than

those in Group 1 The data collected for the firms in both groups were from the same years For

Group 1 the data were derived from financial statements one reporting period prior to

bankruptcy

Using financial statements Altman compiled a list of 22 potentially important financial ratios

based on financial statements for evaluation He classified these variables into five standard ratio

categories liquidity profitability leverage solvency and activity ratios These ratios were

chosen based on their popularity in the literature and their potential relevancey to the study The

final discriminant function estimated by Altman (1968) is as follows

Z = 0012X1 + 0014X2 + 0033X3 + 0006X4 + 0 999X5 (eq 1)

where X1 = Working CcapitalTotal Aassets X2 = Retained EarningsTotal Aassets X3 = Earnings before Iinterest and TtaxesTotal Aassets X4 = Market Vvalue of EequityBook Vvalue of Ttotal Lliabilities X5 = SalesTotal Aassets Z = Overall Index 22 Zrsquo-Score and Zrsquorsquo-Score models for private firms

The original Z-Score model was based on the market value of the firm and was thus applicable

only to publicly traded companies Altman (1983) emphasized that the Z-Score model is a

intended for publicly traded firms model and that ad hoc adjustments are not scientifically valid

9

Therefore Altman (1983) advocated a complete re-estimation of the model substituting the

book value of equity for the market value in X4 Using the same data Altman extracted the

following revised Zrsquo-Score model

Zrsquo = 0717X1 + 0847X2 + 3107X3 + 0420X4 + 0 998X5 (eq 2)

where X4 = Book value of equityBook value of total liabilities with the other variables being

the same as those in the original (1968) Z-Score model

DAltman ue to the lack of a private firm database Altman did not test the Zrsquo-Score model on a

secondary sample due to the lack of a private firm data base However he analyzed the accuracy

of a four-variable Zrsquorsquo-Score model that excludeding the SalesTotal assets ratio X5 from the

revised model because of a potential industry effect The industry effect that is more likely to

take place when this kind of industry-sensitive variable (asset turnover) is included in the model

Altman then estimated the following four-variable Zrsquorsquo-Score model (Altman 1983)

Zrsquorsquo = 325 + 656X1 + 326X2 + 672X3 + 105X4 (eq 3)

The EBITTotal assets ratio X3 made the highest contributioncontributed most to the

discrimination power in this version of the model The classification results for the Zrsquorsquo-Score

model were identical to the revised five-variable Zrsquo-Score model In the currentis study our

empirical analysis focuses on the performance of the Zrsquorsquo-Score model version in predicting

bankruptcy prediction wheredue to it has its widest scope that is because as it is intended for

both privately held and publicly listed firms and for both manufacturing and non-manufacturing

firms

10

3 Literature review

We searched for papers published from the year 2000 onward 4 in prominent international

journals or and books We selected 31 articles in which the Z-Score was either used either as a

failure prediction proxy or was assessed mostly in terms of predictive ability Of the 31 studies

Altmanrsquos Z-Score model5 was used in 16 cases as the measure of distress or of financial strength

In 13 studies Altmanrsquos original model was modified and (or) verified including re-estimation and

in 2 cases it was used solely for the robustness check As Pindado et al (2008) noted the Z-Score

was also used for other purposes such as the evaluation of the costs and benefits of covenants

in bonds the choice of the type of debt type (bank vs non-bank private or public) and the

relationship between investment and internal funds We focused on this part of the literature

that verified and (or) modified Altmanrsquos original model The wide broad usage of the Z-Score

model foras a measuringe of financial distress and performingas well as for the robustness checks

indicates that it is acceptabilityed as a reasonable simple and consistent measure of the

distressed firms

In the case of the modification of the The most common modification to the Z-Score model

model the most common change was the use of other estimation techniques or country-specific

data The use of Altmanrsquos ratios in combination with techniques other than MDA has improved

the its prediction capability TAdditionally the application of new data of on both US and non-US

4 Research devoted to the application of the Z-Score model before 2000 was reviewed by Grice and Ingram (2001) 5 Most of the models focused on stock- exchange- listed firms thus the Zrsquo-Score and Zrsquorsquo-Score models were not used

11

firms also improved model performance Kwak et al (2005) applied used Multiple Criteria Linear

Programming (MCLP) to model 5 Altman and 9 Ohlson variables They used with data on US

bankrupt US firms from 1992 to 1998 and nearly 6 times more matched US control firms The

MCLP approach performed better than Altmanrsquos original model and gave results similar to or

better than those of Ohlsonrsquos original model The original models were not recalculated and the

authors referred solely to their original prediction rates only Merkevicius et al (2006) using data

on US and Lithuanian firms developed a hybrid artificial -discriminantte model combining MDA

and an unsupervised learning artificial neural network (called the SOM-Altman model) with the

use of data on US and Lithuanian firms Thise hybrid SOM-Altman model reached a high

prediction rate of 9235 Xu and Zhang (2009) applied Altmanrsquos Z-score Ohlsonrsquos O-score and

Mertonrsquos distance-to-default (D-score) models to Japanese firms to check whether these models

are useful for bankruptcy prediction in Japan They also ldquomergedrdquo theose models into a new C-

score model They then introduced variables unique to Japan to check whether corporate

structure variables have any impact on the probability of bankruptcy t They called this the model

X-score model These two models were useful for Japanese firms in predicting bankruptcy

prediction but the market-based model was the most successful In summary the developed C-

score and X-score (with country-specific variables) models improved bankruptcy prediction

Tinoco and Wilson (2013) used the original Z-Score as one of the benchmarks to assess the

performance of their model developed for UK listed companies with combined accounting

market and macroeconomic data Altmanrsquos Z-Score presented very good classification accuracy

in the case of financially distressed firms (81 vs 87 for the new model) however it was less

correct for financial health prediction Another modification was introduced by Lyandres and

12

Zhdanov (2013) who posed the question of whether the inclusion of variables related to

investment opportunities improved the predictive power of three models (Altmanrsquos Z-score

model and Zmijewskirsquos and Shumwayrsquos models) They used three proxies for investment

opportunities (market-to-book value-to-book and RampD-to-assets) The measures of investment

opportunities were linked to the likelihood of default The inclusion of either of these measures

improved the out-of-sample forecasting ability of all three

The verification of Altmanrsquos model concentrated on its efficacy or on how it compares a

comparison with other accounting-based market-based or hazard models However although

the original Z-Score model was not solely an accounting-based approach because the market

value of equity was utilized we classify it here as an accounting -based approachbecause the

market value of equity was utilized Grice and Ingram (2001) used a new novel data set of US

firms and posed three questions about the efficacy of Altmanrsquos model They concludinged that

the prediction accuracy of Altmanrsquos model had declined over time and that the coefficients of

the model had significantly changed which means that the relation between the financial ratios

and the signs of financial distress had changed over time The model was sensitive to industry

classification (more efficient for manufacturing firms than for non-manufacturing firms) but was

not sensitive to the type of financial distress Similar conclusions were drawn by Grice and Dugan

(2003) regarding Ohlsonrsquos (1980) and Zmijewskirsquos (1984) models

Hillegeist et al (2004) compared Altmanrsquos Z-score and Ohlsonrsquos O-score (with original and

updated coefficients) with the model based on BSM option pricing (BSM-Prob) They used

13

relative information content tests to compare the out-of-sample performance BSM-Prob

outperformed accounting-based models The conclusions were robust to various modifications

of accounting-based models such as updated coefficients industry effects and the separation

of variables Chava and Jarrow (2004) employed an extended bankruptcy database of US listed

firms to prove the superiority of Shumwayrsquos model (2001) over Altmanrsquos (1968) and Zmijewskirsquos

(1984) models The authors re-estimated the models over the 1962ndash1990 period and forecasted

bankruptcies over the 1991ndash1999 period In the case of Shumwayrsquos model 744 (in the first

decile) of the bankruptcies were correctly identified for with Altmanrsquos model it was 632 and

for with Zmijewskirsquos model it was 432 Shumwayrsquos market-based model also outperformed

accounting-based models in terms of the ROC curve (091)

Reisz and Perlich (2007) used the barrier option for bankruptcy prediction and compared its

discriminatory power with other market-based models and Altmanrsquos Z-Score and Zrdquo-Score The

dataset covered nearly 6000 industrial firms over the period 1988ndash2002 period The authors

proved the domination superiority of Altmanrsquos Z-Score and Zrdquo-Score models for short-term (up

to 1 year) bankruptcy prediction For medium- and long-term bankruptcy prediction their barrier

option model outperformed the other models Pindado et al (2008) developed an ex -ante model

for the estimation of financial distress likelihood (FDL) using awith the use of panel data

methodology and they presented a financially (not legally) based definition of distress Their

sample covered 1583 US companies and 2250 companies from other G-7 countries for the

period 1990ndash2002 period They used a re-estimated Z-Score as a benchmark The FDL model

outperformed the Z-Score model in terms of stability and classification power for different

14

countries and time periods In the case of the re-estimated Z-Score model only profitability and

retained earnings kept maintained their significance for different years and countries

Wu et al (2010) evaluated the performance of five models (Altman 1968 Ohlson 1980

Zmijewski 1984 Shumway 2001 and Hillegeist et al 2004) using an up-to-date data set for

US listed firms Based on these models the authors built their own integrated model that is a

multi-period logit model with an expanded set of variables The integrated model which

combined accounting and market data as well as firmsrsquo characteristics outperformed the other

models Altmanrsquos Z-score performed poorly in compared withison to the other 4 models

Shumwayrsquos model performed best Hillegeist et alrsquos model performed adequately and Ohlsonrsquos

and Zmijewskirsquos models performed adequately although their performance deteriorated over

time

Jackson and Wood (2013) tested 13 different models of bankruptcy prediction and assessed their

efficacy using the ROC curve They selected three single- variable models three accounting-based

models (including Altmanrsquos Z-Score) in two versions (with up-dated coefficients and a neural

network approach) and four contingent claims models The contingent claims models and the

latter group outperformed the other models The best four best models were contingent claims

models based on European call and barrier options Although tThe predictive performance

improved withIn the case of the application of the neural network to accounting-based models

the predictive ability improved but it was still lower than within the case of the market-based

models

15

Acosta-Gonzaacutelez and Fernaacutendez-Rodriacuteguez (2014) used genetic algorithms with the Schwarz

information criterion (GASIC) for variable selection combined with the logit model for bankruptcy

prediction Altmanrsquos Z-Score model was used as one of two benchmarks for the authorsrsquo model

evaluation For one- step- ahead forecasting Altmanrsquos model was better in at predicting failed

firms but the type II error was high For two- and three- steps- ahead forecasts the performance

of the models was similar forin terms of failed firms but forin the case of non-failed firms and

the prediction accuracy of Altmanrsquos model was worse For four- steps- ahead forecasts the GASIC

model outperformed the other models forin the case of failed firms and but it performedwas

comparablye forin the case of non-failed firms

In general Altmanrsquos Z-Score model underperformed in compared withison to market-based

models but there was also evidence indicated that it performed well for short-term distress

prediction The question of whether market-based models are perform better than accounting-

based models has been raised many times (eg discussion in Das et al 2009 Bauer and d

Agarwal 2014) Our purpose is not to contribute to this strand of research but rather to focus on

the accounting-based approach In this study we analyze primarily analyze privately held firms

so there are by definition no market data for these firms In this case aAn accounting-based

approach is the only solution that could be appliedapplicable in this case by banks as lenders or

by investors holding debt securities of firms not listed on the stock exchange Thus far most of

the studies have concentrated on the US market only a few of them have used data from other

16

countries such as Japan the UK Lithuania or the G-7 countries Our analysis is a significant

extension of the previous research

4 Methodology and research hypotheses

The literature survey shows that the Z-Score model (publicly traded firms) the Zrsquo-Score model

(private manufacturing firms) and the Zrsquorsquo-Score model (private and publicly

traded manufacturing and non-manufacturing firms) have been adapted for different purposes

In this study we are first interested first in assessing the performance of the original Zrsquorsquo-Score

model in classifying bankrupt and non-bankrupt firms in an international context with special

focus on the European market However we also validate the results in a set of non-European

countries to generalize the results for circumstances outside Europe Second we will re-estimate

the model using extensive international data and then use the re-estimated Zrsquorsquo-Score model as a

benchmark for assessing the effects of different factors on the modelrsquos performance in terms of

classification accuracy We will assess the effects of the following five factors on this

performance year of bankruptcy size of firms age of firms industry and country of origin WIn

all we will test a set of hypotheses based on the effects of the model on performance on two

different levels First we will test a set of hypotheses on a pooled set of all firms and second on

data from each country individually CIn compared withison to the previous research our this

studyrsquos contribution is the focus on an international context not just simply the model

application or re-estimation for of a given countryrsquos data Because weour aim is focusing on the

performance of the Zrsquorsquo-Score mModel when using ain large body of international data the

research hypotheses are of a technical nature and are presented given as follows

17

H1 Obsolescence of the coefficients The Zrsquorsquo-Score mModel was originally estimated using the

same sample of firms that was used to develop the original Z-Score mModel The bankruptcies

in the estimation data occurred during the period 1946ndash-1965 period Thus the oldest

observations are from nearly seventy years ago during the post-war period Altman (1983)

recommended utilizing data as near to the present as possible when developing a bankruptcy

prediction model It is obvious that firmsrsquo the financial behavior of firms and their business

environment have significantly changed since then Thereforen potentially making the

importance of the financial ratios as reflected by the coefficients of the model may differ from

their original importance reflected by the coefficients of the model Therefore we suggest as

the first hypothesis (H1) that the re-estimation of the coefficients of the four original variables

of the Zrsquorsquo-Score model will improve the classification performance of the model in an

international context This hypothesis is supported by the previous research (eg Grice and

Ingram 2001) and by practice H1 aims to support this evidence on the international level and is

expressed as follows

H1 RThe re-estimatingon of the coefficients of the Zrsquorsquo-Score model improves the its

classification accuracy of the model

H2 Method of estimation The original Zrsquorsquo-Score model has been estimated using MDA However

MDA is based on the ordinary least squares (OLS) method and thus requires assumptions of

multinormality homoscedasticity and linearity which are not often met in empirical financial

ratio analysis Therefore we re-estimate the Zrsquorsquo-Score model using logistic regression analysis

(LRA) to assess the effect of the method of estimation method LRA does not require most of the

18

restricting assumptions of MDA In LRA the multivariate normality of the independent variables

is not required nor are homoscedasticity and linearity For the sake of OLS MDA can be more

useful than LRA for small samples such as the original sample of 66 firms used in the estimation

of the Zrsquorsquo-Score mModel However in a large sample LRA may potentially perform better In this

study we use large samples which is advantageous for LRA Therefore our second hypothesis

(H2) suggests is that the classification performance of the re-estimated Zrsquorsquo-Score model will be

improved when it is estimated using LRA instead of MDA The model re-estimated for the original

variables using LRA and all pooled data is called here the Zrsquorsquo-Score LR-modelLR model The

performance of this re-estimated model is used in this study as the benchmark for further

analyses Thus the second hypothesis is as follows

H2 The prediction accuracy of the logistic regression version of the Zrsquorsquo-Score model is higher

than that of the multiple discriminant analysis version

H3 Bankruptcy year The model based on the relationship between bankruptcy and financial

ratios is likely to be affected by the macroeconomic environment These effects may significantly

decrease the classification accuracy of the model If the model is estimated using data from one

year and it will be applied to data from another year the validity of the model can be questioned

Business cycles inIn terms of economic growth credit policy and interest rates business cycles

can ahave an effect on the boundary between bankrupt and non-bankrupt firms The original Zrsquorsquo-

Score model is estimated using data from the period 1946ndash1965 period which includes several

business cycles Therefore the model is not focused on any specific stage of a cycle and does not

explicitly take into account of the bankruptcy year Altman (1983) suggested gathering data from

19

firms for the last most recent few years when developing a prediction model In this study the

benchmark Zrsquorsquo-Score LR -model will beis estimated for a shorter period than in the original

estimation although it covers several recent years on for different stages of the business cycle in

different countries Therefore the third hypothesis (H3) assumes is that the classification

accuracy of the benchmark model can be increased by explicitly taking into account of the year

of bankruptcy in the estimation of the model estimation The third hypothesis is presented as

follows

H3 The prediction accuracy of the modelrsquos prediction accuracy is higher whenith the

effect of the year of bankruptcy is included is higher than that of the model without the

effect

H4 Size of the firm The boundary between bankrupt and non-bankrupt firms is different for small

and larger firms which decreases the performance of the model estimation when estimated for

data from one size category and isare applied to for data from another size category For the

bankrupt and non-bankrupt firms in the original data for Zrsquorsquo-Score model estimation the range

of asset sizes was ranged between approximately 1 and 25 million US dollars The data did not

include very small or very large firms Altman (1983) regarded the suitability of the original Z-

Score model (and likewisein the same way the Zrsquorsquo-Score model) for all firms as debatable

because of this omission it did not scrutinize very large or very small firms In the currentis study

the benchmark Zrsquorsquo-Score LR -model will beis estimated for data from many size categories from

very small firms to very large firms The fourth hypothesis (H4) assumes that the classification

performance of the uniform benchmark LR -model based on the original four financial variables

20

of the Zrsquorsquo-Score model is improved when the size category of the firm is explicitly taken into

account Thus we present the fourth hypothesis as follows

H4 The modelrsquos prediction accuracy of the modelis higher whenith the effect of size is

includede is higher than that of the model without this effect

H5 Age of the firm International insolvency statistics generally show that bankruptcy risk is a

function of the age of the firm Very young firms typically show a very high risk The original Zrsquorsquo-

Score model does not explicitly take age into account age However Altman (1983) noted that

the age of a firm is implicitly considered in the Retained EarningsTotal Assets ratio (X2) which

was regarded as a new ratio in the bankruptcy prediction context A relatively young firm will

probably show a low ratio because it has not had time to build up cumulative profits Thus a

young firm is to some degree discriminated against in the model and its likelihood of being

classified as bankrupt is relatively higher than that of an older firm The incidence of failure is

much higher in the early years of a firm AlthoughDespite that the age of the firm in this way is

implicitly taken into account in X2 we expect that an explicit consideration of the age will improve

the classification accuracy bydue to controlling for the age factor Therefore the fifth hypothesis

(H5) proposes that the performance of the uniform benchmark model based on the original four

financial variables of the Zrsquorsquo-Score model is increasesd when the age of the firm is explicitly taken

into account The fifth hypothesis is can be presented as follows

H5 The modelrsquos prediction accuracy of the model is higher when with the effect of firm

age is higher than that of the model without this effectincluded

21

H6 Industry of the firm The original Zrsquo-Score model was estimated only for manufacturing firms

Altman (1983) stated that it would be ideal to develop a bankruptcy prediction model utilizing a

homogenous group of bankrupt firms If we are interested in a particular industry grouping we

should gather data from bankrupt and non-bankrupt firms in these groupings Previous studies

show that financial distress analysis is influenced by the industry effect (Smith and Liou 2007)

Firms in different industries tend to report different levels of the same financial ratios which may

have an effect on the boundary between bankrupt and non-bankrupt firms This industry effect

may be present in the Zrsquo-Score model especially due to the SalesTotal Assets ratio (X5) This ratio

which showed the least significance on a univariate basis while making but it had made a very

significant contribution to the discriminant power of the multivariate model Altman (1983)

recognized the potential industry effect due to a the wide variation among industries in asset

turnover and specified the Zrsquorsquo-Score model without X5 However also the Zrsquorsquo-Score model was

also estimated using the original sample of manufacturing firms In our analysis the uniform

benchmark model based on the original four financial variables of the Zrsquorsquo-Score model is

estimated for a statistical sample representing different industries Therefore the sixth

hypothesis (H6) assumes that an explicit consideration of industry will improve the classification

accuracy of this benchmark model H6It can be expressed in the following form

H6 The modelrsquos prediction accuracy of the modelis higher whenith the effect of industry is

higher than that of the model without this effectincluded

H7 Country of origin The original Zrsquorsquo-Score model has been estimated only for USUS firms It

can be expected that the international applicability of the model to other countries is affected by

22

country-specific differences The eEconomic environment legislation culture financial markets

and accounting practices in a country may affect the financial behavior of firms and the boundary

between bankrupt and non-bankrupt firms These factors may potentially weaken the

classification performance of the model in for other countries outside other than that e one fore

country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the

seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm

into account will improve the classification accuracy of the benchmark model In our empirical

study the country effect will beis assessed by including a variable of for country risk Therefore

the seventh hypothesis is presented as follows

H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country

risk is higher than that of the model without this effectincluded

5 Empirical data and statistical methods

51 Sample of firms

The principal data of for this study are were extracted from the ORBIS databases of Bureau Van

Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the

moment of sampling contained administrative information on more than 50 million European

firms However income statement and balance sheet information was available for

approximately 8 million companies More than 99 of the companies covered in this database

are private companies from different various industries justifying the use of the Zrsquorsquo-Score model

instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all

industrial groupings and for both private and public entities (Altman 1983 124 Altman and

Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope

23

of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all

industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly

used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that

international comparability may be a problem when administrative firm-level data are

internationally pooled For administrative data Although the definition of variables is usually less

harmonized for administrative data tThis is less of a problem in the ORBIS database because of

the common international format of balance sheets For example although some discrepancies

in profitloss statements may arise because of differences in fiscal systems across countries

balance sheet variables largely adhere to international standards

AThere are a number of factors that influence the international applicability of bankruptcy

prediction models These factors include accounting legislation and practice creditor rights and

investor protection judicial efficiency corporate governance bankruptcy protection and

insolvency management and firm risk-taking These factors strongly differ between European

and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-

Score model outside Europe First it is especiallyparticularly important to include the USUS as

because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It

is include d in our study as well because it has the largest market capitalization in the world

Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it

applicable to emerging market companies Therefore we include firms from China and Colombia

which represent two very culturally and institutionally very dissimilar emerging market

countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt

24

firms ie the limit we set for European countries) from ORBIS World were not

available Thus the results are also estimated and tested for three non-European countries (the

United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-

Score modelrsquos performance The samples of firms from these countries are were extracted from

ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms

from around the world

SFor statistical sampling several requirements are set for the statistical sampling of the empirical

data First we require that the firm to be selected be an industrial (non-financial) company

Second its owners must have limited liability (so partnerships and sole proprietors are left out

of the study) Third we set a minimum requirement for the size of the firm Because the financial

ratios of very small firms are generally too unstable for a failure prediction model these firms

are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have

exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we

include in our estimation sample firms from all European countries and 3 pre-selected non-

European countries where the number of failed firms is more greater than 60 If the number of

failed firms for any European country is less than 60 the firms from this country are only included

only in the test sample For qualifying European countries the firms are randomly classified in

the estimation and test samples so that the number of firms is approximately equal in both

samples Thus our estimation data include firms from 28 European and 3 non-European

countries Fifth all failed firms that fulfill the above requirements are included in our samples

However if the number of non-failed firms in a country is very high a sample is randomly

25

selected from that country Finally the time span of fiscal years potentially available for this study

ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in

the database are from a financial period within 2007 and 2010 earlier years are also excluded

for non-failed firms for comparability All qualifying observations of non-failed firms from the

years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses

of failed firms to the last most recent financial statements available before failure The four

independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize

outliers

Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test

data by country The estimation sample includes data from 2602563 non-failed and 38215

failed firms from 28 European and 3 non-European countries The test sample is slightly larger

because it includes data from 31 European and 3 non-European countries From For the country

of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt

firms The available US data that were available consist only of listed (and delisted) firms From

China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are

included in the estimation sample although there are only 32 such firms altogether7 The Chinese

datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status

are separately analyzed separately only in for the test data8 ST firms are listed firms suffering

6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor

26

from serious financial difficulties Excluding the special US data and the two non-private Chinese

datasets 994 of the observations in the data are private firms

(Table 1 here)

52 Status of failed firms

ORBIS has five classes for potentially active firms (active default of payment receivership

dormant and branch) and seven classes for inactive firms that no longer carry out business

activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation

branch and no precision) From Among these classes only active is selected to represent non-

distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by

considering (with exceptions described below) a firm failed if its status in ORBIS is stated as

bankruptcy However because of the small number of bankrupt firms in some countries we also

consider receivership (active) firms as failed even if they are active These firms generally suffer

from serious financial distress However firms in liquidation are generally not included in the

sample of failed firms Firms in liquidation may depending on the country contain firms that

have ceased activities due to reasons other than failure (mergers discontinuing the operations

of a daughter company or of a foreign branch etc) Therefore for most countries we select only

firms that are coded as being bankrupt or under receivership However there are a number of

special cases where failed firms are coded under a different status heading These countries or

samples are the following

Country Status categories Bulgaria In liquidation Bankruptcy

27

Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the

study (for example Switzerland) If a country had only a very small number of failed firms it was

dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries

including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status

classes (including the bankruptcy category) are not completely homogenous within European

countries due to different legislations although there are obvious similarities in insolvency acts

(Philippe et al 2002) China is a special case because it includes samples with three different

criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are

two different samples (liquidation and receivership)

53 Statistical methods

In this study seven research hypotheses are drawn for statistical testing The statistical analysis

will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The

classification performance of the original model is assessed by the AUC (Area Under Curve)

measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close

connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random

28

model and 05 for a model with an average classification performance In SAS software is used

for all statistical analysess SAS software is used

The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is

tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical

method (multiple discriminant analysis or MDA) The problem is that the estimation sample

includes different numbers of failed and non-failed firms from 31 countries In the original Z-

Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from

the USUS Following the characteristics of these data we weight the firms so that the weights

for the failed and non-failed firms are equally In so doingthis way the non-proportional

sampling in from different countries does will not affect the re-estimated model The number of

firms from different countries however varies significantly leading to greater weights for larger

countries To avoid this problem the observations are also weighted so that each country has an

equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using

these weighted data and the resulting AUC is compared with that based on the original model

The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score

model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is

based on less- restrictive statistical assumptions than MDA In this estimation the dependent

variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA

does not require that independent variables be multivariate normal or that groups have equal

covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA

29

creates a score (logit) score L for every firm It is assumed that the independent variables are

linearly related to L This score or logit is used to determine the conditional probability of failure

as follows

= (eq 4)

where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the

original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing

the statistical significance of the difference between AUCs for this LR model and for the re-

estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it

is used as a benchmark for further statistical AUC comparisons because LR is applied as the

principal method in the testing of the remaining research hypotheses

The third hypothesis (H3) is associated with the performance effect of taking account of the

bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on

the following logit

(eq 5)

where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model

bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and

D1 = 1 when year = 2008 0 otherwise

D2 = 1 when year = 2009 0 otherwise

D3 = 1 when year = 2010 0 otherwise

)|1( XYp )( 441101

1

1

1XbXbbL ee

3

1

4

10

jjji

ii DcXbbL

30

The dummy variables do not directly refer to the bankruptcy year which that is not given in the

data basedatabase but rather to the last available year before bankruptcy For failed firms there

is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is

the base category If the AUC of this extended LR model statistically significantly exceeds the AUC

of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3

Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third

hypothesis above However for each hypothesis appropriate variables are used instead of the

year dummies Hypothesis H4 addresses the performance effect of taking size into account the

size and it will beis tested using by adding two additional variables measuring firm size in to the

LR -model In this LR model size is measured by the natural logarithm of total assets and its

squared form In this way the effect of logarithmic size can be reflected by in a function following

the second-order parabola Hypothesis H5 predicts that the classification performance improves

when the age of the firm is explicitly taken into account When testing this hypothesis the

category 6ndash14 years category is used as the base category and two dummy variables are

incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis

H6 looks at whether is associated with the classification performance is affected bythe effect of

the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt

is tested here using dummy variables for seven industries (D1 restaurants and hotels D2

construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and

water production D7 information technology) with all other industries acting as the base

category

31

Hypothesis H7 predicts that the explicit consideration of the country of origin will improves

classification performance This hypothesis is tested by using country risk measures instead ofnot

using dummy variables for countries but using country risk measures instead The country risk of

each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the

annual closing of accounts The rating is numerically recoded such that the best rating AAA

equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating

D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial

ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-

modelLR models with the original four financial ratios and the additional variables specified in

the hypotheses are estimated for all data In addition an LR model including all additional

variables is estimated for all data to assess the simultaneous effect of all variables Finally six of

the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not

included in this country- level testing hypothesis H7 is not tested because the additional variable

(country risk) is constant within the country

6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models

Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score

model for all data The variation in the ratios is significant as shown by the standard deviation

and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-

failed firms are close to each other indicating a symmetry of distributions However this is not

32

the case for the failed firms For the faileddistressed firms the median exceeds the mean for

these three ratios indicating negatively skewed distributions For X4 (BVETD) the means

significantly exceed the median for both failed and non-failed firms indicating a positively

skewed distribution For each of the four variables both the mean and the median are higher for

non-failed firms than for failed firms which is consistent with the expectations The difference

between the means of the non-failed and failed firms is larger in the original USUS data than in

our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is

approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of

EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may

indicate lower classification accuracy than in the original sample

(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates

(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the

large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model

Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same

statistical method or MDA The coefficients here are negative because the models are estimated

using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight

than in the original USUS data while the weights of WCTA and BVETD have proportionally

decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the

logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR

model These coefficients are directly comparable with those of the MDA model which isas

expected for the this exceptionally large sample The differences in the coefficients of the original

33

four variables among the eight LR-modelLR models (Models 1-8) are small For each model the

coefficient of BVETD is very close to zero This indicatinges that the original four variables and

the additional variables are quite independent of each other

(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-

modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate

that after 2007 (the base category) this risk of failure significantly decreased year by year The

base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due

to as a result of the global financial crisis The crisis played a significant role in the failure of key

businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The

effects were especially felt in Europe The coefficients of Model 4 for the size variables show that

the contribution of size on to the logit (risk measure) is atreaches its maximum value when

logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model

5 confirms the riskiness of young firms because the risk of failure is very high for newly founded

firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The

coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky

industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is

statistically significant (because of the large sample) but negative and very close to zero Finally

the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7

62 All data performance of the Zrsquorsquo-Score models

34

Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1

refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of

all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average

accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)

Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its

performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The

lower part of the table shows the AUCs for the countries included only in the test data The

performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)

samples It is also low and for the liquidation firms in the United Kingdom

The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5

presents the differences of these medians between the non-failed and failed firms by country

This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of

medians This correlation which is high for each financial ratio showing that the effects of the

ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large

except for EBITTA which This implies with the exceptionally high AUC that the ST firms

systematically differ from the non-ST firms although the differences are not extremely large The

differences between the medians are very large in Poland for each ratio justifying the high AUC

They are also large and in Finland and in the Czech Republic where the difference in EBITTA is

average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in

all four ratios are below the average a result thatwhich is obviously associated with a low AUC

35

In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even

negative In Iceland and Ireland the differences only in EBITTA are exceptionally small

(Table 4 here)

(Table 5 here)

Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by

MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)

supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is

at approximately the average level The re-estimation of the coefficients has led to improved

classification accuracy in a number of countries especially in Bosnia China (ST) Norway and

Greece However it has impaired the classification accuracy in the UK and China (delisted)

Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-

modelLR model) showing the effect of the estimation method For the benchmark model the

AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between

among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR

model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country

This result was expected because the coefficients of the models are directly comparable The

similar results for the models may also indicate that the independent variables conform to

multinormality This last latter result confirms what most researchers in the field of default

classification models have concluded that the accuracy levels of MDA and logistical regression

36

models are extremely similar despite any statistical violations such as normality between MDA

and logistical regression models

Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)

than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects

are not positive for all countries The effects are positive for example for Russia Estonia

Germany Ireland and Latvia but these effects are not statistically significant There are

statistically significant negative effects for China (ST) and Serbia There are notable negative

effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are

due to the exceptional annual distributions of failed firms in these countries For the countries

with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the

countries with positive effects it is only a few percent For each group non-failed firms are quite

equally distributed over years When the coefficient of D3 is very low (-0666) it strongly

decreases the risk estimates of most failed firms in the former countries but of only a few failed

firms in the latter countries This which leads to the observed effects H3 is supported by

the level of the whole sample However in some country samples (at the level of individual

levelies) H3 is not supported

Model 4 (LR-modelLR model with size variables) performs better than the benchmark model

which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads

and to significant improvements in the AUC for eg China (delisted) and Austria For China

(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC

37

also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs

in a few countries but thise decrease is not significant Model 4 is based on the four original

variables and the size effect following a second-order parabola This kind of size effect is very

small for micro firms but increases when approaching its maximum value in middle-sized firms

The countries with a positive change in AUC typically have data where in which the percent of

failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized

failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In

contrast the countries with a negative change in AUC have data where in which the percentage

of failed micro firms is exceptionally high (70-80)

Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly

the same AUC as the benchmark model (AUC = 0748) However the difference is positive and

statistically significant giving at least marginal support to H5 (age effect) For nearly all countries

the effect of the age on AUC is small For Austria however thise effect is however positive and

significant For Austria the percentage of non-failed young firms (less than 6 years) among non-

failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed

firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong

positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only

a few non-failed firms which This obviously leads to an improvement in AUC

Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC

supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its

38

AUC is notably higher than the benchmark AUC only in only a few countries such asfor example

France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg

restaurants and hotels and the information technology industryies but it has a positive effect

for on the construction and manufacturing industries For the countries with a positive effect on

AUC the percent of non-failed firms in restaurants and hotels and information technology is

high while that of failed firms is low For the risky industries (construction and manufacturing)

these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many

failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)

firms Therefore the AUC will be increasesd This situation is reversed for the countries with a

negative effect on AUC (Austria and Slovenia) The samples of these countries include a high

percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC

Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher

classification performance (AUC = 0749) than the benchmark model This result gives only very

weak support for H7 (country of origin effect) However for each country the resultant AUC is

nearly identical to that given by the benchmark model This result was expected due to the

negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7

This result implies that country risk hasdoes not have noany effect on the boundary between

bankrupt and non-bankrupt firms

Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional

variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to

39

the benchmark AUC in all test data However the effect on the AUCs largely varies and is either

negative or positive in different countries The positive effect is large in several countries such

as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative

effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show

that the inclusion of additional variables in the original model will usually increase the AUC but

not in every country

The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with

the USUS firms being unlike the majority of other firms in this study being listed or delisted

companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese

firm samples is associated with very small differences between the medians of the non-failed and

failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable

with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed

firms is very high Prior studies based on Chinese ST -firms have also demonstrated good

predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for

additional research and modeling work regarding unlisted and delisted Chinese firms

63 Country-level data performance of the Zrsquorsquo-Score models

The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform

all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the

different models estimated for each country separately (country-level models) In this table the

ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated

40

from the country data this benchmark will beis clearly outperformed by the resulting MDA

(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and

Sweden) However these results give only weak support for H1 at the country level because the

effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and

2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs

given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed

by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation

method)

Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC

than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3

leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United

StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in

nearly every country supporting H4 This improvement is significant however only in the United

StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark

model but for which it is only 0710 for the benchmark model It This improvement is also notable

in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age

category dummies) gives higher AUCs than the benchmark model for several countries but none

of the improvements are significant The positive effect is particularly strong especially in

Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only

weakly supports H5 because these negative effects are relatively small

41

Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects

on AUCs when compared with the benchmark However the only significant effect is the negative

effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the

industry dummies However there are notable positive effects on the AUC in for example

France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak

support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in

AUC compared with the benchmark model in Colombia Iceland and the Unites States It also

leads and to notable improvements in AUC at least in Belgium Denmark Estonia France

Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are

found only in Austria and Slovenia Thus as a conclusion we can concludestate that the

classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can

remarkably increases in most countries when different effects are taken into account by

additional variables This increase is found in most European countries and in Colombia and the

United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-

modelLR model and it can only be slightly improved only slightly by additional variables

(Table 6 here)

7 Summary of the study and a suggested extension

The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model

originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is

assessed using a very large international data set We test how the original version of the Zrsquorsquo-

42

Score model performs in different countries and how re-estimations using another statistical

method and different additional variables affect the classification performance when the data

are very heterogeneous For this kind of testing seven research hypotheses on classification

performance are extractedformulated These research hypotheses are tested for all data and

separately for country data (country-level analysis) The estimation data are from 31 countries

and the results are validated for 34 countries The countries are mainly from Europe but three

non-European countries are included (China Colombia and the United StatesUS) The statuses

used in the classification are mainly bankruptcyactive but receivership firms are also considered

to have failed In the Chinese data ST (special treatment) and delisted firms are also separately

analyzed as failed firms

The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very

satisfactorily in an international context The effects of the four financial ratios on performance

are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small

contribution in re-estimation The original model performs very well in several countries such as

Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only

marginally improved the classification performance thus weakly supporting the obsolescence

hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust

across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion

holds for the re-estimation of the model using LRA because the performance results are very

similar with to those of MDA (H2) The use of additional variables in the model generally

improvesd the classification accuracy of the original model but the results for countries are

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

8

a lack of data and the very large firms because of a lack of data because offor small firms and the

rarity of bankruptcies among these firms in that period of large firms He did not match the asset

size of the two groups exactly and therefore the firms in Group 2 are were slightly larger than

those in Group 1 The data collected for the firms in both groups were from the same years For

Group 1 the data were derived from financial statements one reporting period prior to

bankruptcy

Using financial statements Altman compiled a list of 22 potentially important financial ratios

based on financial statements for evaluation He classified these variables into five standard ratio

categories liquidity profitability leverage solvency and activity ratios These ratios were

chosen based on their popularity in the literature and their potential relevancey to the study The

final discriminant function estimated by Altman (1968) is as follows

Z = 0012X1 + 0014X2 + 0033X3 + 0006X4 + 0 999X5 (eq 1)

where X1 = Working CcapitalTotal Aassets X2 = Retained EarningsTotal Aassets X3 = Earnings before Iinterest and TtaxesTotal Aassets X4 = Market Vvalue of EequityBook Vvalue of Ttotal Lliabilities X5 = SalesTotal Aassets Z = Overall Index 22 Zrsquo-Score and Zrsquorsquo-Score models for private firms

The original Z-Score model was based on the market value of the firm and was thus applicable

only to publicly traded companies Altman (1983) emphasized that the Z-Score model is a

intended for publicly traded firms model and that ad hoc adjustments are not scientifically valid

9

Therefore Altman (1983) advocated a complete re-estimation of the model substituting the

book value of equity for the market value in X4 Using the same data Altman extracted the

following revised Zrsquo-Score model

Zrsquo = 0717X1 + 0847X2 + 3107X3 + 0420X4 + 0 998X5 (eq 2)

where X4 = Book value of equityBook value of total liabilities with the other variables being

the same as those in the original (1968) Z-Score model

DAltman ue to the lack of a private firm database Altman did not test the Zrsquo-Score model on a

secondary sample due to the lack of a private firm data base However he analyzed the accuracy

of a four-variable Zrsquorsquo-Score model that excludeding the SalesTotal assets ratio X5 from the

revised model because of a potential industry effect The industry effect that is more likely to

take place when this kind of industry-sensitive variable (asset turnover) is included in the model

Altman then estimated the following four-variable Zrsquorsquo-Score model (Altman 1983)

Zrsquorsquo = 325 + 656X1 + 326X2 + 672X3 + 105X4 (eq 3)

The EBITTotal assets ratio X3 made the highest contributioncontributed most to the

discrimination power in this version of the model The classification results for the Zrsquorsquo-Score

model were identical to the revised five-variable Zrsquo-Score model In the currentis study our

empirical analysis focuses on the performance of the Zrsquorsquo-Score model version in predicting

bankruptcy prediction wheredue to it has its widest scope that is because as it is intended for

both privately held and publicly listed firms and for both manufacturing and non-manufacturing

firms

10

3 Literature review

We searched for papers published from the year 2000 onward 4 in prominent international

journals or and books We selected 31 articles in which the Z-Score was either used either as a

failure prediction proxy or was assessed mostly in terms of predictive ability Of the 31 studies

Altmanrsquos Z-Score model5 was used in 16 cases as the measure of distress or of financial strength

In 13 studies Altmanrsquos original model was modified and (or) verified including re-estimation and

in 2 cases it was used solely for the robustness check As Pindado et al (2008) noted the Z-Score

was also used for other purposes such as the evaluation of the costs and benefits of covenants

in bonds the choice of the type of debt type (bank vs non-bank private or public) and the

relationship between investment and internal funds We focused on this part of the literature

that verified and (or) modified Altmanrsquos original model The wide broad usage of the Z-Score

model foras a measuringe of financial distress and performingas well as for the robustness checks

indicates that it is acceptabilityed as a reasonable simple and consistent measure of the

distressed firms

In the case of the modification of the The most common modification to the Z-Score model

model the most common change was the use of other estimation techniques or country-specific

data The use of Altmanrsquos ratios in combination with techniques other than MDA has improved

the its prediction capability TAdditionally the application of new data of on both US and non-US

4 Research devoted to the application of the Z-Score model before 2000 was reviewed by Grice and Ingram (2001) 5 Most of the models focused on stock- exchange- listed firms thus the Zrsquo-Score and Zrsquorsquo-Score models were not used

11

firms also improved model performance Kwak et al (2005) applied used Multiple Criteria Linear

Programming (MCLP) to model 5 Altman and 9 Ohlson variables They used with data on US

bankrupt US firms from 1992 to 1998 and nearly 6 times more matched US control firms The

MCLP approach performed better than Altmanrsquos original model and gave results similar to or

better than those of Ohlsonrsquos original model The original models were not recalculated and the

authors referred solely to their original prediction rates only Merkevicius et al (2006) using data

on US and Lithuanian firms developed a hybrid artificial -discriminantte model combining MDA

and an unsupervised learning artificial neural network (called the SOM-Altman model) with the

use of data on US and Lithuanian firms Thise hybrid SOM-Altman model reached a high

prediction rate of 9235 Xu and Zhang (2009) applied Altmanrsquos Z-score Ohlsonrsquos O-score and

Mertonrsquos distance-to-default (D-score) models to Japanese firms to check whether these models

are useful for bankruptcy prediction in Japan They also ldquomergedrdquo theose models into a new C-

score model They then introduced variables unique to Japan to check whether corporate

structure variables have any impact on the probability of bankruptcy t They called this the model

X-score model These two models were useful for Japanese firms in predicting bankruptcy

prediction but the market-based model was the most successful In summary the developed C-

score and X-score (with country-specific variables) models improved bankruptcy prediction

Tinoco and Wilson (2013) used the original Z-Score as one of the benchmarks to assess the

performance of their model developed for UK listed companies with combined accounting

market and macroeconomic data Altmanrsquos Z-Score presented very good classification accuracy

in the case of financially distressed firms (81 vs 87 for the new model) however it was less

correct for financial health prediction Another modification was introduced by Lyandres and

12

Zhdanov (2013) who posed the question of whether the inclusion of variables related to

investment opportunities improved the predictive power of three models (Altmanrsquos Z-score

model and Zmijewskirsquos and Shumwayrsquos models) They used three proxies for investment

opportunities (market-to-book value-to-book and RampD-to-assets) The measures of investment

opportunities were linked to the likelihood of default The inclusion of either of these measures

improved the out-of-sample forecasting ability of all three

The verification of Altmanrsquos model concentrated on its efficacy or on how it compares a

comparison with other accounting-based market-based or hazard models However although

the original Z-Score model was not solely an accounting-based approach because the market

value of equity was utilized we classify it here as an accounting -based approachbecause the

market value of equity was utilized Grice and Ingram (2001) used a new novel data set of US

firms and posed three questions about the efficacy of Altmanrsquos model They concludinged that

the prediction accuracy of Altmanrsquos model had declined over time and that the coefficients of

the model had significantly changed which means that the relation between the financial ratios

and the signs of financial distress had changed over time The model was sensitive to industry

classification (more efficient for manufacturing firms than for non-manufacturing firms) but was

not sensitive to the type of financial distress Similar conclusions were drawn by Grice and Dugan

(2003) regarding Ohlsonrsquos (1980) and Zmijewskirsquos (1984) models

Hillegeist et al (2004) compared Altmanrsquos Z-score and Ohlsonrsquos O-score (with original and

updated coefficients) with the model based on BSM option pricing (BSM-Prob) They used

13

relative information content tests to compare the out-of-sample performance BSM-Prob

outperformed accounting-based models The conclusions were robust to various modifications

of accounting-based models such as updated coefficients industry effects and the separation

of variables Chava and Jarrow (2004) employed an extended bankruptcy database of US listed

firms to prove the superiority of Shumwayrsquos model (2001) over Altmanrsquos (1968) and Zmijewskirsquos

(1984) models The authors re-estimated the models over the 1962ndash1990 period and forecasted

bankruptcies over the 1991ndash1999 period In the case of Shumwayrsquos model 744 (in the first

decile) of the bankruptcies were correctly identified for with Altmanrsquos model it was 632 and

for with Zmijewskirsquos model it was 432 Shumwayrsquos market-based model also outperformed

accounting-based models in terms of the ROC curve (091)

Reisz and Perlich (2007) used the barrier option for bankruptcy prediction and compared its

discriminatory power with other market-based models and Altmanrsquos Z-Score and Zrdquo-Score The

dataset covered nearly 6000 industrial firms over the period 1988ndash2002 period The authors

proved the domination superiority of Altmanrsquos Z-Score and Zrdquo-Score models for short-term (up

to 1 year) bankruptcy prediction For medium- and long-term bankruptcy prediction their barrier

option model outperformed the other models Pindado et al (2008) developed an ex -ante model

for the estimation of financial distress likelihood (FDL) using awith the use of panel data

methodology and they presented a financially (not legally) based definition of distress Their

sample covered 1583 US companies and 2250 companies from other G-7 countries for the

period 1990ndash2002 period They used a re-estimated Z-Score as a benchmark The FDL model

outperformed the Z-Score model in terms of stability and classification power for different

14

countries and time periods In the case of the re-estimated Z-Score model only profitability and

retained earnings kept maintained their significance for different years and countries

Wu et al (2010) evaluated the performance of five models (Altman 1968 Ohlson 1980

Zmijewski 1984 Shumway 2001 and Hillegeist et al 2004) using an up-to-date data set for

US listed firms Based on these models the authors built their own integrated model that is a

multi-period logit model with an expanded set of variables The integrated model which

combined accounting and market data as well as firmsrsquo characteristics outperformed the other

models Altmanrsquos Z-score performed poorly in compared withison to the other 4 models

Shumwayrsquos model performed best Hillegeist et alrsquos model performed adequately and Ohlsonrsquos

and Zmijewskirsquos models performed adequately although their performance deteriorated over

time

Jackson and Wood (2013) tested 13 different models of bankruptcy prediction and assessed their

efficacy using the ROC curve They selected three single- variable models three accounting-based

models (including Altmanrsquos Z-Score) in two versions (with up-dated coefficients and a neural

network approach) and four contingent claims models The contingent claims models and the

latter group outperformed the other models The best four best models were contingent claims

models based on European call and barrier options Although tThe predictive performance

improved withIn the case of the application of the neural network to accounting-based models

the predictive ability improved but it was still lower than within the case of the market-based

models

15

Acosta-Gonzaacutelez and Fernaacutendez-Rodriacuteguez (2014) used genetic algorithms with the Schwarz

information criterion (GASIC) for variable selection combined with the logit model for bankruptcy

prediction Altmanrsquos Z-Score model was used as one of two benchmarks for the authorsrsquo model

evaluation For one- step- ahead forecasting Altmanrsquos model was better in at predicting failed

firms but the type II error was high For two- and three- steps- ahead forecasts the performance

of the models was similar forin terms of failed firms but forin the case of non-failed firms and

the prediction accuracy of Altmanrsquos model was worse For four- steps- ahead forecasts the GASIC

model outperformed the other models forin the case of failed firms and but it performedwas

comparablye forin the case of non-failed firms

In general Altmanrsquos Z-Score model underperformed in compared withison to market-based

models but there was also evidence indicated that it performed well for short-term distress

prediction The question of whether market-based models are perform better than accounting-

based models has been raised many times (eg discussion in Das et al 2009 Bauer and d

Agarwal 2014) Our purpose is not to contribute to this strand of research but rather to focus on

the accounting-based approach In this study we analyze primarily analyze privately held firms

so there are by definition no market data for these firms In this case aAn accounting-based

approach is the only solution that could be appliedapplicable in this case by banks as lenders or

by investors holding debt securities of firms not listed on the stock exchange Thus far most of

the studies have concentrated on the US market only a few of them have used data from other

16

countries such as Japan the UK Lithuania or the G-7 countries Our analysis is a significant

extension of the previous research

4 Methodology and research hypotheses

The literature survey shows that the Z-Score model (publicly traded firms) the Zrsquo-Score model

(private manufacturing firms) and the Zrsquorsquo-Score model (private and publicly

traded manufacturing and non-manufacturing firms) have been adapted for different purposes

In this study we are first interested first in assessing the performance of the original Zrsquorsquo-Score

model in classifying bankrupt and non-bankrupt firms in an international context with special

focus on the European market However we also validate the results in a set of non-European

countries to generalize the results for circumstances outside Europe Second we will re-estimate

the model using extensive international data and then use the re-estimated Zrsquorsquo-Score model as a

benchmark for assessing the effects of different factors on the modelrsquos performance in terms of

classification accuracy We will assess the effects of the following five factors on this

performance year of bankruptcy size of firms age of firms industry and country of origin WIn

all we will test a set of hypotheses based on the effects of the model on performance on two

different levels First we will test a set of hypotheses on a pooled set of all firms and second on

data from each country individually CIn compared withison to the previous research our this

studyrsquos contribution is the focus on an international context not just simply the model

application or re-estimation for of a given countryrsquos data Because weour aim is focusing on the

performance of the Zrsquorsquo-Score mModel when using ain large body of international data the

research hypotheses are of a technical nature and are presented given as follows

17

H1 Obsolescence of the coefficients The Zrsquorsquo-Score mModel was originally estimated using the

same sample of firms that was used to develop the original Z-Score mModel The bankruptcies

in the estimation data occurred during the period 1946ndash-1965 period Thus the oldest

observations are from nearly seventy years ago during the post-war period Altman (1983)

recommended utilizing data as near to the present as possible when developing a bankruptcy

prediction model It is obvious that firmsrsquo the financial behavior of firms and their business

environment have significantly changed since then Thereforen potentially making the

importance of the financial ratios as reflected by the coefficients of the model may differ from

their original importance reflected by the coefficients of the model Therefore we suggest as

the first hypothesis (H1) that the re-estimation of the coefficients of the four original variables

of the Zrsquorsquo-Score model will improve the classification performance of the model in an

international context This hypothesis is supported by the previous research (eg Grice and

Ingram 2001) and by practice H1 aims to support this evidence on the international level and is

expressed as follows

H1 RThe re-estimatingon of the coefficients of the Zrsquorsquo-Score model improves the its

classification accuracy of the model

H2 Method of estimation The original Zrsquorsquo-Score model has been estimated using MDA However

MDA is based on the ordinary least squares (OLS) method and thus requires assumptions of

multinormality homoscedasticity and linearity which are not often met in empirical financial

ratio analysis Therefore we re-estimate the Zrsquorsquo-Score model using logistic regression analysis

(LRA) to assess the effect of the method of estimation method LRA does not require most of the

18

restricting assumptions of MDA In LRA the multivariate normality of the independent variables

is not required nor are homoscedasticity and linearity For the sake of OLS MDA can be more

useful than LRA for small samples such as the original sample of 66 firms used in the estimation

of the Zrsquorsquo-Score mModel However in a large sample LRA may potentially perform better In this

study we use large samples which is advantageous for LRA Therefore our second hypothesis

(H2) suggests is that the classification performance of the re-estimated Zrsquorsquo-Score model will be

improved when it is estimated using LRA instead of MDA The model re-estimated for the original

variables using LRA and all pooled data is called here the Zrsquorsquo-Score LR-modelLR model The

performance of this re-estimated model is used in this study as the benchmark for further

analyses Thus the second hypothesis is as follows

H2 The prediction accuracy of the logistic regression version of the Zrsquorsquo-Score model is higher

than that of the multiple discriminant analysis version

H3 Bankruptcy year The model based on the relationship between bankruptcy and financial

ratios is likely to be affected by the macroeconomic environment These effects may significantly

decrease the classification accuracy of the model If the model is estimated using data from one

year and it will be applied to data from another year the validity of the model can be questioned

Business cycles inIn terms of economic growth credit policy and interest rates business cycles

can ahave an effect on the boundary between bankrupt and non-bankrupt firms The original Zrsquorsquo-

Score model is estimated using data from the period 1946ndash1965 period which includes several

business cycles Therefore the model is not focused on any specific stage of a cycle and does not

explicitly take into account of the bankruptcy year Altman (1983) suggested gathering data from

19

firms for the last most recent few years when developing a prediction model In this study the

benchmark Zrsquorsquo-Score LR -model will beis estimated for a shorter period than in the original

estimation although it covers several recent years on for different stages of the business cycle in

different countries Therefore the third hypothesis (H3) assumes is that the classification

accuracy of the benchmark model can be increased by explicitly taking into account of the year

of bankruptcy in the estimation of the model estimation The third hypothesis is presented as

follows

H3 The prediction accuracy of the modelrsquos prediction accuracy is higher whenith the

effect of the year of bankruptcy is included is higher than that of the model without the

effect

H4 Size of the firm The boundary between bankrupt and non-bankrupt firms is different for small

and larger firms which decreases the performance of the model estimation when estimated for

data from one size category and isare applied to for data from another size category For the

bankrupt and non-bankrupt firms in the original data for Zrsquorsquo-Score model estimation the range

of asset sizes was ranged between approximately 1 and 25 million US dollars The data did not

include very small or very large firms Altman (1983) regarded the suitability of the original Z-

Score model (and likewisein the same way the Zrsquorsquo-Score model) for all firms as debatable

because of this omission it did not scrutinize very large or very small firms In the currentis study

the benchmark Zrsquorsquo-Score LR -model will beis estimated for data from many size categories from

very small firms to very large firms The fourth hypothesis (H4) assumes that the classification

performance of the uniform benchmark LR -model based on the original four financial variables

20

of the Zrsquorsquo-Score model is improved when the size category of the firm is explicitly taken into

account Thus we present the fourth hypothesis as follows

H4 The modelrsquos prediction accuracy of the modelis higher whenith the effect of size is

includede is higher than that of the model without this effect

H5 Age of the firm International insolvency statistics generally show that bankruptcy risk is a

function of the age of the firm Very young firms typically show a very high risk The original Zrsquorsquo-

Score model does not explicitly take age into account age However Altman (1983) noted that

the age of a firm is implicitly considered in the Retained EarningsTotal Assets ratio (X2) which

was regarded as a new ratio in the bankruptcy prediction context A relatively young firm will

probably show a low ratio because it has not had time to build up cumulative profits Thus a

young firm is to some degree discriminated against in the model and its likelihood of being

classified as bankrupt is relatively higher than that of an older firm The incidence of failure is

much higher in the early years of a firm AlthoughDespite that the age of the firm in this way is

implicitly taken into account in X2 we expect that an explicit consideration of the age will improve

the classification accuracy bydue to controlling for the age factor Therefore the fifth hypothesis

(H5) proposes that the performance of the uniform benchmark model based on the original four

financial variables of the Zrsquorsquo-Score model is increasesd when the age of the firm is explicitly taken

into account The fifth hypothesis is can be presented as follows

H5 The modelrsquos prediction accuracy of the model is higher when with the effect of firm

age is higher than that of the model without this effectincluded

21

H6 Industry of the firm The original Zrsquo-Score model was estimated only for manufacturing firms

Altman (1983) stated that it would be ideal to develop a bankruptcy prediction model utilizing a

homogenous group of bankrupt firms If we are interested in a particular industry grouping we

should gather data from bankrupt and non-bankrupt firms in these groupings Previous studies

show that financial distress analysis is influenced by the industry effect (Smith and Liou 2007)

Firms in different industries tend to report different levels of the same financial ratios which may

have an effect on the boundary between bankrupt and non-bankrupt firms This industry effect

may be present in the Zrsquo-Score model especially due to the SalesTotal Assets ratio (X5) This ratio

which showed the least significance on a univariate basis while making but it had made a very

significant contribution to the discriminant power of the multivariate model Altman (1983)

recognized the potential industry effect due to a the wide variation among industries in asset

turnover and specified the Zrsquorsquo-Score model without X5 However also the Zrsquorsquo-Score model was

also estimated using the original sample of manufacturing firms In our analysis the uniform

benchmark model based on the original four financial variables of the Zrsquorsquo-Score model is

estimated for a statistical sample representing different industries Therefore the sixth

hypothesis (H6) assumes that an explicit consideration of industry will improve the classification

accuracy of this benchmark model H6It can be expressed in the following form

H6 The modelrsquos prediction accuracy of the modelis higher whenith the effect of industry is

higher than that of the model without this effectincluded

H7 Country of origin The original Zrsquorsquo-Score model has been estimated only for USUS firms It

can be expected that the international applicability of the model to other countries is affected by

22

country-specific differences The eEconomic environment legislation culture financial markets

and accounting practices in a country may affect the financial behavior of firms and the boundary

between bankrupt and non-bankrupt firms These factors may potentially weaken the

classification performance of the model in for other countries outside other than that e one fore

country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the

seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm

into account will improve the classification accuracy of the benchmark model In our empirical

study the country effect will beis assessed by including a variable of for country risk Therefore

the seventh hypothesis is presented as follows

H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country

risk is higher than that of the model without this effectincluded

5 Empirical data and statistical methods

51 Sample of firms

The principal data of for this study are were extracted from the ORBIS databases of Bureau Van

Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the

moment of sampling contained administrative information on more than 50 million European

firms However income statement and balance sheet information was available for

approximately 8 million companies More than 99 of the companies covered in this database

are private companies from different various industries justifying the use of the Zrsquorsquo-Score model

instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all

industrial groupings and for both private and public entities (Altman 1983 124 Altman and

Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope

23

of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all

industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly

used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that

international comparability may be a problem when administrative firm-level data are

internationally pooled For administrative data Although the definition of variables is usually less

harmonized for administrative data tThis is less of a problem in the ORBIS database because of

the common international format of balance sheets For example although some discrepancies

in profitloss statements may arise because of differences in fiscal systems across countries

balance sheet variables largely adhere to international standards

AThere are a number of factors that influence the international applicability of bankruptcy

prediction models These factors include accounting legislation and practice creditor rights and

investor protection judicial efficiency corporate governance bankruptcy protection and

insolvency management and firm risk-taking These factors strongly differ between European

and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-

Score model outside Europe First it is especiallyparticularly important to include the USUS as

because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It

is include d in our study as well because it has the largest market capitalization in the world

Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it

applicable to emerging market companies Therefore we include firms from China and Colombia

which represent two very culturally and institutionally very dissimilar emerging market

countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt

24

firms ie the limit we set for European countries) from ORBIS World were not

available Thus the results are also estimated and tested for three non-European countries (the

United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-

Score modelrsquos performance The samples of firms from these countries are were extracted from

ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms

from around the world

SFor statistical sampling several requirements are set for the statistical sampling of the empirical

data First we require that the firm to be selected be an industrial (non-financial) company

Second its owners must have limited liability (so partnerships and sole proprietors are left out

of the study) Third we set a minimum requirement for the size of the firm Because the financial

ratios of very small firms are generally too unstable for a failure prediction model these firms

are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have

exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we

include in our estimation sample firms from all European countries and 3 pre-selected non-

European countries where the number of failed firms is more greater than 60 If the number of

failed firms for any European country is less than 60 the firms from this country are only included

only in the test sample For qualifying European countries the firms are randomly classified in

the estimation and test samples so that the number of firms is approximately equal in both

samples Thus our estimation data include firms from 28 European and 3 non-European

countries Fifth all failed firms that fulfill the above requirements are included in our samples

However if the number of non-failed firms in a country is very high a sample is randomly

25

selected from that country Finally the time span of fiscal years potentially available for this study

ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in

the database are from a financial period within 2007 and 2010 earlier years are also excluded

for non-failed firms for comparability All qualifying observations of non-failed firms from the

years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses

of failed firms to the last most recent financial statements available before failure The four

independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize

outliers

Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test

data by country The estimation sample includes data from 2602563 non-failed and 38215

failed firms from 28 European and 3 non-European countries The test sample is slightly larger

because it includes data from 31 European and 3 non-European countries From For the country

of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt

firms The available US data that were available consist only of listed (and delisted) firms From

China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are

included in the estimation sample although there are only 32 such firms altogether7 The Chinese

datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status

are separately analyzed separately only in for the test data8 ST firms are listed firms suffering

6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor

26

from serious financial difficulties Excluding the special US data and the two non-private Chinese

datasets 994 of the observations in the data are private firms

(Table 1 here)

52 Status of failed firms

ORBIS has five classes for potentially active firms (active default of payment receivership

dormant and branch) and seven classes for inactive firms that no longer carry out business

activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation

branch and no precision) From Among these classes only active is selected to represent non-

distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by

considering (with exceptions described below) a firm failed if its status in ORBIS is stated as

bankruptcy However because of the small number of bankrupt firms in some countries we also

consider receivership (active) firms as failed even if they are active These firms generally suffer

from serious financial distress However firms in liquidation are generally not included in the

sample of failed firms Firms in liquidation may depending on the country contain firms that

have ceased activities due to reasons other than failure (mergers discontinuing the operations

of a daughter company or of a foreign branch etc) Therefore for most countries we select only

firms that are coded as being bankrupt or under receivership However there are a number of

special cases where failed firms are coded under a different status heading These countries or

samples are the following

Country Status categories Bulgaria In liquidation Bankruptcy

27

Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the

study (for example Switzerland) If a country had only a very small number of failed firms it was

dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries

including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status

classes (including the bankruptcy category) are not completely homogenous within European

countries due to different legislations although there are obvious similarities in insolvency acts

(Philippe et al 2002) China is a special case because it includes samples with three different

criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are

two different samples (liquidation and receivership)

53 Statistical methods

In this study seven research hypotheses are drawn for statistical testing The statistical analysis

will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The

classification performance of the original model is assessed by the AUC (Area Under Curve)

measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close

connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random

28

model and 05 for a model with an average classification performance In SAS software is used

for all statistical analysess SAS software is used

The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is

tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical

method (multiple discriminant analysis or MDA) The problem is that the estimation sample

includes different numbers of failed and non-failed firms from 31 countries In the original Z-

Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from

the USUS Following the characteristics of these data we weight the firms so that the weights

for the failed and non-failed firms are equally In so doingthis way the non-proportional

sampling in from different countries does will not affect the re-estimated model The number of

firms from different countries however varies significantly leading to greater weights for larger

countries To avoid this problem the observations are also weighted so that each country has an

equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using

these weighted data and the resulting AUC is compared with that based on the original model

The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score

model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is

based on less- restrictive statistical assumptions than MDA In this estimation the dependent

variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA

does not require that independent variables be multivariate normal or that groups have equal

covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA

29

creates a score (logit) score L for every firm It is assumed that the independent variables are

linearly related to L This score or logit is used to determine the conditional probability of failure

as follows

= (eq 4)

where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the

original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing

the statistical significance of the difference between AUCs for this LR model and for the re-

estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it

is used as a benchmark for further statistical AUC comparisons because LR is applied as the

principal method in the testing of the remaining research hypotheses

The third hypothesis (H3) is associated with the performance effect of taking account of the

bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on

the following logit

(eq 5)

where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model

bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and

D1 = 1 when year = 2008 0 otherwise

D2 = 1 when year = 2009 0 otherwise

D3 = 1 when year = 2010 0 otherwise

)|1( XYp )( 441101

1

1

1XbXbbL ee

3

1

4

10

jjji

ii DcXbbL

30

The dummy variables do not directly refer to the bankruptcy year which that is not given in the

data basedatabase but rather to the last available year before bankruptcy For failed firms there

is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is

the base category If the AUC of this extended LR model statistically significantly exceeds the AUC

of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3

Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third

hypothesis above However for each hypothesis appropriate variables are used instead of the

year dummies Hypothesis H4 addresses the performance effect of taking size into account the

size and it will beis tested using by adding two additional variables measuring firm size in to the

LR -model In this LR model size is measured by the natural logarithm of total assets and its

squared form In this way the effect of logarithmic size can be reflected by in a function following

the second-order parabola Hypothesis H5 predicts that the classification performance improves

when the age of the firm is explicitly taken into account When testing this hypothesis the

category 6ndash14 years category is used as the base category and two dummy variables are

incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis

H6 looks at whether is associated with the classification performance is affected bythe effect of

the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt

is tested here using dummy variables for seven industries (D1 restaurants and hotels D2

construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and

water production D7 information technology) with all other industries acting as the base

category

31

Hypothesis H7 predicts that the explicit consideration of the country of origin will improves

classification performance This hypothesis is tested by using country risk measures instead ofnot

using dummy variables for countries but using country risk measures instead The country risk of

each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the

annual closing of accounts The rating is numerically recoded such that the best rating AAA

equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating

D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial

ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-

modelLR models with the original four financial ratios and the additional variables specified in

the hypotheses are estimated for all data In addition an LR model including all additional

variables is estimated for all data to assess the simultaneous effect of all variables Finally six of

the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not

included in this country- level testing hypothesis H7 is not tested because the additional variable

(country risk) is constant within the country

6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models

Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score

model for all data The variation in the ratios is significant as shown by the standard deviation

and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-

failed firms are close to each other indicating a symmetry of distributions However this is not

32

the case for the failed firms For the faileddistressed firms the median exceeds the mean for

these three ratios indicating negatively skewed distributions For X4 (BVETD) the means

significantly exceed the median for both failed and non-failed firms indicating a positively

skewed distribution For each of the four variables both the mean and the median are higher for

non-failed firms than for failed firms which is consistent with the expectations The difference

between the means of the non-failed and failed firms is larger in the original USUS data than in

our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is

approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of

EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may

indicate lower classification accuracy than in the original sample

(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates

(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the

large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model

Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same

statistical method or MDA The coefficients here are negative because the models are estimated

using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight

than in the original USUS data while the weights of WCTA and BVETD have proportionally

decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the

logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR

model These coefficients are directly comparable with those of the MDA model which isas

expected for the this exceptionally large sample The differences in the coefficients of the original

33

four variables among the eight LR-modelLR models (Models 1-8) are small For each model the

coefficient of BVETD is very close to zero This indicatinges that the original four variables and

the additional variables are quite independent of each other

(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-

modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate

that after 2007 (the base category) this risk of failure significantly decreased year by year The

base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due

to as a result of the global financial crisis The crisis played a significant role in the failure of key

businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The

effects were especially felt in Europe The coefficients of Model 4 for the size variables show that

the contribution of size on to the logit (risk measure) is atreaches its maximum value when

logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model

5 confirms the riskiness of young firms because the risk of failure is very high for newly founded

firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The

coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky

industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is

statistically significant (because of the large sample) but negative and very close to zero Finally

the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7

62 All data performance of the Zrsquorsquo-Score models

34

Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1

refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of

all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average

accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)

Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its

performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The

lower part of the table shows the AUCs for the countries included only in the test data The

performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)

samples It is also low and for the liquidation firms in the United Kingdom

The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5

presents the differences of these medians between the non-failed and failed firms by country

This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of

medians This correlation which is high for each financial ratio showing that the effects of the

ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large

except for EBITTA which This implies with the exceptionally high AUC that the ST firms

systematically differ from the non-ST firms although the differences are not extremely large The

differences between the medians are very large in Poland for each ratio justifying the high AUC

They are also large and in Finland and in the Czech Republic where the difference in EBITTA is

average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in

all four ratios are below the average a result thatwhich is obviously associated with a low AUC

35

In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even

negative In Iceland and Ireland the differences only in EBITTA are exceptionally small

(Table 4 here)

(Table 5 here)

Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by

MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)

supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is

at approximately the average level The re-estimation of the coefficients has led to improved

classification accuracy in a number of countries especially in Bosnia China (ST) Norway and

Greece However it has impaired the classification accuracy in the UK and China (delisted)

Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-

modelLR model) showing the effect of the estimation method For the benchmark model the

AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between

among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR

model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country

This result was expected because the coefficients of the models are directly comparable The

similar results for the models may also indicate that the independent variables conform to

multinormality This last latter result confirms what most researchers in the field of default

classification models have concluded that the accuracy levels of MDA and logistical regression

36

models are extremely similar despite any statistical violations such as normality between MDA

and logistical regression models

Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)

than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects

are not positive for all countries The effects are positive for example for Russia Estonia

Germany Ireland and Latvia but these effects are not statistically significant There are

statistically significant negative effects for China (ST) and Serbia There are notable negative

effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are

due to the exceptional annual distributions of failed firms in these countries For the countries

with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the

countries with positive effects it is only a few percent For each group non-failed firms are quite

equally distributed over years When the coefficient of D3 is very low (-0666) it strongly

decreases the risk estimates of most failed firms in the former countries but of only a few failed

firms in the latter countries This which leads to the observed effects H3 is supported by

the level of the whole sample However in some country samples (at the level of individual

levelies) H3 is not supported

Model 4 (LR-modelLR model with size variables) performs better than the benchmark model

which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads

and to significant improvements in the AUC for eg China (delisted) and Austria For China

(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC

37

also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs

in a few countries but thise decrease is not significant Model 4 is based on the four original

variables and the size effect following a second-order parabola This kind of size effect is very

small for micro firms but increases when approaching its maximum value in middle-sized firms

The countries with a positive change in AUC typically have data where in which the percent of

failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized

failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In

contrast the countries with a negative change in AUC have data where in which the percentage

of failed micro firms is exceptionally high (70-80)

Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly

the same AUC as the benchmark model (AUC = 0748) However the difference is positive and

statistically significant giving at least marginal support to H5 (age effect) For nearly all countries

the effect of the age on AUC is small For Austria however thise effect is however positive and

significant For Austria the percentage of non-failed young firms (less than 6 years) among non-

failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed

firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong

positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only

a few non-failed firms which This obviously leads to an improvement in AUC

Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC

supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its

38

AUC is notably higher than the benchmark AUC only in only a few countries such asfor example

France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg

restaurants and hotels and the information technology industryies but it has a positive effect

for on the construction and manufacturing industries For the countries with a positive effect on

AUC the percent of non-failed firms in restaurants and hotels and information technology is

high while that of failed firms is low For the risky industries (construction and manufacturing)

these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many

failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)

firms Therefore the AUC will be increasesd This situation is reversed for the countries with a

negative effect on AUC (Austria and Slovenia) The samples of these countries include a high

percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC

Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher

classification performance (AUC = 0749) than the benchmark model This result gives only very

weak support for H7 (country of origin effect) However for each country the resultant AUC is

nearly identical to that given by the benchmark model This result was expected due to the

negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7

This result implies that country risk hasdoes not have noany effect on the boundary between

bankrupt and non-bankrupt firms

Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional

variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to

39

the benchmark AUC in all test data However the effect on the AUCs largely varies and is either

negative or positive in different countries The positive effect is large in several countries such

as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative

effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show

that the inclusion of additional variables in the original model will usually increase the AUC but

not in every country

The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with

the USUS firms being unlike the majority of other firms in this study being listed or delisted

companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese

firm samples is associated with very small differences between the medians of the non-failed and

failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable

with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed

firms is very high Prior studies based on Chinese ST -firms have also demonstrated good

predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for

additional research and modeling work regarding unlisted and delisted Chinese firms

63 Country-level data performance of the Zrsquorsquo-Score models

The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform

all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the

different models estimated for each country separately (country-level models) In this table the

ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated

40

from the country data this benchmark will beis clearly outperformed by the resulting MDA

(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and

Sweden) However these results give only weak support for H1 at the country level because the

effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and

2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs

given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed

by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation

method)

Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC

than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3

leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United

StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in

nearly every country supporting H4 This improvement is significant however only in the United

StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark

model but for which it is only 0710 for the benchmark model It This improvement is also notable

in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age

category dummies) gives higher AUCs than the benchmark model for several countries but none

of the improvements are significant The positive effect is particularly strong especially in

Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only

weakly supports H5 because these negative effects are relatively small

41

Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects

on AUCs when compared with the benchmark However the only significant effect is the negative

effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the

industry dummies However there are notable positive effects on the AUC in for example

France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak

support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in

AUC compared with the benchmark model in Colombia Iceland and the Unites States It also

leads and to notable improvements in AUC at least in Belgium Denmark Estonia France

Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are

found only in Austria and Slovenia Thus as a conclusion we can concludestate that the

classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can

remarkably increases in most countries when different effects are taken into account by

additional variables This increase is found in most European countries and in Colombia and the

United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-

modelLR model and it can only be slightly improved only slightly by additional variables

(Table 6 here)

7 Summary of the study and a suggested extension

The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model

originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is

assessed using a very large international data set We test how the original version of the Zrsquorsquo-

42

Score model performs in different countries and how re-estimations using another statistical

method and different additional variables affect the classification performance when the data

are very heterogeneous For this kind of testing seven research hypotheses on classification

performance are extractedformulated These research hypotheses are tested for all data and

separately for country data (country-level analysis) The estimation data are from 31 countries

and the results are validated for 34 countries The countries are mainly from Europe but three

non-European countries are included (China Colombia and the United StatesUS) The statuses

used in the classification are mainly bankruptcyactive but receivership firms are also considered

to have failed In the Chinese data ST (special treatment) and delisted firms are also separately

analyzed as failed firms

The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very

satisfactorily in an international context The effects of the four financial ratios on performance

are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small

contribution in re-estimation The original model performs very well in several countries such as

Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only

marginally improved the classification performance thus weakly supporting the obsolescence

hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust

across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion

holds for the re-estimation of the model using LRA because the performance results are very

similar with to those of MDA (H2) The use of additional variables in the model generally

improvesd the classification accuracy of the original model but the results for countries are

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

9

Therefore Altman (1983) advocated a complete re-estimation of the model substituting the

book value of equity for the market value in X4 Using the same data Altman extracted the

following revised Zrsquo-Score model

Zrsquo = 0717X1 + 0847X2 + 3107X3 + 0420X4 + 0 998X5 (eq 2)

where X4 = Book value of equityBook value of total liabilities with the other variables being

the same as those in the original (1968) Z-Score model

DAltman ue to the lack of a private firm database Altman did not test the Zrsquo-Score model on a

secondary sample due to the lack of a private firm data base However he analyzed the accuracy

of a four-variable Zrsquorsquo-Score model that excludeding the SalesTotal assets ratio X5 from the

revised model because of a potential industry effect The industry effect that is more likely to

take place when this kind of industry-sensitive variable (asset turnover) is included in the model

Altman then estimated the following four-variable Zrsquorsquo-Score model (Altman 1983)

Zrsquorsquo = 325 + 656X1 + 326X2 + 672X3 + 105X4 (eq 3)

The EBITTotal assets ratio X3 made the highest contributioncontributed most to the

discrimination power in this version of the model The classification results for the Zrsquorsquo-Score

model were identical to the revised five-variable Zrsquo-Score model In the currentis study our

empirical analysis focuses on the performance of the Zrsquorsquo-Score model version in predicting

bankruptcy prediction wheredue to it has its widest scope that is because as it is intended for

both privately held and publicly listed firms and for both manufacturing and non-manufacturing

firms

10

3 Literature review

We searched for papers published from the year 2000 onward 4 in prominent international

journals or and books We selected 31 articles in which the Z-Score was either used either as a

failure prediction proxy or was assessed mostly in terms of predictive ability Of the 31 studies

Altmanrsquos Z-Score model5 was used in 16 cases as the measure of distress or of financial strength

In 13 studies Altmanrsquos original model was modified and (or) verified including re-estimation and

in 2 cases it was used solely for the robustness check As Pindado et al (2008) noted the Z-Score

was also used for other purposes such as the evaluation of the costs and benefits of covenants

in bonds the choice of the type of debt type (bank vs non-bank private or public) and the

relationship between investment and internal funds We focused on this part of the literature

that verified and (or) modified Altmanrsquos original model The wide broad usage of the Z-Score

model foras a measuringe of financial distress and performingas well as for the robustness checks

indicates that it is acceptabilityed as a reasonable simple and consistent measure of the

distressed firms

In the case of the modification of the The most common modification to the Z-Score model

model the most common change was the use of other estimation techniques or country-specific

data The use of Altmanrsquos ratios in combination with techniques other than MDA has improved

the its prediction capability TAdditionally the application of new data of on both US and non-US

4 Research devoted to the application of the Z-Score model before 2000 was reviewed by Grice and Ingram (2001) 5 Most of the models focused on stock- exchange- listed firms thus the Zrsquo-Score and Zrsquorsquo-Score models were not used

11

firms also improved model performance Kwak et al (2005) applied used Multiple Criteria Linear

Programming (MCLP) to model 5 Altman and 9 Ohlson variables They used with data on US

bankrupt US firms from 1992 to 1998 and nearly 6 times more matched US control firms The

MCLP approach performed better than Altmanrsquos original model and gave results similar to or

better than those of Ohlsonrsquos original model The original models were not recalculated and the

authors referred solely to their original prediction rates only Merkevicius et al (2006) using data

on US and Lithuanian firms developed a hybrid artificial -discriminantte model combining MDA

and an unsupervised learning artificial neural network (called the SOM-Altman model) with the

use of data on US and Lithuanian firms Thise hybrid SOM-Altman model reached a high

prediction rate of 9235 Xu and Zhang (2009) applied Altmanrsquos Z-score Ohlsonrsquos O-score and

Mertonrsquos distance-to-default (D-score) models to Japanese firms to check whether these models

are useful for bankruptcy prediction in Japan They also ldquomergedrdquo theose models into a new C-

score model They then introduced variables unique to Japan to check whether corporate

structure variables have any impact on the probability of bankruptcy t They called this the model

X-score model These two models were useful for Japanese firms in predicting bankruptcy

prediction but the market-based model was the most successful In summary the developed C-

score and X-score (with country-specific variables) models improved bankruptcy prediction

Tinoco and Wilson (2013) used the original Z-Score as one of the benchmarks to assess the

performance of their model developed for UK listed companies with combined accounting

market and macroeconomic data Altmanrsquos Z-Score presented very good classification accuracy

in the case of financially distressed firms (81 vs 87 for the new model) however it was less

correct for financial health prediction Another modification was introduced by Lyandres and

12

Zhdanov (2013) who posed the question of whether the inclusion of variables related to

investment opportunities improved the predictive power of three models (Altmanrsquos Z-score

model and Zmijewskirsquos and Shumwayrsquos models) They used three proxies for investment

opportunities (market-to-book value-to-book and RampD-to-assets) The measures of investment

opportunities were linked to the likelihood of default The inclusion of either of these measures

improved the out-of-sample forecasting ability of all three

The verification of Altmanrsquos model concentrated on its efficacy or on how it compares a

comparison with other accounting-based market-based or hazard models However although

the original Z-Score model was not solely an accounting-based approach because the market

value of equity was utilized we classify it here as an accounting -based approachbecause the

market value of equity was utilized Grice and Ingram (2001) used a new novel data set of US

firms and posed three questions about the efficacy of Altmanrsquos model They concludinged that

the prediction accuracy of Altmanrsquos model had declined over time and that the coefficients of

the model had significantly changed which means that the relation between the financial ratios

and the signs of financial distress had changed over time The model was sensitive to industry

classification (more efficient for manufacturing firms than for non-manufacturing firms) but was

not sensitive to the type of financial distress Similar conclusions were drawn by Grice and Dugan

(2003) regarding Ohlsonrsquos (1980) and Zmijewskirsquos (1984) models

Hillegeist et al (2004) compared Altmanrsquos Z-score and Ohlsonrsquos O-score (with original and

updated coefficients) with the model based on BSM option pricing (BSM-Prob) They used

13

relative information content tests to compare the out-of-sample performance BSM-Prob

outperformed accounting-based models The conclusions were robust to various modifications

of accounting-based models such as updated coefficients industry effects and the separation

of variables Chava and Jarrow (2004) employed an extended bankruptcy database of US listed

firms to prove the superiority of Shumwayrsquos model (2001) over Altmanrsquos (1968) and Zmijewskirsquos

(1984) models The authors re-estimated the models over the 1962ndash1990 period and forecasted

bankruptcies over the 1991ndash1999 period In the case of Shumwayrsquos model 744 (in the first

decile) of the bankruptcies were correctly identified for with Altmanrsquos model it was 632 and

for with Zmijewskirsquos model it was 432 Shumwayrsquos market-based model also outperformed

accounting-based models in terms of the ROC curve (091)

Reisz and Perlich (2007) used the barrier option for bankruptcy prediction and compared its

discriminatory power with other market-based models and Altmanrsquos Z-Score and Zrdquo-Score The

dataset covered nearly 6000 industrial firms over the period 1988ndash2002 period The authors

proved the domination superiority of Altmanrsquos Z-Score and Zrdquo-Score models for short-term (up

to 1 year) bankruptcy prediction For medium- and long-term bankruptcy prediction their barrier

option model outperformed the other models Pindado et al (2008) developed an ex -ante model

for the estimation of financial distress likelihood (FDL) using awith the use of panel data

methodology and they presented a financially (not legally) based definition of distress Their

sample covered 1583 US companies and 2250 companies from other G-7 countries for the

period 1990ndash2002 period They used a re-estimated Z-Score as a benchmark The FDL model

outperformed the Z-Score model in terms of stability and classification power for different

14

countries and time periods In the case of the re-estimated Z-Score model only profitability and

retained earnings kept maintained their significance for different years and countries

Wu et al (2010) evaluated the performance of five models (Altman 1968 Ohlson 1980

Zmijewski 1984 Shumway 2001 and Hillegeist et al 2004) using an up-to-date data set for

US listed firms Based on these models the authors built their own integrated model that is a

multi-period logit model with an expanded set of variables The integrated model which

combined accounting and market data as well as firmsrsquo characteristics outperformed the other

models Altmanrsquos Z-score performed poorly in compared withison to the other 4 models

Shumwayrsquos model performed best Hillegeist et alrsquos model performed adequately and Ohlsonrsquos

and Zmijewskirsquos models performed adequately although their performance deteriorated over

time

Jackson and Wood (2013) tested 13 different models of bankruptcy prediction and assessed their

efficacy using the ROC curve They selected three single- variable models three accounting-based

models (including Altmanrsquos Z-Score) in two versions (with up-dated coefficients and a neural

network approach) and four contingent claims models The contingent claims models and the

latter group outperformed the other models The best four best models were contingent claims

models based on European call and barrier options Although tThe predictive performance

improved withIn the case of the application of the neural network to accounting-based models

the predictive ability improved but it was still lower than within the case of the market-based

models

15

Acosta-Gonzaacutelez and Fernaacutendez-Rodriacuteguez (2014) used genetic algorithms with the Schwarz

information criterion (GASIC) for variable selection combined with the logit model for bankruptcy

prediction Altmanrsquos Z-Score model was used as one of two benchmarks for the authorsrsquo model

evaluation For one- step- ahead forecasting Altmanrsquos model was better in at predicting failed

firms but the type II error was high For two- and three- steps- ahead forecasts the performance

of the models was similar forin terms of failed firms but forin the case of non-failed firms and

the prediction accuracy of Altmanrsquos model was worse For four- steps- ahead forecasts the GASIC

model outperformed the other models forin the case of failed firms and but it performedwas

comparablye forin the case of non-failed firms

In general Altmanrsquos Z-Score model underperformed in compared withison to market-based

models but there was also evidence indicated that it performed well for short-term distress

prediction The question of whether market-based models are perform better than accounting-

based models has been raised many times (eg discussion in Das et al 2009 Bauer and d

Agarwal 2014) Our purpose is not to contribute to this strand of research but rather to focus on

the accounting-based approach In this study we analyze primarily analyze privately held firms

so there are by definition no market data for these firms In this case aAn accounting-based

approach is the only solution that could be appliedapplicable in this case by banks as lenders or

by investors holding debt securities of firms not listed on the stock exchange Thus far most of

the studies have concentrated on the US market only a few of them have used data from other

16

countries such as Japan the UK Lithuania or the G-7 countries Our analysis is a significant

extension of the previous research

4 Methodology and research hypotheses

The literature survey shows that the Z-Score model (publicly traded firms) the Zrsquo-Score model

(private manufacturing firms) and the Zrsquorsquo-Score model (private and publicly

traded manufacturing and non-manufacturing firms) have been adapted for different purposes

In this study we are first interested first in assessing the performance of the original Zrsquorsquo-Score

model in classifying bankrupt and non-bankrupt firms in an international context with special

focus on the European market However we also validate the results in a set of non-European

countries to generalize the results for circumstances outside Europe Second we will re-estimate

the model using extensive international data and then use the re-estimated Zrsquorsquo-Score model as a

benchmark for assessing the effects of different factors on the modelrsquos performance in terms of

classification accuracy We will assess the effects of the following five factors on this

performance year of bankruptcy size of firms age of firms industry and country of origin WIn

all we will test a set of hypotheses based on the effects of the model on performance on two

different levels First we will test a set of hypotheses on a pooled set of all firms and second on

data from each country individually CIn compared withison to the previous research our this

studyrsquos contribution is the focus on an international context not just simply the model

application or re-estimation for of a given countryrsquos data Because weour aim is focusing on the

performance of the Zrsquorsquo-Score mModel when using ain large body of international data the

research hypotheses are of a technical nature and are presented given as follows

17

H1 Obsolescence of the coefficients The Zrsquorsquo-Score mModel was originally estimated using the

same sample of firms that was used to develop the original Z-Score mModel The bankruptcies

in the estimation data occurred during the period 1946ndash-1965 period Thus the oldest

observations are from nearly seventy years ago during the post-war period Altman (1983)

recommended utilizing data as near to the present as possible when developing a bankruptcy

prediction model It is obvious that firmsrsquo the financial behavior of firms and their business

environment have significantly changed since then Thereforen potentially making the

importance of the financial ratios as reflected by the coefficients of the model may differ from

their original importance reflected by the coefficients of the model Therefore we suggest as

the first hypothesis (H1) that the re-estimation of the coefficients of the four original variables

of the Zrsquorsquo-Score model will improve the classification performance of the model in an

international context This hypothesis is supported by the previous research (eg Grice and

Ingram 2001) and by practice H1 aims to support this evidence on the international level and is

expressed as follows

H1 RThe re-estimatingon of the coefficients of the Zrsquorsquo-Score model improves the its

classification accuracy of the model

H2 Method of estimation The original Zrsquorsquo-Score model has been estimated using MDA However

MDA is based on the ordinary least squares (OLS) method and thus requires assumptions of

multinormality homoscedasticity and linearity which are not often met in empirical financial

ratio analysis Therefore we re-estimate the Zrsquorsquo-Score model using logistic regression analysis

(LRA) to assess the effect of the method of estimation method LRA does not require most of the

18

restricting assumptions of MDA In LRA the multivariate normality of the independent variables

is not required nor are homoscedasticity and linearity For the sake of OLS MDA can be more

useful than LRA for small samples such as the original sample of 66 firms used in the estimation

of the Zrsquorsquo-Score mModel However in a large sample LRA may potentially perform better In this

study we use large samples which is advantageous for LRA Therefore our second hypothesis

(H2) suggests is that the classification performance of the re-estimated Zrsquorsquo-Score model will be

improved when it is estimated using LRA instead of MDA The model re-estimated for the original

variables using LRA and all pooled data is called here the Zrsquorsquo-Score LR-modelLR model The

performance of this re-estimated model is used in this study as the benchmark for further

analyses Thus the second hypothesis is as follows

H2 The prediction accuracy of the logistic regression version of the Zrsquorsquo-Score model is higher

than that of the multiple discriminant analysis version

H3 Bankruptcy year The model based on the relationship between bankruptcy and financial

ratios is likely to be affected by the macroeconomic environment These effects may significantly

decrease the classification accuracy of the model If the model is estimated using data from one

year and it will be applied to data from another year the validity of the model can be questioned

Business cycles inIn terms of economic growth credit policy and interest rates business cycles

can ahave an effect on the boundary between bankrupt and non-bankrupt firms The original Zrsquorsquo-

Score model is estimated using data from the period 1946ndash1965 period which includes several

business cycles Therefore the model is not focused on any specific stage of a cycle and does not

explicitly take into account of the bankruptcy year Altman (1983) suggested gathering data from

19

firms for the last most recent few years when developing a prediction model In this study the

benchmark Zrsquorsquo-Score LR -model will beis estimated for a shorter period than in the original

estimation although it covers several recent years on for different stages of the business cycle in

different countries Therefore the third hypothesis (H3) assumes is that the classification

accuracy of the benchmark model can be increased by explicitly taking into account of the year

of bankruptcy in the estimation of the model estimation The third hypothesis is presented as

follows

H3 The prediction accuracy of the modelrsquos prediction accuracy is higher whenith the

effect of the year of bankruptcy is included is higher than that of the model without the

effect

H4 Size of the firm The boundary between bankrupt and non-bankrupt firms is different for small

and larger firms which decreases the performance of the model estimation when estimated for

data from one size category and isare applied to for data from another size category For the

bankrupt and non-bankrupt firms in the original data for Zrsquorsquo-Score model estimation the range

of asset sizes was ranged between approximately 1 and 25 million US dollars The data did not

include very small or very large firms Altman (1983) regarded the suitability of the original Z-

Score model (and likewisein the same way the Zrsquorsquo-Score model) for all firms as debatable

because of this omission it did not scrutinize very large or very small firms In the currentis study

the benchmark Zrsquorsquo-Score LR -model will beis estimated for data from many size categories from

very small firms to very large firms The fourth hypothesis (H4) assumes that the classification

performance of the uniform benchmark LR -model based on the original four financial variables

20

of the Zrsquorsquo-Score model is improved when the size category of the firm is explicitly taken into

account Thus we present the fourth hypothesis as follows

H4 The modelrsquos prediction accuracy of the modelis higher whenith the effect of size is

includede is higher than that of the model without this effect

H5 Age of the firm International insolvency statistics generally show that bankruptcy risk is a

function of the age of the firm Very young firms typically show a very high risk The original Zrsquorsquo-

Score model does not explicitly take age into account age However Altman (1983) noted that

the age of a firm is implicitly considered in the Retained EarningsTotal Assets ratio (X2) which

was regarded as a new ratio in the bankruptcy prediction context A relatively young firm will

probably show a low ratio because it has not had time to build up cumulative profits Thus a

young firm is to some degree discriminated against in the model and its likelihood of being

classified as bankrupt is relatively higher than that of an older firm The incidence of failure is

much higher in the early years of a firm AlthoughDespite that the age of the firm in this way is

implicitly taken into account in X2 we expect that an explicit consideration of the age will improve

the classification accuracy bydue to controlling for the age factor Therefore the fifth hypothesis

(H5) proposes that the performance of the uniform benchmark model based on the original four

financial variables of the Zrsquorsquo-Score model is increasesd when the age of the firm is explicitly taken

into account The fifth hypothesis is can be presented as follows

H5 The modelrsquos prediction accuracy of the model is higher when with the effect of firm

age is higher than that of the model without this effectincluded

21

H6 Industry of the firm The original Zrsquo-Score model was estimated only for manufacturing firms

Altman (1983) stated that it would be ideal to develop a bankruptcy prediction model utilizing a

homogenous group of bankrupt firms If we are interested in a particular industry grouping we

should gather data from bankrupt and non-bankrupt firms in these groupings Previous studies

show that financial distress analysis is influenced by the industry effect (Smith and Liou 2007)

Firms in different industries tend to report different levels of the same financial ratios which may

have an effect on the boundary between bankrupt and non-bankrupt firms This industry effect

may be present in the Zrsquo-Score model especially due to the SalesTotal Assets ratio (X5) This ratio

which showed the least significance on a univariate basis while making but it had made a very

significant contribution to the discriminant power of the multivariate model Altman (1983)

recognized the potential industry effect due to a the wide variation among industries in asset

turnover and specified the Zrsquorsquo-Score model without X5 However also the Zrsquorsquo-Score model was

also estimated using the original sample of manufacturing firms In our analysis the uniform

benchmark model based on the original four financial variables of the Zrsquorsquo-Score model is

estimated for a statistical sample representing different industries Therefore the sixth

hypothesis (H6) assumes that an explicit consideration of industry will improve the classification

accuracy of this benchmark model H6It can be expressed in the following form

H6 The modelrsquos prediction accuracy of the modelis higher whenith the effect of industry is

higher than that of the model without this effectincluded

H7 Country of origin The original Zrsquorsquo-Score model has been estimated only for USUS firms It

can be expected that the international applicability of the model to other countries is affected by

22

country-specific differences The eEconomic environment legislation culture financial markets

and accounting practices in a country may affect the financial behavior of firms and the boundary

between bankrupt and non-bankrupt firms These factors may potentially weaken the

classification performance of the model in for other countries outside other than that e one fore

country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the

seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm

into account will improve the classification accuracy of the benchmark model In our empirical

study the country effect will beis assessed by including a variable of for country risk Therefore

the seventh hypothesis is presented as follows

H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country

risk is higher than that of the model without this effectincluded

5 Empirical data and statistical methods

51 Sample of firms

The principal data of for this study are were extracted from the ORBIS databases of Bureau Van

Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the

moment of sampling contained administrative information on more than 50 million European

firms However income statement and balance sheet information was available for

approximately 8 million companies More than 99 of the companies covered in this database

are private companies from different various industries justifying the use of the Zrsquorsquo-Score model

instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all

industrial groupings and for both private and public entities (Altman 1983 124 Altman and

Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope

23

of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all

industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly

used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that

international comparability may be a problem when administrative firm-level data are

internationally pooled For administrative data Although the definition of variables is usually less

harmonized for administrative data tThis is less of a problem in the ORBIS database because of

the common international format of balance sheets For example although some discrepancies

in profitloss statements may arise because of differences in fiscal systems across countries

balance sheet variables largely adhere to international standards

AThere are a number of factors that influence the international applicability of bankruptcy

prediction models These factors include accounting legislation and practice creditor rights and

investor protection judicial efficiency corporate governance bankruptcy protection and

insolvency management and firm risk-taking These factors strongly differ between European

and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-

Score model outside Europe First it is especiallyparticularly important to include the USUS as

because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It

is include d in our study as well because it has the largest market capitalization in the world

Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it

applicable to emerging market companies Therefore we include firms from China and Colombia

which represent two very culturally and institutionally very dissimilar emerging market

countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt

24

firms ie the limit we set for European countries) from ORBIS World were not

available Thus the results are also estimated and tested for three non-European countries (the

United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-

Score modelrsquos performance The samples of firms from these countries are were extracted from

ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms

from around the world

SFor statistical sampling several requirements are set for the statistical sampling of the empirical

data First we require that the firm to be selected be an industrial (non-financial) company

Second its owners must have limited liability (so partnerships and sole proprietors are left out

of the study) Third we set a minimum requirement for the size of the firm Because the financial

ratios of very small firms are generally too unstable for a failure prediction model these firms

are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have

exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we

include in our estimation sample firms from all European countries and 3 pre-selected non-

European countries where the number of failed firms is more greater than 60 If the number of

failed firms for any European country is less than 60 the firms from this country are only included

only in the test sample For qualifying European countries the firms are randomly classified in

the estimation and test samples so that the number of firms is approximately equal in both

samples Thus our estimation data include firms from 28 European and 3 non-European

countries Fifth all failed firms that fulfill the above requirements are included in our samples

However if the number of non-failed firms in a country is very high a sample is randomly

25

selected from that country Finally the time span of fiscal years potentially available for this study

ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in

the database are from a financial period within 2007 and 2010 earlier years are also excluded

for non-failed firms for comparability All qualifying observations of non-failed firms from the

years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses

of failed firms to the last most recent financial statements available before failure The four

independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize

outliers

Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test

data by country The estimation sample includes data from 2602563 non-failed and 38215

failed firms from 28 European and 3 non-European countries The test sample is slightly larger

because it includes data from 31 European and 3 non-European countries From For the country

of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt

firms The available US data that were available consist only of listed (and delisted) firms From

China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are

included in the estimation sample although there are only 32 such firms altogether7 The Chinese

datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status

are separately analyzed separately only in for the test data8 ST firms are listed firms suffering

6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor

26

from serious financial difficulties Excluding the special US data and the two non-private Chinese

datasets 994 of the observations in the data are private firms

(Table 1 here)

52 Status of failed firms

ORBIS has five classes for potentially active firms (active default of payment receivership

dormant and branch) and seven classes for inactive firms that no longer carry out business

activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation

branch and no precision) From Among these classes only active is selected to represent non-

distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by

considering (with exceptions described below) a firm failed if its status in ORBIS is stated as

bankruptcy However because of the small number of bankrupt firms in some countries we also

consider receivership (active) firms as failed even if they are active These firms generally suffer

from serious financial distress However firms in liquidation are generally not included in the

sample of failed firms Firms in liquidation may depending on the country contain firms that

have ceased activities due to reasons other than failure (mergers discontinuing the operations

of a daughter company or of a foreign branch etc) Therefore for most countries we select only

firms that are coded as being bankrupt or under receivership However there are a number of

special cases where failed firms are coded under a different status heading These countries or

samples are the following

Country Status categories Bulgaria In liquidation Bankruptcy

27

Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the

study (for example Switzerland) If a country had only a very small number of failed firms it was

dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries

including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status

classes (including the bankruptcy category) are not completely homogenous within European

countries due to different legislations although there are obvious similarities in insolvency acts

(Philippe et al 2002) China is a special case because it includes samples with three different

criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are

two different samples (liquidation and receivership)

53 Statistical methods

In this study seven research hypotheses are drawn for statistical testing The statistical analysis

will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The

classification performance of the original model is assessed by the AUC (Area Under Curve)

measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close

connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random

28

model and 05 for a model with an average classification performance In SAS software is used

for all statistical analysess SAS software is used

The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is

tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical

method (multiple discriminant analysis or MDA) The problem is that the estimation sample

includes different numbers of failed and non-failed firms from 31 countries In the original Z-

Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from

the USUS Following the characteristics of these data we weight the firms so that the weights

for the failed and non-failed firms are equally In so doingthis way the non-proportional

sampling in from different countries does will not affect the re-estimated model The number of

firms from different countries however varies significantly leading to greater weights for larger

countries To avoid this problem the observations are also weighted so that each country has an

equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using

these weighted data and the resulting AUC is compared with that based on the original model

The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score

model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is

based on less- restrictive statistical assumptions than MDA In this estimation the dependent

variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA

does not require that independent variables be multivariate normal or that groups have equal

covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA

29

creates a score (logit) score L for every firm It is assumed that the independent variables are

linearly related to L This score or logit is used to determine the conditional probability of failure

as follows

= (eq 4)

where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the

original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing

the statistical significance of the difference between AUCs for this LR model and for the re-

estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it

is used as a benchmark for further statistical AUC comparisons because LR is applied as the

principal method in the testing of the remaining research hypotheses

The third hypothesis (H3) is associated with the performance effect of taking account of the

bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on

the following logit

(eq 5)

where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model

bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and

D1 = 1 when year = 2008 0 otherwise

D2 = 1 when year = 2009 0 otherwise

D3 = 1 when year = 2010 0 otherwise

)|1( XYp )( 441101

1

1

1XbXbbL ee

3

1

4

10

jjji

ii DcXbbL

30

The dummy variables do not directly refer to the bankruptcy year which that is not given in the

data basedatabase but rather to the last available year before bankruptcy For failed firms there

is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is

the base category If the AUC of this extended LR model statistically significantly exceeds the AUC

of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3

Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third

hypothesis above However for each hypothesis appropriate variables are used instead of the

year dummies Hypothesis H4 addresses the performance effect of taking size into account the

size and it will beis tested using by adding two additional variables measuring firm size in to the

LR -model In this LR model size is measured by the natural logarithm of total assets and its

squared form In this way the effect of logarithmic size can be reflected by in a function following

the second-order parabola Hypothesis H5 predicts that the classification performance improves

when the age of the firm is explicitly taken into account When testing this hypothesis the

category 6ndash14 years category is used as the base category and two dummy variables are

incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis

H6 looks at whether is associated with the classification performance is affected bythe effect of

the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt

is tested here using dummy variables for seven industries (D1 restaurants and hotels D2

construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and

water production D7 information technology) with all other industries acting as the base

category

31

Hypothesis H7 predicts that the explicit consideration of the country of origin will improves

classification performance This hypothesis is tested by using country risk measures instead ofnot

using dummy variables for countries but using country risk measures instead The country risk of

each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the

annual closing of accounts The rating is numerically recoded such that the best rating AAA

equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating

D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial

ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-

modelLR models with the original four financial ratios and the additional variables specified in

the hypotheses are estimated for all data In addition an LR model including all additional

variables is estimated for all data to assess the simultaneous effect of all variables Finally six of

the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not

included in this country- level testing hypothesis H7 is not tested because the additional variable

(country risk) is constant within the country

6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models

Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score

model for all data The variation in the ratios is significant as shown by the standard deviation

and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-

failed firms are close to each other indicating a symmetry of distributions However this is not

32

the case for the failed firms For the faileddistressed firms the median exceeds the mean for

these three ratios indicating negatively skewed distributions For X4 (BVETD) the means

significantly exceed the median for both failed and non-failed firms indicating a positively

skewed distribution For each of the four variables both the mean and the median are higher for

non-failed firms than for failed firms which is consistent with the expectations The difference

between the means of the non-failed and failed firms is larger in the original USUS data than in

our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is

approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of

EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may

indicate lower classification accuracy than in the original sample

(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates

(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the

large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model

Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same

statistical method or MDA The coefficients here are negative because the models are estimated

using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight

than in the original USUS data while the weights of WCTA and BVETD have proportionally

decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the

logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR

model These coefficients are directly comparable with those of the MDA model which isas

expected for the this exceptionally large sample The differences in the coefficients of the original

33

four variables among the eight LR-modelLR models (Models 1-8) are small For each model the

coefficient of BVETD is very close to zero This indicatinges that the original four variables and

the additional variables are quite independent of each other

(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-

modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate

that after 2007 (the base category) this risk of failure significantly decreased year by year The

base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due

to as a result of the global financial crisis The crisis played a significant role in the failure of key

businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The

effects were especially felt in Europe The coefficients of Model 4 for the size variables show that

the contribution of size on to the logit (risk measure) is atreaches its maximum value when

logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model

5 confirms the riskiness of young firms because the risk of failure is very high for newly founded

firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The

coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky

industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is

statistically significant (because of the large sample) but negative and very close to zero Finally

the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7

62 All data performance of the Zrsquorsquo-Score models

34

Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1

refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of

all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average

accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)

Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its

performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The

lower part of the table shows the AUCs for the countries included only in the test data The

performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)

samples It is also low and for the liquidation firms in the United Kingdom

The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5

presents the differences of these medians between the non-failed and failed firms by country

This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of

medians This correlation which is high for each financial ratio showing that the effects of the

ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large

except for EBITTA which This implies with the exceptionally high AUC that the ST firms

systematically differ from the non-ST firms although the differences are not extremely large The

differences between the medians are very large in Poland for each ratio justifying the high AUC

They are also large and in Finland and in the Czech Republic where the difference in EBITTA is

average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in

all four ratios are below the average a result thatwhich is obviously associated with a low AUC

35

In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even

negative In Iceland and Ireland the differences only in EBITTA are exceptionally small

(Table 4 here)

(Table 5 here)

Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by

MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)

supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is

at approximately the average level The re-estimation of the coefficients has led to improved

classification accuracy in a number of countries especially in Bosnia China (ST) Norway and

Greece However it has impaired the classification accuracy in the UK and China (delisted)

Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-

modelLR model) showing the effect of the estimation method For the benchmark model the

AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between

among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR

model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country

This result was expected because the coefficients of the models are directly comparable The

similar results for the models may also indicate that the independent variables conform to

multinormality This last latter result confirms what most researchers in the field of default

classification models have concluded that the accuracy levels of MDA and logistical regression

36

models are extremely similar despite any statistical violations such as normality between MDA

and logistical regression models

Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)

than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects

are not positive for all countries The effects are positive for example for Russia Estonia

Germany Ireland and Latvia but these effects are not statistically significant There are

statistically significant negative effects for China (ST) and Serbia There are notable negative

effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are

due to the exceptional annual distributions of failed firms in these countries For the countries

with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the

countries with positive effects it is only a few percent For each group non-failed firms are quite

equally distributed over years When the coefficient of D3 is very low (-0666) it strongly

decreases the risk estimates of most failed firms in the former countries but of only a few failed

firms in the latter countries This which leads to the observed effects H3 is supported by

the level of the whole sample However in some country samples (at the level of individual

levelies) H3 is not supported

Model 4 (LR-modelLR model with size variables) performs better than the benchmark model

which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads

and to significant improvements in the AUC for eg China (delisted) and Austria For China

(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC

37

also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs

in a few countries but thise decrease is not significant Model 4 is based on the four original

variables and the size effect following a second-order parabola This kind of size effect is very

small for micro firms but increases when approaching its maximum value in middle-sized firms

The countries with a positive change in AUC typically have data where in which the percent of

failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized

failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In

contrast the countries with a negative change in AUC have data where in which the percentage

of failed micro firms is exceptionally high (70-80)

Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly

the same AUC as the benchmark model (AUC = 0748) However the difference is positive and

statistically significant giving at least marginal support to H5 (age effect) For nearly all countries

the effect of the age on AUC is small For Austria however thise effect is however positive and

significant For Austria the percentage of non-failed young firms (less than 6 years) among non-

failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed

firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong

positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only

a few non-failed firms which This obviously leads to an improvement in AUC

Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC

supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its

38

AUC is notably higher than the benchmark AUC only in only a few countries such asfor example

France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg

restaurants and hotels and the information technology industryies but it has a positive effect

for on the construction and manufacturing industries For the countries with a positive effect on

AUC the percent of non-failed firms in restaurants and hotels and information technology is

high while that of failed firms is low For the risky industries (construction and manufacturing)

these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many

failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)

firms Therefore the AUC will be increasesd This situation is reversed for the countries with a

negative effect on AUC (Austria and Slovenia) The samples of these countries include a high

percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC

Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher

classification performance (AUC = 0749) than the benchmark model This result gives only very

weak support for H7 (country of origin effect) However for each country the resultant AUC is

nearly identical to that given by the benchmark model This result was expected due to the

negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7

This result implies that country risk hasdoes not have noany effect on the boundary between

bankrupt and non-bankrupt firms

Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional

variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to

39

the benchmark AUC in all test data However the effect on the AUCs largely varies and is either

negative or positive in different countries The positive effect is large in several countries such

as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative

effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show

that the inclusion of additional variables in the original model will usually increase the AUC but

not in every country

The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with

the USUS firms being unlike the majority of other firms in this study being listed or delisted

companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese

firm samples is associated with very small differences between the medians of the non-failed and

failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable

with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed

firms is very high Prior studies based on Chinese ST -firms have also demonstrated good

predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for

additional research and modeling work regarding unlisted and delisted Chinese firms

63 Country-level data performance of the Zrsquorsquo-Score models

The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform

all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the

different models estimated for each country separately (country-level models) In this table the

ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated

40

from the country data this benchmark will beis clearly outperformed by the resulting MDA

(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and

Sweden) However these results give only weak support for H1 at the country level because the

effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and

2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs

given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed

by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation

method)

Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC

than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3

leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United

StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in

nearly every country supporting H4 This improvement is significant however only in the United

StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark

model but for which it is only 0710 for the benchmark model It This improvement is also notable

in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age

category dummies) gives higher AUCs than the benchmark model for several countries but none

of the improvements are significant The positive effect is particularly strong especially in

Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only

weakly supports H5 because these negative effects are relatively small

41

Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects

on AUCs when compared with the benchmark However the only significant effect is the negative

effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the

industry dummies However there are notable positive effects on the AUC in for example

France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak

support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in

AUC compared with the benchmark model in Colombia Iceland and the Unites States It also

leads and to notable improvements in AUC at least in Belgium Denmark Estonia France

Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are

found only in Austria and Slovenia Thus as a conclusion we can concludestate that the

classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can

remarkably increases in most countries when different effects are taken into account by

additional variables This increase is found in most European countries and in Colombia and the

United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-

modelLR model and it can only be slightly improved only slightly by additional variables

(Table 6 here)

7 Summary of the study and a suggested extension

The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model

originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is

assessed using a very large international data set We test how the original version of the Zrsquorsquo-

42

Score model performs in different countries and how re-estimations using another statistical

method and different additional variables affect the classification performance when the data

are very heterogeneous For this kind of testing seven research hypotheses on classification

performance are extractedformulated These research hypotheses are tested for all data and

separately for country data (country-level analysis) The estimation data are from 31 countries

and the results are validated for 34 countries The countries are mainly from Europe but three

non-European countries are included (China Colombia and the United StatesUS) The statuses

used in the classification are mainly bankruptcyactive but receivership firms are also considered

to have failed In the Chinese data ST (special treatment) and delisted firms are also separately

analyzed as failed firms

The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very

satisfactorily in an international context The effects of the four financial ratios on performance

are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small

contribution in re-estimation The original model performs very well in several countries such as

Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only

marginally improved the classification performance thus weakly supporting the obsolescence

hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust

across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion

holds for the re-estimation of the model using LRA because the performance results are very

similar with to those of MDA (H2) The use of additional variables in the model generally

improvesd the classification accuracy of the original model but the results for countries are

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

10

3 Literature review

We searched for papers published from the year 2000 onward 4 in prominent international

journals or and books We selected 31 articles in which the Z-Score was either used either as a

failure prediction proxy or was assessed mostly in terms of predictive ability Of the 31 studies

Altmanrsquos Z-Score model5 was used in 16 cases as the measure of distress or of financial strength

In 13 studies Altmanrsquos original model was modified and (or) verified including re-estimation and

in 2 cases it was used solely for the robustness check As Pindado et al (2008) noted the Z-Score

was also used for other purposes such as the evaluation of the costs and benefits of covenants

in bonds the choice of the type of debt type (bank vs non-bank private or public) and the

relationship between investment and internal funds We focused on this part of the literature

that verified and (or) modified Altmanrsquos original model The wide broad usage of the Z-Score

model foras a measuringe of financial distress and performingas well as for the robustness checks

indicates that it is acceptabilityed as a reasonable simple and consistent measure of the

distressed firms

In the case of the modification of the The most common modification to the Z-Score model

model the most common change was the use of other estimation techniques or country-specific

data The use of Altmanrsquos ratios in combination with techniques other than MDA has improved

the its prediction capability TAdditionally the application of new data of on both US and non-US

4 Research devoted to the application of the Z-Score model before 2000 was reviewed by Grice and Ingram (2001) 5 Most of the models focused on stock- exchange- listed firms thus the Zrsquo-Score and Zrsquorsquo-Score models were not used

11

firms also improved model performance Kwak et al (2005) applied used Multiple Criteria Linear

Programming (MCLP) to model 5 Altman and 9 Ohlson variables They used with data on US

bankrupt US firms from 1992 to 1998 and nearly 6 times more matched US control firms The

MCLP approach performed better than Altmanrsquos original model and gave results similar to or

better than those of Ohlsonrsquos original model The original models were not recalculated and the

authors referred solely to their original prediction rates only Merkevicius et al (2006) using data

on US and Lithuanian firms developed a hybrid artificial -discriminantte model combining MDA

and an unsupervised learning artificial neural network (called the SOM-Altman model) with the

use of data on US and Lithuanian firms Thise hybrid SOM-Altman model reached a high

prediction rate of 9235 Xu and Zhang (2009) applied Altmanrsquos Z-score Ohlsonrsquos O-score and

Mertonrsquos distance-to-default (D-score) models to Japanese firms to check whether these models

are useful for bankruptcy prediction in Japan They also ldquomergedrdquo theose models into a new C-

score model They then introduced variables unique to Japan to check whether corporate

structure variables have any impact on the probability of bankruptcy t They called this the model

X-score model These two models were useful for Japanese firms in predicting bankruptcy

prediction but the market-based model was the most successful In summary the developed C-

score and X-score (with country-specific variables) models improved bankruptcy prediction

Tinoco and Wilson (2013) used the original Z-Score as one of the benchmarks to assess the

performance of their model developed for UK listed companies with combined accounting

market and macroeconomic data Altmanrsquos Z-Score presented very good classification accuracy

in the case of financially distressed firms (81 vs 87 for the new model) however it was less

correct for financial health prediction Another modification was introduced by Lyandres and

12

Zhdanov (2013) who posed the question of whether the inclusion of variables related to

investment opportunities improved the predictive power of three models (Altmanrsquos Z-score

model and Zmijewskirsquos and Shumwayrsquos models) They used three proxies for investment

opportunities (market-to-book value-to-book and RampD-to-assets) The measures of investment

opportunities were linked to the likelihood of default The inclusion of either of these measures

improved the out-of-sample forecasting ability of all three

The verification of Altmanrsquos model concentrated on its efficacy or on how it compares a

comparison with other accounting-based market-based or hazard models However although

the original Z-Score model was not solely an accounting-based approach because the market

value of equity was utilized we classify it here as an accounting -based approachbecause the

market value of equity was utilized Grice and Ingram (2001) used a new novel data set of US

firms and posed three questions about the efficacy of Altmanrsquos model They concludinged that

the prediction accuracy of Altmanrsquos model had declined over time and that the coefficients of

the model had significantly changed which means that the relation between the financial ratios

and the signs of financial distress had changed over time The model was sensitive to industry

classification (more efficient for manufacturing firms than for non-manufacturing firms) but was

not sensitive to the type of financial distress Similar conclusions were drawn by Grice and Dugan

(2003) regarding Ohlsonrsquos (1980) and Zmijewskirsquos (1984) models

Hillegeist et al (2004) compared Altmanrsquos Z-score and Ohlsonrsquos O-score (with original and

updated coefficients) with the model based on BSM option pricing (BSM-Prob) They used

13

relative information content tests to compare the out-of-sample performance BSM-Prob

outperformed accounting-based models The conclusions were robust to various modifications

of accounting-based models such as updated coefficients industry effects and the separation

of variables Chava and Jarrow (2004) employed an extended bankruptcy database of US listed

firms to prove the superiority of Shumwayrsquos model (2001) over Altmanrsquos (1968) and Zmijewskirsquos

(1984) models The authors re-estimated the models over the 1962ndash1990 period and forecasted

bankruptcies over the 1991ndash1999 period In the case of Shumwayrsquos model 744 (in the first

decile) of the bankruptcies were correctly identified for with Altmanrsquos model it was 632 and

for with Zmijewskirsquos model it was 432 Shumwayrsquos market-based model also outperformed

accounting-based models in terms of the ROC curve (091)

Reisz and Perlich (2007) used the barrier option for bankruptcy prediction and compared its

discriminatory power with other market-based models and Altmanrsquos Z-Score and Zrdquo-Score The

dataset covered nearly 6000 industrial firms over the period 1988ndash2002 period The authors

proved the domination superiority of Altmanrsquos Z-Score and Zrdquo-Score models for short-term (up

to 1 year) bankruptcy prediction For medium- and long-term bankruptcy prediction their barrier

option model outperformed the other models Pindado et al (2008) developed an ex -ante model

for the estimation of financial distress likelihood (FDL) using awith the use of panel data

methodology and they presented a financially (not legally) based definition of distress Their

sample covered 1583 US companies and 2250 companies from other G-7 countries for the

period 1990ndash2002 period They used a re-estimated Z-Score as a benchmark The FDL model

outperformed the Z-Score model in terms of stability and classification power for different

14

countries and time periods In the case of the re-estimated Z-Score model only profitability and

retained earnings kept maintained their significance for different years and countries

Wu et al (2010) evaluated the performance of five models (Altman 1968 Ohlson 1980

Zmijewski 1984 Shumway 2001 and Hillegeist et al 2004) using an up-to-date data set for

US listed firms Based on these models the authors built their own integrated model that is a

multi-period logit model with an expanded set of variables The integrated model which

combined accounting and market data as well as firmsrsquo characteristics outperformed the other

models Altmanrsquos Z-score performed poorly in compared withison to the other 4 models

Shumwayrsquos model performed best Hillegeist et alrsquos model performed adequately and Ohlsonrsquos

and Zmijewskirsquos models performed adequately although their performance deteriorated over

time

Jackson and Wood (2013) tested 13 different models of bankruptcy prediction and assessed their

efficacy using the ROC curve They selected three single- variable models three accounting-based

models (including Altmanrsquos Z-Score) in two versions (with up-dated coefficients and a neural

network approach) and four contingent claims models The contingent claims models and the

latter group outperformed the other models The best four best models were contingent claims

models based on European call and barrier options Although tThe predictive performance

improved withIn the case of the application of the neural network to accounting-based models

the predictive ability improved but it was still lower than within the case of the market-based

models

15

Acosta-Gonzaacutelez and Fernaacutendez-Rodriacuteguez (2014) used genetic algorithms with the Schwarz

information criterion (GASIC) for variable selection combined with the logit model for bankruptcy

prediction Altmanrsquos Z-Score model was used as one of two benchmarks for the authorsrsquo model

evaluation For one- step- ahead forecasting Altmanrsquos model was better in at predicting failed

firms but the type II error was high For two- and three- steps- ahead forecasts the performance

of the models was similar forin terms of failed firms but forin the case of non-failed firms and

the prediction accuracy of Altmanrsquos model was worse For four- steps- ahead forecasts the GASIC

model outperformed the other models forin the case of failed firms and but it performedwas

comparablye forin the case of non-failed firms

In general Altmanrsquos Z-Score model underperformed in compared withison to market-based

models but there was also evidence indicated that it performed well for short-term distress

prediction The question of whether market-based models are perform better than accounting-

based models has been raised many times (eg discussion in Das et al 2009 Bauer and d

Agarwal 2014) Our purpose is not to contribute to this strand of research but rather to focus on

the accounting-based approach In this study we analyze primarily analyze privately held firms

so there are by definition no market data for these firms In this case aAn accounting-based

approach is the only solution that could be appliedapplicable in this case by banks as lenders or

by investors holding debt securities of firms not listed on the stock exchange Thus far most of

the studies have concentrated on the US market only a few of them have used data from other

16

countries such as Japan the UK Lithuania or the G-7 countries Our analysis is a significant

extension of the previous research

4 Methodology and research hypotheses

The literature survey shows that the Z-Score model (publicly traded firms) the Zrsquo-Score model

(private manufacturing firms) and the Zrsquorsquo-Score model (private and publicly

traded manufacturing and non-manufacturing firms) have been adapted for different purposes

In this study we are first interested first in assessing the performance of the original Zrsquorsquo-Score

model in classifying bankrupt and non-bankrupt firms in an international context with special

focus on the European market However we also validate the results in a set of non-European

countries to generalize the results for circumstances outside Europe Second we will re-estimate

the model using extensive international data and then use the re-estimated Zrsquorsquo-Score model as a

benchmark for assessing the effects of different factors on the modelrsquos performance in terms of

classification accuracy We will assess the effects of the following five factors on this

performance year of bankruptcy size of firms age of firms industry and country of origin WIn

all we will test a set of hypotheses based on the effects of the model on performance on two

different levels First we will test a set of hypotheses on a pooled set of all firms and second on

data from each country individually CIn compared withison to the previous research our this

studyrsquos contribution is the focus on an international context not just simply the model

application or re-estimation for of a given countryrsquos data Because weour aim is focusing on the

performance of the Zrsquorsquo-Score mModel when using ain large body of international data the

research hypotheses are of a technical nature and are presented given as follows

17

H1 Obsolescence of the coefficients The Zrsquorsquo-Score mModel was originally estimated using the

same sample of firms that was used to develop the original Z-Score mModel The bankruptcies

in the estimation data occurred during the period 1946ndash-1965 period Thus the oldest

observations are from nearly seventy years ago during the post-war period Altman (1983)

recommended utilizing data as near to the present as possible when developing a bankruptcy

prediction model It is obvious that firmsrsquo the financial behavior of firms and their business

environment have significantly changed since then Thereforen potentially making the

importance of the financial ratios as reflected by the coefficients of the model may differ from

their original importance reflected by the coefficients of the model Therefore we suggest as

the first hypothesis (H1) that the re-estimation of the coefficients of the four original variables

of the Zrsquorsquo-Score model will improve the classification performance of the model in an

international context This hypothesis is supported by the previous research (eg Grice and

Ingram 2001) and by practice H1 aims to support this evidence on the international level and is

expressed as follows

H1 RThe re-estimatingon of the coefficients of the Zrsquorsquo-Score model improves the its

classification accuracy of the model

H2 Method of estimation The original Zrsquorsquo-Score model has been estimated using MDA However

MDA is based on the ordinary least squares (OLS) method and thus requires assumptions of

multinormality homoscedasticity and linearity which are not often met in empirical financial

ratio analysis Therefore we re-estimate the Zrsquorsquo-Score model using logistic regression analysis

(LRA) to assess the effect of the method of estimation method LRA does not require most of the

18

restricting assumptions of MDA In LRA the multivariate normality of the independent variables

is not required nor are homoscedasticity and linearity For the sake of OLS MDA can be more

useful than LRA for small samples such as the original sample of 66 firms used in the estimation

of the Zrsquorsquo-Score mModel However in a large sample LRA may potentially perform better In this

study we use large samples which is advantageous for LRA Therefore our second hypothesis

(H2) suggests is that the classification performance of the re-estimated Zrsquorsquo-Score model will be

improved when it is estimated using LRA instead of MDA The model re-estimated for the original

variables using LRA and all pooled data is called here the Zrsquorsquo-Score LR-modelLR model The

performance of this re-estimated model is used in this study as the benchmark for further

analyses Thus the second hypothesis is as follows

H2 The prediction accuracy of the logistic regression version of the Zrsquorsquo-Score model is higher

than that of the multiple discriminant analysis version

H3 Bankruptcy year The model based on the relationship between bankruptcy and financial

ratios is likely to be affected by the macroeconomic environment These effects may significantly

decrease the classification accuracy of the model If the model is estimated using data from one

year and it will be applied to data from another year the validity of the model can be questioned

Business cycles inIn terms of economic growth credit policy and interest rates business cycles

can ahave an effect on the boundary between bankrupt and non-bankrupt firms The original Zrsquorsquo-

Score model is estimated using data from the period 1946ndash1965 period which includes several

business cycles Therefore the model is not focused on any specific stage of a cycle and does not

explicitly take into account of the bankruptcy year Altman (1983) suggested gathering data from

19

firms for the last most recent few years when developing a prediction model In this study the

benchmark Zrsquorsquo-Score LR -model will beis estimated for a shorter period than in the original

estimation although it covers several recent years on for different stages of the business cycle in

different countries Therefore the third hypothesis (H3) assumes is that the classification

accuracy of the benchmark model can be increased by explicitly taking into account of the year

of bankruptcy in the estimation of the model estimation The third hypothesis is presented as

follows

H3 The prediction accuracy of the modelrsquos prediction accuracy is higher whenith the

effect of the year of bankruptcy is included is higher than that of the model without the

effect

H4 Size of the firm The boundary between bankrupt and non-bankrupt firms is different for small

and larger firms which decreases the performance of the model estimation when estimated for

data from one size category and isare applied to for data from another size category For the

bankrupt and non-bankrupt firms in the original data for Zrsquorsquo-Score model estimation the range

of asset sizes was ranged between approximately 1 and 25 million US dollars The data did not

include very small or very large firms Altman (1983) regarded the suitability of the original Z-

Score model (and likewisein the same way the Zrsquorsquo-Score model) for all firms as debatable

because of this omission it did not scrutinize very large or very small firms In the currentis study

the benchmark Zrsquorsquo-Score LR -model will beis estimated for data from many size categories from

very small firms to very large firms The fourth hypothesis (H4) assumes that the classification

performance of the uniform benchmark LR -model based on the original four financial variables

20

of the Zrsquorsquo-Score model is improved when the size category of the firm is explicitly taken into

account Thus we present the fourth hypothesis as follows

H4 The modelrsquos prediction accuracy of the modelis higher whenith the effect of size is

includede is higher than that of the model without this effect

H5 Age of the firm International insolvency statistics generally show that bankruptcy risk is a

function of the age of the firm Very young firms typically show a very high risk The original Zrsquorsquo-

Score model does not explicitly take age into account age However Altman (1983) noted that

the age of a firm is implicitly considered in the Retained EarningsTotal Assets ratio (X2) which

was regarded as a new ratio in the bankruptcy prediction context A relatively young firm will

probably show a low ratio because it has not had time to build up cumulative profits Thus a

young firm is to some degree discriminated against in the model and its likelihood of being

classified as bankrupt is relatively higher than that of an older firm The incidence of failure is

much higher in the early years of a firm AlthoughDespite that the age of the firm in this way is

implicitly taken into account in X2 we expect that an explicit consideration of the age will improve

the classification accuracy bydue to controlling for the age factor Therefore the fifth hypothesis

(H5) proposes that the performance of the uniform benchmark model based on the original four

financial variables of the Zrsquorsquo-Score model is increasesd when the age of the firm is explicitly taken

into account The fifth hypothesis is can be presented as follows

H5 The modelrsquos prediction accuracy of the model is higher when with the effect of firm

age is higher than that of the model without this effectincluded

21

H6 Industry of the firm The original Zrsquo-Score model was estimated only for manufacturing firms

Altman (1983) stated that it would be ideal to develop a bankruptcy prediction model utilizing a

homogenous group of bankrupt firms If we are interested in a particular industry grouping we

should gather data from bankrupt and non-bankrupt firms in these groupings Previous studies

show that financial distress analysis is influenced by the industry effect (Smith and Liou 2007)

Firms in different industries tend to report different levels of the same financial ratios which may

have an effect on the boundary between bankrupt and non-bankrupt firms This industry effect

may be present in the Zrsquo-Score model especially due to the SalesTotal Assets ratio (X5) This ratio

which showed the least significance on a univariate basis while making but it had made a very

significant contribution to the discriminant power of the multivariate model Altman (1983)

recognized the potential industry effect due to a the wide variation among industries in asset

turnover and specified the Zrsquorsquo-Score model without X5 However also the Zrsquorsquo-Score model was

also estimated using the original sample of manufacturing firms In our analysis the uniform

benchmark model based on the original four financial variables of the Zrsquorsquo-Score model is

estimated for a statistical sample representing different industries Therefore the sixth

hypothesis (H6) assumes that an explicit consideration of industry will improve the classification

accuracy of this benchmark model H6It can be expressed in the following form

H6 The modelrsquos prediction accuracy of the modelis higher whenith the effect of industry is

higher than that of the model without this effectincluded

H7 Country of origin The original Zrsquorsquo-Score model has been estimated only for USUS firms It

can be expected that the international applicability of the model to other countries is affected by

22

country-specific differences The eEconomic environment legislation culture financial markets

and accounting practices in a country may affect the financial behavior of firms and the boundary

between bankrupt and non-bankrupt firms These factors may potentially weaken the

classification performance of the model in for other countries outside other than that e one fore

country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the

seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm

into account will improve the classification accuracy of the benchmark model In our empirical

study the country effect will beis assessed by including a variable of for country risk Therefore

the seventh hypothesis is presented as follows

H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country

risk is higher than that of the model without this effectincluded

5 Empirical data and statistical methods

51 Sample of firms

The principal data of for this study are were extracted from the ORBIS databases of Bureau Van

Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the

moment of sampling contained administrative information on more than 50 million European

firms However income statement and balance sheet information was available for

approximately 8 million companies More than 99 of the companies covered in this database

are private companies from different various industries justifying the use of the Zrsquorsquo-Score model

instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all

industrial groupings and for both private and public entities (Altman 1983 124 Altman and

Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope

23

of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all

industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly

used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that

international comparability may be a problem when administrative firm-level data are

internationally pooled For administrative data Although the definition of variables is usually less

harmonized for administrative data tThis is less of a problem in the ORBIS database because of

the common international format of balance sheets For example although some discrepancies

in profitloss statements may arise because of differences in fiscal systems across countries

balance sheet variables largely adhere to international standards

AThere are a number of factors that influence the international applicability of bankruptcy

prediction models These factors include accounting legislation and practice creditor rights and

investor protection judicial efficiency corporate governance bankruptcy protection and

insolvency management and firm risk-taking These factors strongly differ between European

and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-

Score model outside Europe First it is especiallyparticularly important to include the USUS as

because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It

is include d in our study as well because it has the largest market capitalization in the world

Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it

applicable to emerging market companies Therefore we include firms from China and Colombia

which represent two very culturally and institutionally very dissimilar emerging market

countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt

24

firms ie the limit we set for European countries) from ORBIS World were not

available Thus the results are also estimated and tested for three non-European countries (the

United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-

Score modelrsquos performance The samples of firms from these countries are were extracted from

ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms

from around the world

SFor statistical sampling several requirements are set for the statistical sampling of the empirical

data First we require that the firm to be selected be an industrial (non-financial) company

Second its owners must have limited liability (so partnerships and sole proprietors are left out

of the study) Third we set a minimum requirement for the size of the firm Because the financial

ratios of very small firms are generally too unstable for a failure prediction model these firms

are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have

exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we

include in our estimation sample firms from all European countries and 3 pre-selected non-

European countries where the number of failed firms is more greater than 60 If the number of

failed firms for any European country is less than 60 the firms from this country are only included

only in the test sample For qualifying European countries the firms are randomly classified in

the estimation and test samples so that the number of firms is approximately equal in both

samples Thus our estimation data include firms from 28 European and 3 non-European

countries Fifth all failed firms that fulfill the above requirements are included in our samples

However if the number of non-failed firms in a country is very high a sample is randomly

25

selected from that country Finally the time span of fiscal years potentially available for this study

ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in

the database are from a financial period within 2007 and 2010 earlier years are also excluded

for non-failed firms for comparability All qualifying observations of non-failed firms from the

years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses

of failed firms to the last most recent financial statements available before failure The four

independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize

outliers

Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test

data by country The estimation sample includes data from 2602563 non-failed and 38215

failed firms from 28 European and 3 non-European countries The test sample is slightly larger

because it includes data from 31 European and 3 non-European countries From For the country

of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt

firms The available US data that were available consist only of listed (and delisted) firms From

China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are

included in the estimation sample although there are only 32 such firms altogether7 The Chinese

datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status

are separately analyzed separately only in for the test data8 ST firms are listed firms suffering

6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor

26

from serious financial difficulties Excluding the special US data and the two non-private Chinese

datasets 994 of the observations in the data are private firms

(Table 1 here)

52 Status of failed firms

ORBIS has five classes for potentially active firms (active default of payment receivership

dormant and branch) and seven classes for inactive firms that no longer carry out business

activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation

branch and no precision) From Among these classes only active is selected to represent non-

distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by

considering (with exceptions described below) a firm failed if its status in ORBIS is stated as

bankruptcy However because of the small number of bankrupt firms in some countries we also

consider receivership (active) firms as failed even if they are active These firms generally suffer

from serious financial distress However firms in liquidation are generally not included in the

sample of failed firms Firms in liquidation may depending on the country contain firms that

have ceased activities due to reasons other than failure (mergers discontinuing the operations

of a daughter company or of a foreign branch etc) Therefore for most countries we select only

firms that are coded as being bankrupt or under receivership However there are a number of

special cases where failed firms are coded under a different status heading These countries or

samples are the following

Country Status categories Bulgaria In liquidation Bankruptcy

27

Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the

study (for example Switzerland) If a country had only a very small number of failed firms it was

dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries

including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status

classes (including the bankruptcy category) are not completely homogenous within European

countries due to different legislations although there are obvious similarities in insolvency acts

(Philippe et al 2002) China is a special case because it includes samples with three different

criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are

two different samples (liquidation and receivership)

53 Statistical methods

In this study seven research hypotheses are drawn for statistical testing The statistical analysis

will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The

classification performance of the original model is assessed by the AUC (Area Under Curve)

measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close

connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random

28

model and 05 for a model with an average classification performance In SAS software is used

for all statistical analysess SAS software is used

The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is

tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical

method (multiple discriminant analysis or MDA) The problem is that the estimation sample

includes different numbers of failed and non-failed firms from 31 countries In the original Z-

Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from

the USUS Following the characteristics of these data we weight the firms so that the weights

for the failed and non-failed firms are equally In so doingthis way the non-proportional

sampling in from different countries does will not affect the re-estimated model The number of

firms from different countries however varies significantly leading to greater weights for larger

countries To avoid this problem the observations are also weighted so that each country has an

equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using

these weighted data and the resulting AUC is compared with that based on the original model

The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score

model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is

based on less- restrictive statistical assumptions than MDA In this estimation the dependent

variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA

does not require that independent variables be multivariate normal or that groups have equal

covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA

29

creates a score (logit) score L for every firm It is assumed that the independent variables are

linearly related to L This score or logit is used to determine the conditional probability of failure

as follows

= (eq 4)

where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the

original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing

the statistical significance of the difference between AUCs for this LR model and for the re-

estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it

is used as a benchmark for further statistical AUC comparisons because LR is applied as the

principal method in the testing of the remaining research hypotheses

The third hypothesis (H3) is associated with the performance effect of taking account of the

bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on

the following logit

(eq 5)

where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model

bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and

D1 = 1 when year = 2008 0 otherwise

D2 = 1 when year = 2009 0 otherwise

D3 = 1 when year = 2010 0 otherwise

)|1( XYp )( 441101

1

1

1XbXbbL ee

3

1

4

10

jjji

ii DcXbbL

30

The dummy variables do not directly refer to the bankruptcy year which that is not given in the

data basedatabase but rather to the last available year before bankruptcy For failed firms there

is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is

the base category If the AUC of this extended LR model statistically significantly exceeds the AUC

of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3

Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third

hypothesis above However for each hypothesis appropriate variables are used instead of the

year dummies Hypothesis H4 addresses the performance effect of taking size into account the

size and it will beis tested using by adding two additional variables measuring firm size in to the

LR -model In this LR model size is measured by the natural logarithm of total assets and its

squared form In this way the effect of logarithmic size can be reflected by in a function following

the second-order parabola Hypothesis H5 predicts that the classification performance improves

when the age of the firm is explicitly taken into account When testing this hypothesis the

category 6ndash14 years category is used as the base category and two dummy variables are

incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis

H6 looks at whether is associated with the classification performance is affected bythe effect of

the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt

is tested here using dummy variables for seven industries (D1 restaurants and hotels D2

construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and

water production D7 information technology) with all other industries acting as the base

category

31

Hypothesis H7 predicts that the explicit consideration of the country of origin will improves

classification performance This hypothesis is tested by using country risk measures instead ofnot

using dummy variables for countries but using country risk measures instead The country risk of

each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the

annual closing of accounts The rating is numerically recoded such that the best rating AAA

equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating

D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial

ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-

modelLR models with the original four financial ratios and the additional variables specified in

the hypotheses are estimated for all data In addition an LR model including all additional

variables is estimated for all data to assess the simultaneous effect of all variables Finally six of

the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not

included in this country- level testing hypothesis H7 is not tested because the additional variable

(country risk) is constant within the country

6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models

Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score

model for all data The variation in the ratios is significant as shown by the standard deviation

and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-

failed firms are close to each other indicating a symmetry of distributions However this is not

32

the case for the failed firms For the faileddistressed firms the median exceeds the mean for

these three ratios indicating negatively skewed distributions For X4 (BVETD) the means

significantly exceed the median for both failed and non-failed firms indicating a positively

skewed distribution For each of the four variables both the mean and the median are higher for

non-failed firms than for failed firms which is consistent with the expectations The difference

between the means of the non-failed and failed firms is larger in the original USUS data than in

our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is

approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of

EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may

indicate lower classification accuracy than in the original sample

(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates

(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the

large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model

Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same

statistical method or MDA The coefficients here are negative because the models are estimated

using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight

than in the original USUS data while the weights of WCTA and BVETD have proportionally

decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the

logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR

model These coefficients are directly comparable with those of the MDA model which isas

expected for the this exceptionally large sample The differences in the coefficients of the original

33

four variables among the eight LR-modelLR models (Models 1-8) are small For each model the

coefficient of BVETD is very close to zero This indicatinges that the original four variables and

the additional variables are quite independent of each other

(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-

modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate

that after 2007 (the base category) this risk of failure significantly decreased year by year The

base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due

to as a result of the global financial crisis The crisis played a significant role in the failure of key

businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The

effects were especially felt in Europe The coefficients of Model 4 for the size variables show that

the contribution of size on to the logit (risk measure) is atreaches its maximum value when

logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model

5 confirms the riskiness of young firms because the risk of failure is very high for newly founded

firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The

coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky

industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is

statistically significant (because of the large sample) but negative and very close to zero Finally

the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7

62 All data performance of the Zrsquorsquo-Score models

34

Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1

refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of

all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average

accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)

Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its

performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The

lower part of the table shows the AUCs for the countries included only in the test data The

performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)

samples It is also low and for the liquidation firms in the United Kingdom

The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5

presents the differences of these medians between the non-failed and failed firms by country

This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of

medians This correlation which is high for each financial ratio showing that the effects of the

ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large

except for EBITTA which This implies with the exceptionally high AUC that the ST firms

systematically differ from the non-ST firms although the differences are not extremely large The

differences between the medians are very large in Poland for each ratio justifying the high AUC

They are also large and in Finland and in the Czech Republic where the difference in EBITTA is

average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in

all four ratios are below the average a result thatwhich is obviously associated with a low AUC

35

In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even

negative In Iceland and Ireland the differences only in EBITTA are exceptionally small

(Table 4 here)

(Table 5 here)

Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by

MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)

supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is

at approximately the average level The re-estimation of the coefficients has led to improved

classification accuracy in a number of countries especially in Bosnia China (ST) Norway and

Greece However it has impaired the classification accuracy in the UK and China (delisted)

Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-

modelLR model) showing the effect of the estimation method For the benchmark model the

AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between

among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR

model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country

This result was expected because the coefficients of the models are directly comparable The

similar results for the models may also indicate that the independent variables conform to

multinormality This last latter result confirms what most researchers in the field of default

classification models have concluded that the accuracy levels of MDA and logistical regression

36

models are extremely similar despite any statistical violations such as normality between MDA

and logistical regression models

Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)

than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects

are not positive for all countries The effects are positive for example for Russia Estonia

Germany Ireland and Latvia but these effects are not statistically significant There are

statistically significant negative effects for China (ST) and Serbia There are notable negative

effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are

due to the exceptional annual distributions of failed firms in these countries For the countries

with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the

countries with positive effects it is only a few percent For each group non-failed firms are quite

equally distributed over years When the coefficient of D3 is very low (-0666) it strongly

decreases the risk estimates of most failed firms in the former countries but of only a few failed

firms in the latter countries This which leads to the observed effects H3 is supported by

the level of the whole sample However in some country samples (at the level of individual

levelies) H3 is not supported

Model 4 (LR-modelLR model with size variables) performs better than the benchmark model

which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads

and to significant improvements in the AUC for eg China (delisted) and Austria For China

(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC

37

also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs

in a few countries but thise decrease is not significant Model 4 is based on the four original

variables and the size effect following a second-order parabola This kind of size effect is very

small for micro firms but increases when approaching its maximum value in middle-sized firms

The countries with a positive change in AUC typically have data where in which the percent of

failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized

failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In

contrast the countries with a negative change in AUC have data where in which the percentage

of failed micro firms is exceptionally high (70-80)

Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly

the same AUC as the benchmark model (AUC = 0748) However the difference is positive and

statistically significant giving at least marginal support to H5 (age effect) For nearly all countries

the effect of the age on AUC is small For Austria however thise effect is however positive and

significant For Austria the percentage of non-failed young firms (less than 6 years) among non-

failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed

firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong

positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only

a few non-failed firms which This obviously leads to an improvement in AUC

Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC

supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its

38

AUC is notably higher than the benchmark AUC only in only a few countries such asfor example

France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg

restaurants and hotels and the information technology industryies but it has a positive effect

for on the construction and manufacturing industries For the countries with a positive effect on

AUC the percent of non-failed firms in restaurants and hotels and information technology is

high while that of failed firms is low For the risky industries (construction and manufacturing)

these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many

failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)

firms Therefore the AUC will be increasesd This situation is reversed for the countries with a

negative effect on AUC (Austria and Slovenia) The samples of these countries include a high

percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC

Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher

classification performance (AUC = 0749) than the benchmark model This result gives only very

weak support for H7 (country of origin effect) However for each country the resultant AUC is

nearly identical to that given by the benchmark model This result was expected due to the

negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7

This result implies that country risk hasdoes not have noany effect on the boundary between

bankrupt and non-bankrupt firms

Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional

variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to

39

the benchmark AUC in all test data However the effect on the AUCs largely varies and is either

negative or positive in different countries The positive effect is large in several countries such

as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative

effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show

that the inclusion of additional variables in the original model will usually increase the AUC but

not in every country

The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with

the USUS firms being unlike the majority of other firms in this study being listed or delisted

companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese

firm samples is associated with very small differences between the medians of the non-failed and

failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable

with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed

firms is very high Prior studies based on Chinese ST -firms have also demonstrated good

predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for

additional research and modeling work regarding unlisted and delisted Chinese firms

63 Country-level data performance of the Zrsquorsquo-Score models

The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform

all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the

different models estimated for each country separately (country-level models) In this table the

ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated

40

from the country data this benchmark will beis clearly outperformed by the resulting MDA

(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and

Sweden) However these results give only weak support for H1 at the country level because the

effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and

2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs

given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed

by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation

method)

Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC

than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3

leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United

StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in

nearly every country supporting H4 This improvement is significant however only in the United

StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark

model but for which it is only 0710 for the benchmark model It This improvement is also notable

in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age

category dummies) gives higher AUCs than the benchmark model for several countries but none

of the improvements are significant The positive effect is particularly strong especially in

Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only

weakly supports H5 because these negative effects are relatively small

41

Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects

on AUCs when compared with the benchmark However the only significant effect is the negative

effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the

industry dummies However there are notable positive effects on the AUC in for example

France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak

support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in

AUC compared with the benchmark model in Colombia Iceland and the Unites States It also

leads and to notable improvements in AUC at least in Belgium Denmark Estonia France

Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are

found only in Austria and Slovenia Thus as a conclusion we can concludestate that the

classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can

remarkably increases in most countries when different effects are taken into account by

additional variables This increase is found in most European countries and in Colombia and the

United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-

modelLR model and it can only be slightly improved only slightly by additional variables

(Table 6 here)

7 Summary of the study and a suggested extension

The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model

originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is

assessed using a very large international data set We test how the original version of the Zrsquorsquo-

42

Score model performs in different countries and how re-estimations using another statistical

method and different additional variables affect the classification performance when the data

are very heterogeneous For this kind of testing seven research hypotheses on classification

performance are extractedformulated These research hypotheses are tested for all data and

separately for country data (country-level analysis) The estimation data are from 31 countries

and the results are validated for 34 countries The countries are mainly from Europe but three

non-European countries are included (China Colombia and the United StatesUS) The statuses

used in the classification are mainly bankruptcyactive but receivership firms are also considered

to have failed In the Chinese data ST (special treatment) and delisted firms are also separately

analyzed as failed firms

The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very

satisfactorily in an international context The effects of the four financial ratios on performance

are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small

contribution in re-estimation The original model performs very well in several countries such as

Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only

marginally improved the classification performance thus weakly supporting the obsolescence

hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust

across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion

holds for the re-estimation of the model using LRA because the performance results are very

similar with to those of MDA (H2) The use of additional variables in the model generally

improvesd the classification accuracy of the original model but the results for countries are

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

11

firms also improved model performance Kwak et al (2005) applied used Multiple Criteria Linear

Programming (MCLP) to model 5 Altman and 9 Ohlson variables They used with data on US

bankrupt US firms from 1992 to 1998 and nearly 6 times more matched US control firms The

MCLP approach performed better than Altmanrsquos original model and gave results similar to or

better than those of Ohlsonrsquos original model The original models were not recalculated and the

authors referred solely to their original prediction rates only Merkevicius et al (2006) using data

on US and Lithuanian firms developed a hybrid artificial -discriminantte model combining MDA

and an unsupervised learning artificial neural network (called the SOM-Altman model) with the

use of data on US and Lithuanian firms Thise hybrid SOM-Altman model reached a high

prediction rate of 9235 Xu and Zhang (2009) applied Altmanrsquos Z-score Ohlsonrsquos O-score and

Mertonrsquos distance-to-default (D-score) models to Japanese firms to check whether these models

are useful for bankruptcy prediction in Japan They also ldquomergedrdquo theose models into a new C-

score model They then introduced variables unique to Japan to check whether corporate

structure variables have any impact on the probability of bankruptcy t They called this the model

X-score model These two models were useful for Japanese firms in predicting bankruptcy

prediction but the market-based model was the most successful In summary the developed C-

score and X-score (with country-specific variables) models improved bankruptcy prediction

Tinoco and Wilson (2013) used the original Z-Score as one of the benchmarks to assess the

performance of their model developed for UK listed companies with combined accounting

market and macroeconomic data Altmanrsquos Z-Score presented very good classification accuracy

in the case of financially distressed firms (81 vs 87 for the new model) however it was less

correct for financial health prediction Another modification was introduced by Lyandres and

12

Zhdanov (2013) who posed the question of whether the inclusion of variables related to

investment opportunities improved the predictive power of three models (Altmanrsquos Z-score

model and Zmijewskirsquos and Shumwayrsquos models) They used three proxies for investment

opportunities (market-to-book value-to-book and RampD-to-assets) The measures of investment

opportunities were linked to the likelihood of default The inclusion of either of these measures

improved the out-of-sample forecasting ability of all three

The verification of Altmanrsquos model concentrated on its efficacy or on how it compares a

comparison with other accounting-based market-based or hazard models However although

the original Z-Score model was not solely an accounting-based approach because the market

value of equity was utilized we classify it here as an accounting -based approachbecause the

market value of equity was utilized Grice and Ingram (2001) used a new novel data set of US

firms and posed three questions about the efficacy of Altmanrsquos model They concludinged that

the prediction accuracy of Altmanrsquos model had declined over time and that the coefficients of

the model had significantly changed which means that the relation between the financial ratios

and the signs of financial distress had changed over time The model was sensitive to industry

classification (more efficient for manufacturing firms than for non-manufacturing firms) but was

not sensitive to the type of financial distress Similar conclusions were drawn by Grice and Dugan

(2003) regarding Ohlsonrsquos (1980) and Zmijewskirsquos (1984) models

Hillegeist et al (2004) compared Altmanrsquos Z-score and Ohlsonrsquos O-score (with original and

updated coefficients) with the model based on BSM option pricing (BSM-Prob) They used

13

relative information content tests to compare the out-of-sample performance BSM-Prob

outperformed accounting-based models The conclusions were robust to various modifications

of accounting-based models such as updated coefficients industry effects and the separation

of variables Chava and Jarrow (2004) employed an extended bankruptcy database of US listed

firms to prove the superiority of Shumwayrsquos model (2001) over Altmanrsquos (1968) and Zmijewskirsquos

(1984) models The authors re-estimated the models over the 1962ndash1990 period and forecasted

bankruptcies over the 1991ndash1999 period In the case of Shumwayrsquos model 744 (in the first

decile) of the bankruptcies were correctly identified for with Altmanrsquos model it was 632 and

for with Zmijewskirsquos model it was 432 Shumwayrsquos market-based model also outperformed

accounting-based models in terms of the ROC curve (091)

Reisz and Perlich (2007) used the barrier option for bankruptcy prediction and compared its

discriminatory power with other market-based models and Altmanrsquos Z-Score and Zrdquo-Score The

dataset covered nearly 6000 industrial firms over the period 1988ndash2002 period The authors

proved the domination superiority of Altmanrsquos Z-Score and Zrdquo-Score models for short-term (up

to 1 year) bankruptcy prediction For medium- and long-term bankruptcy prediction their barrier

option model outperformed the other models Pindado et al (2008) developed an ex -ante model

for the estimation of financial distress likelihood (FDL) using awith the use of panel data

methodology and they presented a financially (not legally) based definition of distress Their

sample covered 1583 US companies and 2250 companies from other G-7 countries for the

period 1990ndash2002 period They used a re-estimated Z-Score as a benchmark The FDL model

outperformed the Z-Score model in terms of stability and classification power for different

14

countries and time periods In the case of the re-estimated Z-Score model only profitability and

retained earnings kept maintained their significance for different years and countries

Wu et al (2010) evaluated the performance of five models (Altman 1968 Ohlson 1980

Zmijewski 1984 Shumway 2001 and Hillegeist et al 2004) using an up-to-date data set for

US listed firms Based on these models the authors built their own integrated model that is a

multi-period logit model with an expanded set of variables The integrated model which

combined accounting and market data as well as firmsrsquo characteristics outperformed the other

models Altmanrsquos Z-score performed poorly in compared withison to the other 4 models

Shumwayrsquos model performed best Hillegeist et alrsquos model performed adequately and Ohlsonrsquos

and Zmijewskirsquos models performed adequately although their performance deteriorated over

time

Jackson and Wood (2013) tested 13 different models of bankruptcy prediction and assessed their

efficacy using the ROC curve They selected three single- variable models three accounting-based

models (including Altmanrsquos Z-Score) in two versions (with up-dated coefficients and a neural

network approach) and four contingent claims models The contingent claims models and the

latter group outperformed the other models The best four best models were contingent claims

models based on European call and barrier options Although tThe predictive performance

improved withIn the case of the application of the neural network to accounting-based models

the predictive ability improved but it was still lower than within the case of the market-based

models

15

Acosta-Gonzaacutelez and Fernaacutendez-Rodriacuteguez (2014) used genetic algorithms with the Schwarz

information criterion (GASIC) for variable selection combined with the logit model for bankruptcy

prediction Altmanrsquos Z-Score model was used as one of two benchmarks for the authorsrsquo model

evaluation For one- step- ahead forecasting Altmanrsquos model was better in at predicting failed

firms but the type II error was high For two- and three- steps- ahead forecasts the performance

of the models was similar forin terms of failed firms but forin the case of non-failed firms and

the prediction accuracy of Altmanrsquos model was worse For four- steps- ahead forecasts the GASIC

model outperformed the other models forin the case of failed firms and but it performedwas

comparablye forin the case of non-failed firms

In general Altmanrsquos Z-Score model underperformed in compared withison to market-based

models but there was also evidence indicated that it performed well for short-term distress

prediction The question of whether market-based models are perform better than accounting-

based models has been raised many times (eg discussion in Das et al 2009 Bauer and d

Agarwal 2014) Our purpose is not to contribute to this strand of research but rather to focus on

the accounting-based approach In this study we analyze primarily analyze privately held firms

so there are by definition no market data for these firms In this case aAn accounting-based

approach is the only solution that could be appliedapplicable in this case by banks as lenders or

by investors holding debt securities of firms not listed on the stock exchange Thus far most of

the studies have concentrated on the US market only a few of them have used data from other

16

countries such as Japan the UK Lithuania or the G-7 countries Our analysis is a significant

extension of the previous research

4 Methodology and research hypotheses

The literature survey shows that the Z-Score model (publicly traded firms) the Zrsquo-Score model

(private manufacturing firms) and the Zrsquorsquo-Score model (private and publicly

traded manufacturing and non-manufacturing firms) have been adapted for different purposes

In this study we are first interested first in assessing the performance of the original Zrsquorsquo-Score

model in classifying bankrupt and non-bankrupt firms in an international context with special

focus on the European market However we also validate the results in a set of non-European

countries to generalize the results for circumstances outside Europe Second we will re-estimate

the model using extensive international data and then use the re-estimated Zrsquorsquo-Score model as a

benchmark for assessing the effects of different factors on the modelrsquos performance in terms of

classification accuracy We will assess the effects of the following five factors on this

performance year of bankruptcy size of firms age of firms industry and country of origin WIn

all we will test a set of hypotheses based on the effects of the model on performance on two

different levels First we will test a set of hypotheses on a pooled set of all firms and second on

data from each country individually CIn compared withison to the previous research our this

studyrsquos contribution is the focus on an international context not just simply the model

application or re-estimation for of a given countryrsquos data Because weour aim is focusing on the

performance of the Zrsquorsquo-Score mModel when using ain large body of international data the

research hypotheses are of a technical nature and are presented given as follows

17

H1 Obsolescence of the coefficients The Zrsquorsquo-Score mModel was originally estimated using the

same sample of firms that was used to develop the original Z-Score mModel The bankruptcies

in the estimation data occurred during the period 1946ndash-1965 period Thus the oldest

observations are from nearly seventy years ago during the post-war period Altman (1983)

recommended utilizing data as near to the present as possible when developing a bankruptcy

prediction model It is obvious that firmsrsquo the financial behavior of firms and their business

environment have significantly changed since then Thereforen potentially making the

importance of the financial ratios as reflected by the coefficients of the model may differ from

their original importance reflected by the coefficients of the model Therefore we suggest as

the first hypothesis (H1) that the re-estimation of the coefficients of the four original variables

of the Zrsquorsquo-Score model will improve the classification performance of the model in an

international context This hypothesis is supported by the previous research (eg Grice and

Ingram 2001) and by practice H1 aims to support this evidence on the international level and is

expressed as follows

H1 RThe re-estimatingon of the coefficients of the Zrsquorsquo-Score model improves the its

classification accuracy of the model

H2 Method of estimation The original Zrsquorsquo-Score model has been estimated using MDA However

MDA is based on the ordinary least squares (OLS) method and thus requires assumptions of

multinormality homoscedasticity and linearity which are not often met in empirical financial

ratio analysis Therefore we re-estimate the Zrsquorsquo-Score model using logistic regression analysis

(LRA) to assess the effect of the method of estimation method LRA does not require most of the

18

restricting assumptions of MDA In LRA the multivariate normality of the independent variables

is not required nor are homoscedasticity and linearity For the sake of OLS MDA can be more

useful than LRA for small samples such as the original sample of 66 firms used in the estimation

of the Zrsquorsquo-Score mModel However in a large sample LRA may potentially perform better In this

study we use large samples which is advantageous for LRA Therefore our second hypothesis

(H2) suggests is that the classification performance of the re-estimated Zrsquorsquo-Score model will be

improved when it is estimated using LRA instead of MDA The model re-estimated for the original

variables using LRA and all pooled data is called here the Zrsquorsquo-Score LR-modelLR model The

performance of this re-estimated model is used in this study as the benchmark for further

analyses Thus the second hypothesis is as follows

H2 The prediction accuracy of the logistic regression version of the Zrsquorsquo-Score model is higher

than that of the multiple discriminant analysis version

H3 Bankruptcy year The model based on the relationship between bankruptcy and financial

ratios is likely to be affected by the macroeconomic environment These effects may significantly

decrease the classification accuracy of the model If the model is estimated using data from one

year and it will be applied to data from another year the validity of the model can be questioned

Business cycles inIn terms of economic growth credit policy and interest rates business cycles

can ahave an effect on the boundary between bankrupt and non-bankrupt firms The original Zrsquorsquo-

Score model is estimated using data from the period 1946ndash1965 period which includes several

business cycles Therefore the model is not focused on any specific stage of a cycle and does not

explicitly take into account of the bankruptcy year Altman (1983) suggested gathering data from

19

firms for the last most recent few years when developing a prediction model In this study the

benchmark Zrsquorsquo-Score LR -model will beis estimated for a shorter period than in the original

estimation although it covers several recent years on for different stages of the business cycle in

different countries Therefore the third hypothesis (H3) assumes is that the classification

accuracy of the benchmark model can be increased by explicitly taking into account of the year

of bankruptcy in the estimation of the model estimation The third hypothesis is presented as

follows

H3 The prediction accuracy of the modelrsquos prediction accuracy is higher whenith the

effect of the year of bankruptcy is included is higher than that of the model without the

effect

H4 Size of the firm The boundary between bankrupt and non-bankrupt firms is different for small

and larger firms which decreases the performance of the model estimation when estimated for

data from one size category and isare applied to for data from another size category For the

bankrupt and non-bankrupt firms in the original data for Zrsquorsquo-Score model estimation the range

of asset sizes was ranged between approximately 1 and 25 million US dollars The data did not

include very small or very large firms Altman (1983) regarded the suitability of the original Z-

Score model (and likewisein the same way the Zrsquorsquo-Score model) for all firms as debatable

because of this omission it did not scrutinize very large or very small firms In the currentis study

the benchmark Zrsquorsquo-Score LR -model will beis estimated for data from many size categories from

very small firms to very large firms The fourth hypothesis (H4) assumes that the classification

performance of the uniform benchmark LR -model based on the original four financial variables

20

of the Zrsquorsquo-Score model is improved when the size category of the firm is explicitly taken into

account Thus we present the fourth hypothesis as follows

H4 The modelrsquos prediction accuracy of the modelis higher whenith the effect of size is

includede is higher than that of the model without this effect

H5 Age of the firm International insolvency statistics generally show that bankruptcy risk is a

function of the age of the firm Very young firms typically show a very high risk The original Zrsquorsquo-

Score model does not explicitly take age into account age However Altman (1983) noted that

the age of a firm is implicitly considered in the Retained EarningsTotal Assets ratio (X2) which

was regarded as a new ratio in the bankruptcy prediction context A relatively young firm will

probably show a low ratio because it has not had time to build up cumulative profits Thus a

young firm is to some degree discriminated against in the model and its likelihood of being

classified as bankrupt is relatively higher than that of an older firm The incidence of failure is

much higher in the early years of a firm AlthoughDespite that the age of the firm in this way is

implicitly taken into account in X2 we expect that an explicit consideration of the age will improve

the classification accuracy bydue to controlling for the age factor Therefore the fifth hypothesis

(H5) proposes that the performance of the uniform benchmark model based on the original four

financial variables of the Zrsquorsquo-Score model is increasesd when the age of the firm is explicitly taken

into account The fifth hypothesis is can be presented as follows

H5 The modelrsquos prediction accuracy of the model is higher when with the effect of firm

age is higher than that of the model without this effectincluded

21

H6 Industry of the firm The original Zrsquo-Score model was estimated only for manufacturing firms

Altman (1983) stated that it would be ideal to develop a bankruptcy prediction model utilizing a

homogenous group of bankrupt firms If we are interested in a particular industry grouping we

should gather data from bankrupt and non-bankrupt firms in these groupings Previous studies

show that financial distress analysis is influenced by the industry effect (Smith and Liou 2007)

Firms in different industries tend to report different levels of the same financial ratios which may

have an effect on the boundary between bankrupt and non-bankrupt firms This industry effect

may be present in the Zrsquo-Score model especially due to the SalesTotal Assets ratio (X5) This ratio

which showed the least significance on a univariate basis while making but it had made a very

significant contribution to the discriminant power of the multivariate model Altman (1983)

recognized the potential industry effect due to a the wide variation among industries in asset

turnover and specified the Zrsquorsquo-Score model without X5 However also the Zrsquorsquo-Score model was

also estimated using the original sample of manufacturing firms In our analysis the uniform

benchmark model based on the original four financial variables of the Zrsquorsquo-Score model is

estimated for a statistical sample representing different industries Therefore the sixth

hypothesis (H6) assumes that an explicit consideration of industry will improve the classification

accuracy of this benchmark model H6It can be expressed in the following form

H6 The modelrsquos prediction accuracy of the modelis higher whenith the effect of industry is

higher than that of the model without this effectincluded

H7 Country of origin The original Zrsquorsquo-Score model has been estimated only for USUS firms It

can be expected that the international applicability of the model to other countries is affected by

22

country-specific differences The eEconomic environment legislation culture financial markets

and accounting practices in a country may affect the financial behavior of firms and the boundary

between bankrupt and non-bankrupt firms These factors may potentially weaken the

classification performance of the model in for other countries outside other than that e one fore

country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the

seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm

into account will improve the classification accuracy of the benchmark model In our empirical

study the country effect will beis assessed by including a variable of for country risk Therefore

the seventh hypothesis is presented as follows

H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country

risk is higher than that of the model without this effectincluded

5 Empirical data and statistical methods

51 Sample of firms

The principal data of for this study are were extracted from the ORBIS databases of Bureau Van

Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the

moment of sampling contained administrative information on more than 50 million European

firms However income statement and balance sheet information was available for

approximately 8 million companies More than 99 of the companies covered in this database

are private companies from different various industries justifying the use of the Zrsquorsquo-Score model

instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all

industrial groupings and for both private and public entities (Altman 1983 124 Altman and

Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope

23

of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all

industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly

used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that

international comparability may be a problem when administrative firm-level data are

internationally pooled For administrative data Although the definition of variables is usually less

harmonized for administrative data tThis is less of a problem in the ORBIS database because of

the common international format of balance sheets For example although some discrepancies

in profitloss statements may arise because of differences in fiscal systems across countries

balance sheet variables largely adhere to international standards

AThere are a number of factors that influence the international applicability of bankruptcy

prediction models These factors include accounting legislation and practice creditor rights and

investor protection judicial efficiency corporate governance bankruptcy protection and

insolvency management and firm risk-taking These factors strongly differ between European

and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-

Score model outside Europe First it is especiallyparticularly important to include the USUS as

because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It

is include d in our study as well because it has the largest market capitalization in the world

Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it

applicable to emerging market companies Therefore we include firms from China and Colombia

which represent two very culturally and institutionally very dissimilar emerging market

countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt

24

firms ie the limit we set for European countries) from ORBIS World were not

available Thus the results are also estimated and tested for three non-European countries (the

United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-

Score modelrsquos performance The samples of firms from these countries are were extracted from

ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms

from around the world

SFor statistical sampling several requirements are set for the statistical sampling of the empirical

data First we require that the firm to be selected be an industrial (non-financial) company

Second its owners must have limited liability (so partnerships and sole proprietors are left out

of the study) Third we set a minimum requirement for the size of the firm Because the financial

ratios of very small firms are generally too unstable for a failure prediction model these firms

are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have

exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we

include in our estimation sample firms from all European countries and 3 pre-selected non-

European countries where the number of failed firms is more greater than 60 If the number of

failed firms for any European country is less than 60 the firms from this country are only included

only in the test sample For qualifying European countries the firms are randomly classified in

the estimation and test samples so that the number of firms is approximately equal in both

samples Thus our estimation data include firms from 28 European and 3 non-European

countries Fifth all failed firms that fulfill the above requirements are included in our samples

However if the number of non-failed firms in a country is very high a sample is randomly

25

selected from that country Finally the time span of fiscal years potentially available for this study

ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in

the database are from a financial period within 2007 and 2010 earlier years are also excluded

for non-failed firms for comparability All qualifying observations of non-failed firms from the

years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses

of failed firms to the last most recent financial statements available before failure The four

independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize

outliers

Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test

data by country The estimation sample includes data from 2602563 non-failed and 38215

failed firms from 28 European and 3 non-European countries The test sample is slightly larger

because it includes data from 31 European and 3 non-European countries From For the country

of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt

firms The available US data that were available consist only of listed (and delisted) firms From

China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are

included in the estimation sample although there are only 32 such firms altogether7 The Chinese

datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status

are separately analyzed separately only in for the test data8 ST firms are listed firms suffering

6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor

26

from serious financial difficulties Excluding the special US data and the two non-private Chinese

datasets 994 of the observations in the data are private firms

(Table 1 here)

52 Status of failed firms

ORBIS has five classes for potentially active firms (active default of payment receivership

dormant and branch) and seven classes for inactive firms that no longer carry out business

activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation

branch and no precision) From Among these classes only active is selected to represent non-

distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by

considering (with exceptions described below) a firm failed if its status in ORBIS is stated as

bankruptcy However because of the small number of bankrupt firms in some countries we also

consider receivership (active) firms as failed even if they are active These firms generally suffer

from serious financial distress However firms in liquidation are generally not included in the

sample of failed firms Firms in liquidation may depending on the country contain firms that

have ceased activities due to reasons other than failure (mergers discontinuing the operations

of a daughter company or of a foreign branch etc) Therefore for most countries we select only

firms that are coded as being bankrupt or under receivership However there are a number of

special cases where failed firms are coded under a different status heading These countries or

samples are the following

Country Status categories Bulgaria In liquidation Bankruptcy

27

Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the

study (for example Switzerland) If a country had only a very small number of failed firms it was

dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries

including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status

classes (including the bankruptcy category) are not completely homogenous within European

countries due to different legislations although there are obvious similarities in insolvency acts

(Philippe et al 2002) China is a special case because it includes samples with three different

criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are

two different samples (liquidation and receivership)

53 Statistical methods

In this study seven research hypotheses are drawn for statistical testing The statistical analysis

will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The

classification performance of the original model is assessed by the AUC (Area Under Curve)

measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close

connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random

28

model and 05 for a model with an average classification performance In SAS software is used

for all statistical analysess SAS software is used

The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is

tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical

method (multiple discriminant analysis or MDA) The problem is that the estimation sample

includes different numbers of failed and non-failed firms from 31 countries In the original Z-

Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from

the USUS Following the characteristics of these data we weight the firms so that the weights

for the failed and non-failed firms are equally In so doingthis way the non-proportional

sampling in from different countries does will not affect the re-estimated model The number of

firms from different countries however varies significantly leading to greater weights for larger

countries To avoid this problem the observations are also weighted so that each country has an

equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using

these weighted data and the resulting AUC is compared with that based on the original model

The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score

model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is

based on less- restrictive statistical assumptions than MDA In this estimation the dependent

variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA

does not require that independent variables be multivariate normal or that groups have equal

covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA

29

creates a score (logit) score L for every firm It is assumed that the independent variables are

linearly related to L This score or logit is used to determine the conditional probability of failure

as follows

= (eq 4)

where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the

original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing

the statistical significance of the difference between AUCs for this LR model and for the re-

estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it

is used as a benchmark for further statistical AUC comparisons because LR is applied as the

principal method in the testing of the remaining research hypotheses

The third hypothesis (H3) is associated with the performance effect of taking account of the

bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on

the following logit

(eq 5)

where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model

bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and

D1 = 1 when year = 2008 0 otherwise

D2 = 1 when year = 2009 0 otherwise

D3 = 1 when year = 2010 0 otherwise

)|1( XYp )( 441101

1

1

1XbXbbL ee

3

1

4

10

jjji

ii DcXbbL

30

The dummy variables do not directly refer to the bankruptcy year which that is not given in the

data basedatabase but rather to the last available year before bankruptcy For failed firms there

is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is

the base category If the AUC of this extended LR model statistically significantly exceeds the AUC

of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3

Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third

hypothesis above However for each hypothesis appropriate variables are used instead of the

year dummies Hypothesis H4 addresses the performance effect of taking size into account the

size and it will beis tested using by adding two additional variables measuring firm size in to the

LR -model In this LR model size is measured by the natural logarithm of total assets and its

squared form In this way the effect of logarithmic size can be reflected by in a function following

the second-order parabola Hypothesis H5 predicts that the classification performance improves

when the age of the firm is explicitly taken into account When testing this hypothesis the

category 6ndash14 years category is used as the base category and two dummy variables are

incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis

H6 looks at whether is associated with the classification performance is affected bythe effect of

the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt

is tested here using dummy variables for seven industries (D1 restaurants and hotels D2

construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and

water production D7 information technology) with all other industries acting as the base

category

31

Hypothesis H7 predicts that the explicit consideration of the country of origin will improves

classification performance This hypothesis is tested by using country risk measures instead ofnot

using dummy variables for countries but using country risk measures instead The country risk of

each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the

annual closing of accounts The rating is numerically recoded such that the best rating AAA

equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating

D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial

ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-

modelLR models with the original four financial ratios and the additional variables specified in

the hypotheses are estimated for all data In addition an LR model including all additional

variables is estimated for all data to assess the simultaneous effect of all variables Finally six of

the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not

included in this country- level testing hypothesis H7 is not tested because the additional variable

(country risk) is constant within the country

6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models

Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score

model for all data The variation in the ratios is significant as shown by the standard deviation

and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-

failed firms are close to each other indicating a symmetry of distributions However this is not

32

the case for the failed firms For the faileddistressed firms the median exceeds the mean for

these three ratios indicating negatively skewed distributions For X4 (BVETD) the means

significantly exceed the median for both failed and non-failed firms indicating a positively

skewed distribution For each of the four variables both the mean and the median are higher for

non-failed firms than for failed firms which is consistent with the expectations The difference

between the means of the non-failed and failed firms is larger in the original USUS data than in

our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is

approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of

EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may

indicate lower classification accuracy than in the original sample

(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates

(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the

large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model

Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same

statistical method or MDA The coefficients here are negative because the models are estimated

using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight

than in the original USUS data while the weights of WCTA and BVETD have proportionally

decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the

logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR

model These coefficients are directly comparable with those of the MDA model which isas

expected for the this exceptionally large sample The differences in the coefficients of the original

33

four variables among the eight LR-modelLR models (Models 1-8) are small For each model the

coefficient of BVETD is very close to zero This indicatinges that the original four variables and

the additional variables are quite independent of each other

(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-

modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate

that after 2007 (the base category) this risk of failure significantly decreased year by year The

base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due

to as a result of the global financial crisis The crisis played a significant role in the failure of key

businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The

effects were especially felt in Europe The coefficients of Model 4 for the size variables show that

the contribution of size on to the logit (risk measure) is atreaches its maximum value when

logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model

5 confirms the riskiness of young firms because the risk of failure is very high for newly founded

firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The

coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky

industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is

statistically significant (because of the large sample) but negative and very close to zero Finally

the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7

62 All data performance of the Zrsquorsquo-Score models

34

Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1

refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of

all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average

accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)

Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its

performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The

lower part of the table shows the AUCs for the countries included only in the test data The

performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)

samples It is also low and for the liquidation firms in the United Kingdom

The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5

presents the differences of these medians between the non-failed and failed firms by country

This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of

medians This correlation which is high for each financial ratio showing that the effects of the

ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large

except for EBITTA which This implies with the exceptionally high AUC that the ST firms

systematically differ from the non-ST firms although the differences are not extremely large The

differences between the medians are very large in Poland for each ratio justifying the high AUC

They are also large and in Finland and in the Czech Republic where the difference in EBITTA is

average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in

all four ratios are below the average a result thatwhich is obviously associated with a low AUC

35

In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even

negative In Iceland and Ireland the differences only in EBITTA are exceptionally small

(Table 4 here)

(Table 5 here)

Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by

MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)

supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is

at approximately the average level The re-estimation of the coefficients has led to improved

classification accuracy in a number of countries especially in Bosnia China (ST) Norway and

Greece However it has impaired the classification accuracy in the UK and China (delisted)

Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-

modelLR model) showing the effect of the estimation method For the benchmark model the

AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between

among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR

model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country

This result was expected because the coefficients of the models are directly comparable The

similar results for the models may also indicate that the independent variables conform to

multinormality This last latter result confirms what most researchers in the field of default

classification models have concluded that the accuracy levels of MDA and logistical regression

36

models are extremely similar despite any statistical violations such as normality between MDA

and logistical regression models

Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)

than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects

are not positive for all countries The effects are positive for example for Russia Estonia

Germany Ireland and Latvia but these effects are not statistically significant There are

statistically significant negative effects for China (ST) and Serbia There are notable negative

effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are

due to the exceptional annual distributions of failed firms in these countries For the countries

with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the

countries with positive effects it is only a few percent For each group non-failed firms are quite

equally distributed over years When the coefficient of D3 is very low (-0666) it strongly

decreases the risk estimates of most failed firms in the former countries but of only a few failed

firms in the latter countries This which leads to the observed effects H3 is supported by

the level of the whole sample However in some country samples (at the level of individual

levelies) H3 is not supported

Model 4 (LR-modelLR model with size variables) performs better than the benchmark model

which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads

and to significant improvements in the AUC for eg China (delisted) and Austria For China

(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC

37

also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs

in a few countries but thise decrease is not significant Model 4 is based on the four original

variables and the size effect following a second-order parabola This kind of size effect is very

small for micro firms but increases when approaching its maximum value in middle-sized firms

The countries with a positive change in AUC typically have data where in which the percent of

failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized

failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In

contrast the countries with a negative change in AUC have data where in which the percentage

of failed micro firms is exceptionally high (70-80)

Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly

the same AUC as the benchmark model (AUC = 0748) However the difference is positive and

statistically significant giving at least marginal support to H5 (age effect) For nearly all countries

the effect of the age on AUC is small For Austria however thise effect is however positive and

significant For Austria the percentage of non-failed young firms (less than 6 years) among non-

failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed

firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong

positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only

a few non-failed firms which This obviously leads to an improvement in AUC

Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC

supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its

38

AUC is notably higher than the benchmark AUC only in only a few countries such asfor example

France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg

restaurants and hotels and the information technology industryies but it has a positive effect

for on the construction and manufacturing industries For the countries with a positive effect on

AUC the percent of non-failed firms in restaurants and hotels and information technology is

high while that of failed firms is low For the risky industries (construction and manufacturing)

these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many

failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)

firms Therefore the AUC will be increasesd This situation is reversed for the countries with a

negative effect on AUC (Austria and Slovenia) The samples of these countries include a high

percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC

Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher

classification performance (AUC = 0749) than the benchmark model This result gives only very

weak support for H7 (country of origin effect) However for each country the resultant AUC is

nearly identical to that given by the benchmark model This result was expected due to the

negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7

This result implies that country risk hasdoes not have noany effect on the boundary between

bankrupt and non-bankrupt firms

Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional

variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to

39

the benchmark AUC in all test data However the effect on the AUCs largely varies and is either

negative or positive in different countries The positive effect is large in several countries such

as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative

effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show

that the inclusion of additional variables in the original model will usually increase the AUC but

not in every country

The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with

the USUS firms being unlike the majority of other firms in this study being listed or delisted

companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese

firm samples is associated with very small differences between the medians of the non-failed and

failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable

with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed

firms is very high Prior studies based on Chinese ST -firms have also demonstrated good

predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for

additional research and modeling work regarding unlisted and delisted Chinese firms

63 Country-level data performance of the Zrsquorsquo-Score models

The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform

all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the

different models estimated for each country separately (country-level models) In this table the

ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated

40

from the country data this benchmark will beis clearly outperformed by the resulting MDA

(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and

Sweden) However these results give only weak support for H1 at the country level because the

effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and

2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs

given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed

by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation

method)

Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC

than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3

leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United

StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in

nearly every country supporting H4 This improvement is significant however only in the United

StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark

model but for which it is only 0710 for the benchmark model It This improvement is also notable

in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age

category dummies) gives higher AUCs than the benchmark model for several countries but none

of the improvements are significant The positive effect is particularly strong especially in

Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only

weakly supports H5 because these negative effects are relatively small

41

Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects

on AUCs when compared with the benchmark However the only significant effect is the negative

effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the

industry dummies However there are notable positive effects on the AUC in for example

France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak

support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in

AUC compared with the benchmark model in Colombia Iceland and the Unites States It also

leads and to notable improvements in AUC at least in Belgium Denmark Estonia France

Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are

found only in Austria and Slovenia Thus as a conclusion we can concludestate that the

classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can

remarkably increases in most countries when different effects are taken into account by

additional variables This increase is found in most European countries and in Colombia and the

United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-

modelLR model and it can only be slightly improved only slightly by additional variables

(Table 6 here)

7 Summary of the study and a suggested extension

The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model

originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is

assessed using a very large international data set We test how the original version of the Zrsquorsquo-

42

Score model performs in different countries and how re-estimations using another statistical

method and different additional variables affect the classification performance when the data

are very heterogeneous For this kind of testing seven research hypotheses on classification

performance are extractedformulated These research hypotheses are tested for all data and

separately for country data (country-level analysis) The estimation data are from 31 countries

and the results are validated for 34 countries The countries are mainly from Europe but three

non-European countries are included (China Colombia and the United StatesUS) The statuses

used in the classification are mainly bankruptcyactive but receivership firms are also considered

to have failed In the Chinese data ST (special treatment) and delisted firms are also separately

analyzed as failed firms

The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very

satisfactorily in an international context The effects of the four financial ratios on performance

are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small

contribution in re-estimation The original model performs very well in several countries such as

Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only

marginally improved the classification performance thus weakly supporting the obsolescence

hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust

across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion

holds for the re-estimation of the model using LRA because the performance results are very

similar with to those of MDA (H2) The use of additional variables in the model generally

improvesd the classification accuracy of the original model but the results for countries are

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

12

Zhdanov (2013) who posed the question of whether the inclusion of variables related to

investment opportunities improved the predictive power of three models (Altmanrsquos Z-score

model and Zmijewskirsquos and Shumwayrsquos models) They used three proxies for investment

opportunities (market-to-book value-to-book and RampD-to-assets) The measures of investment

opportunities were linked to the likelihood of default The inclusion of either of these measures

improved the out-of-sample forecasting ability of all three

The verification of Altmanrsquos model concentrated on its efficacy or on how it compares a

comparison with other accounting-based market-based or hazard models However although

the original Z-Score model was not solely an accounting-based approach because the market

value of equity was utilized we classify it here as an accounting -based approachbecause the

market value of equity was utilized Grice and Ingram (2001) used a new novel data set of US

firms and posed three questions about the efficacy of Altmanrsquos model They concludinged that

the prediction accuracy of Altmanrsquos model had declined over time and that the coefficients of

the model had significantly changed which means that the relation between the financial ratios

and the signs of financial distress had changed over time The model was sensitive to industry

classification (more efficient for manufacturing firms than for non-manufacturing firms) but was

not sensitive to the type of financial distress Similar conclusions were drawn by Grice and Dugan

(2003) regarding Ohlsonrsquos (1980) and Zmijewskirsquos (1984) models

Hillegeist et al (2004) compared Altmanrsquos Z-score and Ohlsonrsquos O-score (with original and

updated coefficients) with the model based on BSM option pricing (BSM-Prob) They used

13

relative information content tests to compare the out-of-sample performance BSM-Prob

outperformed accounting-based models The conclusions were robust to various modifications

of accounting-based models such as updated coefficients industry effects and the separation

of variables Chava and Jarrow (2004) employed an extended bankruptcy database of US listed

firms to prove the superiority of Shumwayrsquos model (2001) over Altmanrsquos (1968) and Zmijewskirsquos

(1984) models The authors re-estimated the models over the 1962ndash1990 period and forecasted

bankruptcies over the 1991ndash1999 period In the case of Shumwayrsquos model 744 (in the first

decile) of the bankruptcies were correctly identified for with Altmanrsquos model it was 632 and

for with Zmijewskirsquos model it was 432 Shumwayrsquos market-based model also outperformed

accounting-based models in terms of the ROC curve (091)

Reisz and Perlich (2007) used the barrier option for bankruptcy prediction and compared its

discriminatory power with other market-based models and Altmanrsquos Z-Score and Zrdquo-Score The

dataset covered nearly 6000 industrial firms over the period 1988ndash2002 period The authors

proved the domination superiority of Altmanrsquos Z-Score and Zrdquo-Score models for short-term (up

to 1 year) bankruptcy prediction For medium- and long-term bankruptcy prediction their barrier

option model outperformed the other models Pindado et al (2008) developed an ex -ante model

for the estimation of financial distress likelihood (FDL) using awith the use of panel data

methodology and they presented a financially (not legally) based definition of distress Their

sample covered 1583 US companies and 2250 companies from other G-7 countries for the

period 1990ndash2002 period They used a re-estimated Z-Score as a benchmark The FDL model

outperformed the Z-Score model in terms of stability and classification power for different

14

countries and time periods In the case of the re-estimated Z-Score model only profitability and

retained earnings kept maintained their significance for different years and countries

Wu et al (2010) evaluated the performance of five models (Altman 1968 Ohlson 1980

Zmijewski 1984 Shumway 2001 and Hillegeist et al 2004) using an up-to-date data set for

US listed firms Based on these models the authors built their own integrated model that is a

multi-period logit model with an expanded set of variables The integrated model which

combined accounting and market data as well as firmsrsquo characteristics outperformed the other

models Altmanrsquos Z-score performed poorly in compared withison to the other 4 models

Shumwayrsquos model performed best Hillegeist et alrsquos model performed adequately and Ohlsonrsquos

and Zmijewskirsquos models performed adequately although their performance deteriorated over

time

Jackson and Wood (2013) tested 13 different models of bankruptcy prediction and assessed their

efficacy using the ROC curve They selected three single- variable models three accounting-based

models (including Altmanrsquos Z-Score) in two versions (with up-dated coefficients and a neural

network approach) and four contingent claims models The contingent claims models and the

latter group outperformed the other models The best four best models were contingent claims

models based on European call and barrier options Although tThe predictive performance

improved withIn the case of the application of the neural network to accounting-based models

the predictive ability improved but it was still lower than within the case of the market-based

models

15

Acosta-Gonzaacutelez and Fernaacutendez-Rodriacuteguez (2014) used genetic algorithms with the Schwarz

information criterion (GASIC) for variable selection combined with the logit model for bankruptcy

prediction Altmanrsquos Z-Score model was used as one of two benchmarks for the authorsrsquo model

evaluation For one- step- ahead forecasting Altmanrsquos model was better in at predicting failed

firms but the type II error was high For two- and three- steps- ahead forecasts the performance

of the models was similar forin terms of failed firms but forin the case of non-failed firms and

the prediction accuracy of Altmanrsquos model was worse For four- steps- ahead forecasts the GASIC

model outperformed the other models forin the case of failed firms and but it performedwas

comparablye forin the case of non-failed firms

In general Altmanrsquos Z-Score model underperformed in compared withison to market-based

models but there was also evidence indicated that it performed well for short-term distress

prediction The question of whether market-based models are perform better than accounting-

based models has been raised many times (eg discussion in Das et al 2009 Bauer and d

Agarwal 2014) Our purpose is not to contribute to this strand of research but rather to focus on

the accounting-based approach In this study we analyze primarily analyze privately held firms

so there are by definition no market data for these firms In this case aAn accounting-based

approach is the only solution that could be appliedapplicable in this case by banks as lenders or

by investors holding debt securities of firms not listed on the stock exchange Thus far most of

the studies have concentrated on the US market only a few of them have used data from other

16

countries such as Japan the UK Lithuania or the G-7 countries Our analysis is a significant

extension of the previous research

4 Methodology and research hypotheses

The literature survey shows that the Z-Score model (publicly traded firms) the Zrsquo-Score model

(private manufacturing firms) and the Zrsquorsquo-Score model (private and publicly

traded manufacturing and non-manufacturing firms) have been adapted for different purposes

In this study we are first interested first in assessing the performance of the original Zrsquorsquo-Score

model in classifying bankrupt and non-bankrupt firms in an international context with special

focus on the European market However we also validate the results in a set of non-European

countries to generalize the results for circumstances outside Europe Second we will re-estimate

the model using extensive international data and then use the re-estimated Zrsquorsquo-Score model as a

benchmark for assessing the effects of different factors on the modelrsquos performance in terms of

classification accuracy We will assess the effects of the following five factors on this

performance year of bankruptcy size of firms age of firms industry and country of origin WIn

all we will test a set of hypotheses based on the effects of the model on performance on two

different levels First we will test a set of hypotheses on a pooled set of all firms and second on

data from each country individually CIn compared withison to the previous research our this

studyrsquos contribution is the focus on an international context not just simply the model

application or re-estimation for of a given countryrsquos data Because weour aim is focusing on the

performance of the Zrsquorsquo-Score mModel when using ain large body of international data the

research hypotheses are of a technical nature and are presented given as follows

17

H1 Obsolescence of the coefficients The Zrsquorsquo-Score mModel was originally estimated using the

same sample of firms that was used to develop the original Z-Score mModel The bankruptcies

in the estimation data occurred during the period 1946ndash-1965 period Thus the oldest

observations are from nearly seventy years ago during the post-war period Altman (1983)

recommended utilizing data as near to the present as possible when developing a bankruptcy

prediction model It is obvious that firmsrsquo the financial behavior of firms and their business

environment have significantly changed since then Thereforen potentially making the

importance of the financial ratios as reflected by the coefficients of the model may differ from

their original importance reflected by the coefficients of the model Therefore we suggest as

the first hypothesis (H1) that the re-estimation of the coefficients of the four original variables

of the Zrsquorsquo-Score model will improve the classification performance of the model in an

international context This hypothesis is supported by the previous research (eg Grice and

Ingram 2001) and by practice H1 aims to support this evidence on the international level and is

expressed as follows

H1 RThe re-estimatingon of the coefficients of the Zrsquorsquo-Score model improves the its

classification accuracy of the model

H2 Method of estimation The original Zrsquorsquo-Score model has been estimated using MDA However

MDA is based on the ordinary least squares (OLS) method and thus requires assumptions of

multinormality homoscedasticity and linearity which are not often met in empirical financial

ratio analysis Therefore we re-estimate the Zrsquorsquo-Score model using logistic regression analysis

(LRA) to assess the effect of the method of estimation method LRA does not require most of the

18

restricting assumptions of MDA In LRA the multivariate normality of the independent variables

is not required nor are homoscedasticity and linearity For the sake of OLS MDA can be more

useful than LRA for small samples such as the original sample of 66 firms used in the estimation

of the Zrsquorsquo-Score mModel However in a large sample LRA may potentially perform better In this

study we use large samples which is advantageous for LRA Therefore our second hypothesis

(H2) suggests is that the classification performance of the re-estimated Zrsquorsquo-Score model will be

improved when it is estimated using LRA instead of MDA The model re-estimated for the original

variables using LRA and all pooled data is called here the Zrsquorsquo-Score LR-modelLR model The

performance of this re-estimated model is used in this study as the benchmark for further

analyses Thus the second hypothesis is as follows

H2 The prediction accuracy of the logistic regression version of the Zrsquorsquo-Score model is higher

than that of the multiple discriminant analysis version

H3 Bankruptcy year The model based on the relationship between bankruptcy and financial

ratios is likely to be affected by the macroeconomic environment These effects may significantly

decrease the classification accuracy of the model If the model is estimated using data from one

year and it will be applied to data from another year the validity of the model can be questioned

Business cycles inIn terms of economic growth credit policy and interest rates business cycles

can ahave an effect on the boundary between bankrupt and non-bankrupt firms The original Zrsquorsquo-

Score model is estimated using data from the period 1946ndash1965 period which includes several

business cycles Therefore the model is not focused on any specific stage of a cycle and does not

explicitly take into account of the bankruptcy year Altman (1983) suggested gathering data from

19

firms for the last most recent few years when developing a prediction model In this study the

benchmark Zrsquorsquo-Score LR -model will beis estimated for a shorter period than in the original

estimation although it covers several recent years on for different stages of the business cycle in

different countries Therefore the third hypothesis (H3) assumes is that the classification

accuracy of the benchmark model can be increased by explicitly taking into account of the year

of bankruptcy in the estimation of the model estimation The third hypothesis is presented as

follows

H3 The prediction accuracy of the modelrsquos prediction accuracy is higher whenith the

effect of the year of bankruptcy is included is higher than that of the model without the

effect

H4 Size of the firm The boundary between bankrupt and non-bankrupt firms is different for small

and larger firms which decreases the performance of the model estimation when estimated for

data from one size category and isare applied to for data from another size category For the

bankrupt and non-bankrupt firms in the original data for Zrsquorsquo-Score model estimation the range

of asset sizes was ranged between approximately 1 and 25 million US dollars The data did not

include very small or very large firms Altman (1983) regarded the suitability of the original Z-

Score model (and likewisein the same way the Zrsquorsquo-Score model) for all firms as debatable

because of this omission it did not scrutinize very large or very small firms In the currentis study

the benchmark Zrsquorsquo-Score LR -model will beis estimated for data from many size categories from

very small firms to very large firms The fourth hypothesis (H4) assumes that the classification

performance of the uniform benchmark LR -model based on the original four financial variables

20

of the Zrsquorsquo-Score model is improved when the size category of the firm is explicitly taken into

account Thus we present the fourth hypothesis as follows

H4 The modelrsquos prediction accuracy of the modelis higher whenith the effect of size is

includede is higher than that of the model without this effect

H5 Age of the firm International insolvency statistics generally show that bankruptcy risk is a

function of the age of the firm Very young firms typically show a very high risk The original Zrsquorsquo-

Score model does not explicitly take age into account age However Altman (1983) noted that

the age of a firm is implicitly considered in the Retained EarningsTotal Assets ratio (X2) which

was regarded as a new ratio in the bankruptcy prediction context A relatively young firm will

probably show a low ratio because it has not had time to build up cumulative profits Thus a

young firm is to some degree discriminated against in the model and its likelihood of being

classified as bankrupt is relatively higher than that of an older firm The incidence of failure is

much higher in the early years of a firm AlthoughDespite that the age of the firm in this way is

implicitly taken into account in X2 we expect that an explicit consideration of the age will improve

the classification accuracy bydue to controlling for the age factor Therefore the fifth hypothesis

(H5) proposes that the performance of the uniform benchmark model based on the original four

financial variables of the Zrsquorsquo-Score model is increasesd when the age of the firm is explicitly taken

into account The fifth hypothesis is can be presented as follows

H5 The modelrsquos prediction accuracy of the model is higher when with the effect of firm

age is higher than that of the model without this effectincluded

21

H6 Industry of the firm The original Zrsquo-Score model was estimated only for manufacturing firms

Altman (1983) stated that it would be ideal to develop a bankruptcy prediction model utilizing a

homogenous group of bankrupt firms If we are interested in a particular industry grouping we

should gather data from bankrupt and non-bankrupt firms in these groupings Previous studies

show that financial distress analysis is influenced by the industry effect (Smith and Liou 2007)

Firms in different industries tend to report different levels of the same financial ratios which may

have an effect on the boundary between bankrupt and non-bankrupt firms This industry effect

may be present in the Zrsquo-Score model especially due to the SalesTotal Assets ratio (X5) This ratio

which showed the least significance on a univariate basis while making but it had made a very

significant contribution to the discriminant power of the multivariate model Altman (1983)

recognized the potential industry effect due to a the wide variation among industries in asset

turnover and specified the Zrsquorsquo-Score model without X5 However also the Zrsquorsquo-Score model was

also estimated using the original sample of manufacturing firms In our analysis the uniform

benchmark model based on the original four financial variables of the Zrsquorsquo-Score model is

estimated for a statistical sample representing different industries Therefore the sixth

hypothesis (H6) assumes that an explicit consideration of industry will improve the classification

accuracy of this benchmark model H6It can be expressed in the following form

H6 The modelrsquos prediction accuracy of the modelis higher whenith the effect of industry is

higher than that of the model without this effectincluded

H7 Country of origin The original Zrsquorsquo-Score model has been estimated only for USUS firms It

can be expected that the international applicability of the model to other countries is affected by

22

country-specific differences The eEconomic environment legislation culture financial markets

and accounting practices in a country may affect the financial behavior of firms and the boundary

between bankrupt and non-bankrupt firms These factors may potentially weaken the

classification performance of the model in for other countries outside other than that e one fore

country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the

seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm

into account will improve the classification accuracy of the benchmark model In our empirical

study the country effect will beis assessed by including a variable of for country risk Therefore

the seventh hypothesis is presented as follows

H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country

risk is higher than that of the model without this effectincluded

5 Empirical data and statistical methods

51 Sample of firms

The principal data of for this study are were extracted from the ORBIS databases of Bureau Van

Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the

moment of sampling contained administrative information on more than 50 million European

firms However income statement and balance sheet information was available for

approximately 8 million companies More than 99 of the companies covered in this database

are private companies from different various industries justifying the use of the Zrsquorsquo-Score model

instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all

industrial groupings and for both private and public entities (Altman 1983 124 Altman and

Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope

23

of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all

industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly

used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that

international comparability may be a problem when administrative firm-level data are

internationally pooled For administrative data Although the definition of variables is usually less

harmonized for administrative data tThis is less of a problem in the ORBIS database because of

the common international format of balance sheets For example although some discrepancies

in profitloss statements may arise because of differences in fiscal systems across countries

balance sheet variables largely adhere to international standards

AThere are a number of factors that influence the international applicability of bankruptcy

prediction models These factors include accounting legislation and practice creditor rights and

investor protection judicial efficiency corporate governance bankruptcy protection and

insolvency management and firm risk-taking These factors strongly differ between European

and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-

Score model outside Europe First it is especiallyparticularly important to include the USUS as

because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It

is include d in our study as well because it has the largest market capitalization in the world

Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it

applicable to emerging market companies Therefore we include firms from China and Colombia

which represent two very culturally and institutionally very dissimilar emerging market

countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt

24

firms ie the limit we set for European countries) from ORBIS World were not

available Thus the results are also estimated and tested for three non-European countries (the

United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-

Score modelrsquos performance The samples of firms from these countries are were extracted from

ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms

from around the world

SFor statistical sampling several requirements are set for the statistical sampling of the empirical

data First we require that the firm to be selected be an industrial (non-financial) company

Second its owners must have limited liability (so partnerships and sole proprietors are left out

of the study) Third we set a minimum requirement for the size of the firm Because the financial

ratios of very small firms are generally too unstable for a failure prediction model these firms

are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have

exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we

include in our estimation sample firms from all European countries and 3 pre-selected non-

European countries where the number of failed firms is more greater than 60 If the number of

failed firms for any European country is less than 60 the firms from this country are only included

only in the test sample For qualifying European countries the firms are randomly classified in

the estimation and test samples so that the number of firms is approximately equal in both

samples Thus our estimation data include firms from 28 European and 3 non-European

countries Fifth all failed firms that fulfill the above requirements are included in our samples

However if the number of non-failed firms in a country is very high a sample is randomly

25

selected from that country Finally the time span of fiscal years potentially available for this study

ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in

the database are from a financial period within 2007 and 2010 earlier years are also excluded

for non-failed firms for comparability All qualifying observations of non-failed firms from the

years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses

of failed firms to the last most recent financial statements available before failure The four

independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize

outliers

Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test

data by country The estimation sample includes data from 2602563 non-failed and 38215

failed firms from 28 European and 3 non-European countries The test sample is slightly larger

because it includes data from 31 European and 3 non-European countries From For the country

of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt

firms The available US data that were available consist only of listed (and delisted) firms From

China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are

included in the estimation sample although there are only 32 such firms altogether7 The Chinese

datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status

are separately analyzed separately only in for the test data8 ST firms are listed firms suffering

6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor

26

from serious financial difficulties Excluding the special US data and the two non-private Chinese

datasets 994 of the observations in the data are private firms

(Table 1 here)

52 Status of failed firms

ORBIS has five classes for potentially active firms (active default of payment receivership

dormant and branch) and seven classes for inactive firms that no longer carry out business

activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation

branch and no precision) From Among these classes only active is selected to represent non-

distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by

considering (with exceptions described below) a firm failed if its status in ORBIS is stated as

bankruptcy However because of the small number of bankrupt firms in some countries we also

consider receivership (active) firms as failed even if they are active These firms generally suffer

from serious financial distress However firms in liquidation are generally not included in the

sample of failed firms Firms in liquidation may depending on the country contain firms that

have ceased activities due to reasons other than failure (mergers discontinuing the operations

of a daughter company or of a foreign branch etc) Therefore for most countries we select only

firms that are coded as being bankrupt or under receivership However there are a number of

special cases where failed firms are coded under a different status heading These countries or

samples are the following

Country Status categories Bulgaria In liquidation Bankruptcy

27

Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the

study (for example Switzerland) If a country had only a very small number of failed firms it was

dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries

including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status

classes (including the bankruptcy category) are not completely homogenous within European

countries due to different legislations although there are obvious similarities in insolvency acts

(Philippe et al 2002) China is a special case because it includes samples with three different

criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are

two different samples (liquidation and receivership)

53 Statistical methods

In this study seven research hypotheses are drawn for statistical testing The statistical analysis

will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The

classification performance of the original model is assessed by the AUC (Area Under Curve)

measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close

connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random

28

model and 05 for a model with an average classification performance In SAS software is used

for all statistical analysess SAS software is used

The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is

tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical

method (multiple discriminant analysis or MDA) The problem is that the estimation sample

includes different numbers of failed and non-failed firms from 31 countries In the original Z-

Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from

the USUS Following the characteristics of these data we weight the firms so that the weights

for the failed and non-failed firms are equally In so doingthis way the non-proportional

sampling in from different countries does will not affect the re-estimated model The number of

firms from different countries however varies significantly leading to greater weights for larger

countries To avoid this problem the observations are also weighted so that each country has an

equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using

these weighted data and the resulting AUC is compared with that based on the original model

The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score

model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is

based on less- restrictive statistical assumptions than MDA In this estimation the dependent

variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA

does not require that independent variables be multivariate normal or that groups have equal

covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA

29

creates a score (logit) score L for every firm It is assumed that the independent variables are

linearly related to L This score or logit is used to determine the conditional probability of failure

as follows

= (eq 4)

where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the

original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing

the statistical significance of the difference between AUCs for this LR model and for the re-

estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it

is used as a benchmark for further statistical AUC comparisons because LR is applied as the

principal method in the testing of the remaining research hypotheses

The third hypothesis (H3) is associated with the performance effect of taking account of the

bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on

the following logit

(eq 5)

where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model

bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and

D1 = 1 when year = 2008 0 otherwise

D2 = 1 when year = 2009 0 otherwise

D3 = 1 when year = 2010 0 otherwise

)|1( XYp )( 441101

1

1

1XbXbbL ee

3

1

4

10

jjji

ii DcXbbL

30

The dummy variables do not directly refer to the bankruptcy year which that is not given in the

data basedatabase but rather to the last available year before bankruptcy For failed firms there

is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is

the base category If the AUC of this extended LR model statistically significantly exceeds the AUC

of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3

Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third

hypothesis above However for each hypothesis appropriate variables are used instead of the

year dummies Hypothesis H4 addresses the performance effect of taking size into account the

size and it will beis tested using by adding two additional variables measuring firm size in to the

LR -model In this LR model size is measured by the natural logarithm of total assets and its

squared form In this way the effect of logarithmic size can be reflected by in a function following

the second-order parabola Hypothesis H5 predicts that the classification performance improves

when the age of the firm is explicitly taken into account When testing this hypothesis the

category 6ndash14 years category is used as the base category and two dummy variables are

incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis

H6 looks at whether is associated with the classification performance is affected bythe effect of

the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt

is tested here using dummy variables for seven industries (D1 restaurants and hotels D2

construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and

water production D7 information technology) with all other industries acting as the base

category

31

Hypothesis H7 predicts that the explicit consideration of the country of origin will improves

classification performance This hypothesis is tested by using country risk measures instead ofnot

using dummy variables for countries but using country risk measures instead The country risk of

each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the

annual closing of accounts The rating is numerically recoded such that the best rating AAA

equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating

D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial

ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-

modelLR models with the original four financial ratios and the additional variables specified in

the hypotheses are estimated for all data In addition an LR model including all additional

variables is estimated for all data to assess the simultaneous effect of all variables Finally six of

the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not

included in this country- level testing hypothesis H7 is not tested because the additional variable

(country risk) is constant within the country

6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models

Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score

model for all data The variation in the ratios is significant as shown by the standard deviation

and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-

failed firms are close to each other indicating a symmetry of distributions However this is not

32

the case for the failed firms For the faileddistressed firms the median exceeds the mean for

these three ratios indicating negatively skewed distributions For X4 (BVETD) the means

significantly exceed the median for both failed and non-failed firms indicating a positively

skewed distribution For each of the four variables both the mean and the median are higher for

non-failed firms than for failed firms which is consistent with the expectations The difference

between the means of the non-failed and failed firms is larger in the original USUS data than in

our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is

approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of

EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may

indicate lower classification accuracy than in the original sample

(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates

(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the

large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model

Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same

statistical method or MDA The coefficients here are negative because the models are estimated

using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight

than in the original USUS data while the weights of WCTA and BVETD have proportionally

decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the

logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR

model These coefficients are directly comparable with those of the MDA model which isas

expected for the this exceptionally large sample The differences in the coefficients of the original

33

four variables among the eight LR-modelLR models (Models 1-8) are small For each model the

coefficient of BVETD is very close to zero This indicatinges that the original four variables and

the additional variables are quite independent of each other

(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-

modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate

that after 2007 (the base category) this risk of failure significantly decreased year by year The

base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due

to as a result of the global financial crisis The crisis played a significant role in the failure of key

businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The

effects were especially felt in Europe The coefficients of Model 4 for the size variables show that

the contribution of size on to the logit (risk measure) is atreaches its maximum value when

logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model

5 confirms the riskiness of young firms because the risk of failure is very high for newly founded

firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The

coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky

industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is

statistically significant (because of the large sample) but negative and very close to zero Finally

the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7

62 All data performance of the Zrsquorsquo-Score models

34

Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1

refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of

all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average

accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)

Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its

performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The

lower part of the table shows the AUCs for the countries included only in the test data The

performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)

samples It is also low and for the liquidation firms in the United Kingdom

The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5

presents the differences of these medians between the non-failed and failed firms by country

This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of

medians This correlation which is high for each financial ratio showing that the effects of the

ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large

except for EBITTA which This implies with the exceptionally high AUC that the ST firms

systematically differ from the non-ST firms although the differences are not extremely large The

differences between the medians are very large in Poland for each ratio justifying the high AUC

They are also large and in Finland and in the Czech Republic where the difference in EBITTA is

average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in

all four ratios are below the average a result thatwhich is obviously associated with a low AUC

35

In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even

negative In Iceland and Ireland the differences only in EBITTA are exceptionally small

(Table 4 here)

(Table 5 here)

Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by

MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)

supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is

at approximately the average level The re-estimation of the coefficients has led to improved

classification accuracy in a number of countries especially in Bosnia China (ST) Norway and

Greece However it has impaired the classification accuracy in the UK and China (delisted)

Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-

modelLR model) showing the effect of the estimation method For the benchmark model the

AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between

among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR

model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country

This result was expected because the coefficients of the models are directly comparable The

similar results for the models may also indicate that the independent variables conform to

multinormality This last latter result confirms what most researchers in the field of default

classification models have concluded that the accuracy levels of MDA and logistical regression

36

models are extremely similar despite any statistical violations such as normality between MDA

and logistical regression models

Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)

than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects

are not positive for all countries The effects are positive for example for Russia Estonia

Germany Ireland and Latvia but these effects are not statistically significant There are

statistically significant negative effects for China (ST) and Serbia There are notable negative

effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are

due to the exceptional annual distributions of failed firms in these countries For the countries

with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the

countries with positive effects it is only a few percent For each group non-failed firms are quite

equally distributed over years When the coefficient of D3 is very low (-0666) it strongly

decreases the risk estimates of most failed firms in the former countries but of only a few failed

firms in the latter countries This which leads to the observed effects H3 is supported by

the level of the whole sample However in some country samples (at the level of individual

levelies) H3 is not supported

Model 4 (LR-modelLR model with size variables) performs better than the benchmark model

which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads

and to significant improvements in the AUC for eg China (delisted) and Austria For China

(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC

37

also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs

in a few countries but thise decrease is not significant Model 4 is based on the four original

variables and the size effect following a second-order parabola This kind of size effect is very

small for micro firms but increases when approaching its maximum value in middle-sized firms

The countries with a positive change in AUC typically have data where in which the percent of

failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized

failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In

contrast the countries with a negative change in AUC have data where in which the percentage

of failed micro firms is exceptionally high (70-80)

Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly

the same AUC as the benchmark model (AUC = 0748) However the difference is positive and

statistically significant giving at least marginal support to H5 (age effect) For nearly all countries

the effect of the age on AUC is small For Austria however thise effect is however positive and

significant For Austria the percentage of non-failed young firms (less than 6 years) among non-

failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed

firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong

positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only

a few non-failed firms which This obviously leads to an improvement in AUC

Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC

supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its

38

AUC is notably higher than the benchmark AUC only in only a few countries such asfor example

France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg

restaurants and hotels and the information technology industryies but it has a positive effect

for on the construction and manufacturing industries For the countries with a positive effect on

AUC the percent of non-failed firms in restaurants and hotels and information technology is

high while that of failed firms is low For the risky industries (construction and manufacturing)

these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many

failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)

firms Therefore the AUC will be increasesd This situation is reversed for the countries with a

negative effect on AUC (Austria and Slovenia) The samples of these countries include a high

percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC

Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher

classification performance (AUC = 0749) than the benchmark model This result gives only very

weak support for H7 (country of origin effect) However for each country the resultant AUC is

nearly identical to that given by the benchmark model This result was expected due to the

negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7

This result implies that country risk hasdoes not have noany effect on the boundary between

bankrupt and non-bankrupt firms

Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional

variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to

39

the benchmark AUC in all test data However the effect on the AUCs largely varies and is either

negative or positive in different countries The positive effect is large in several countries such

as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative

effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show

that the inclusion of additional variables in the original model will usually increase the AUC but

not in every country

The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with

the USUS firms being unlike the majority of other firms in this study being listed or delisted

companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese

firm samples is associated with very small differences between the medians of the non-failed and

failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable

with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed

firms is very high Prior studies based on Chinese ST -firms have also demonstrated good

predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for

additional research and modeling work regarding unlisted and delisted Chinese firms

63 Country-level data performance of the Zrsquorsquo-Score models

The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform

all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the

different models estimated for each country separately (country-level models) In this table the

ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated

40

from the country data this benchmark will beis clearly outperformed by the resulting MDA

(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and

Sweden) However these results give only weak support for H1 at the country level because the

effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and

2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs

given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed

by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation

method)

Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC

than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3

leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United

StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in

nearly every country supporting H4 This improvement is significant however only in the United

StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark

model but for which it is only 0710 for the benchmark model It This improvement is also notable

in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age

category dummies) gives higher AUCs than the benchmark model for several countries but none

of the improvements are significant The positive effect is particularly strong especially in

Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only

weakly supports H5 because these negative effects are relatively small

41

Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects

on AUCs when compared with the benchmark However the only significant effect is the negative

effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the

industry dummies However there are notable positive effects on the AUC in for example

France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak

support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in

AUC compared with the benchmark model in Colombia Iceland and the Unites States It also

leads and to notable improvements in AUC at least in Belgium Denmark Estonia France

Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are

found only in Austria and Slovenia Thus as a conclusion we can concludestate that the

classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can

remarkably increases in most countries when different effects are taken into account by

additional variables This increase is found in most European countries and in Colombia and the

United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-

modelLR model and it can only be slightly improved only slightly by additional variables

(Table 6 here)

7 Summary of the study and a suggested extension

The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model

originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is

assessed using a very large international data set We test how the original version of the Zrsquorsquo-

42

Score model performs in different countries and how re-estimations using another statistical

method and different additional variables affect the classification performance when the data

are very heterogeneous For this kind of testing seven research hypotheses on classification

performance are extractedformulated These research hypotheses are tested for all data and

separately for country data (country-level analysis) The estimation data are from 31 countries

and the results are validated for 34 countries The countries are mainly from Europe but three

non-European countries are included (China Colombia and the United StatesUS) The statuses

used in the classification are mainly bankruptcyactive but receivership firms are also considered

to have failed In the Chinese data ST (special treatment) and delisted firms are also separately

analyzed as failed firms

The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very

satisfactorily in an international context The effects of the four financial ratios on performance

are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small

contribution in re-estimation The original model performs very well in several countries such as

Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only

marginally improved the classification performance thus weakly supporting the obsolescence

hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust

across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion

holds for the re-estimation of the model using LRA because the performance results are very

similar with to those of MDA (H2) The use of additional variables in the model generally

improvesd the classification accuracy of the original model but the results for countries are

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

13

relative information content tests to compare the out-of-sample performance BSM-Prob

outperformed accounting-based models The conclusions were robust to various modifications

of accounting-based models such as updated coefficients industry effects and the separation

of variables Chava and Jarrow (2004) employed an extended bankruptcy database of US listed

firms to prove the superiority of Shumwayrsquos model (2001) over Altmanrsquos (1968) and Zmijewskirsquos

(1984) models The authors re-estimated the models over the 1962ndash1990 period and forecasted

bankruptcies over the 1991ndash1999 period In the case of Shumwayrsquos model 744 (in the first

decile) of the bankruptcies were correctly identified for with Altmanrsquos model it was 632 and

for with Zmijewskirsquos model it was 432 Shumwayrsquos market-based model also outperformed

accounting-based models in terms of the ROC curve (091)

Reisz and Perlich (2007) used the barrier option for bankruptcy prediction and compared its

discriminatory power with other market-based models and Altmanrsquos Z-Score and Zrdquo-Score The

dataset covered nearly 6000 industrial firms over the period 1988ndash2002 period The authors

proved the domination superiority of Altmanrsquos Z-Score and Zrdquo-Score models for short-term (up

to 1 year) bankruptcy prediction For medium- and long-term bankruptcy prediction their barrier

option model outperformed the other models Pindado et al (2008) developed an ex -ante model

for the estimation of financial distress likelihood (FDL) using awith the use of panel data

methodology and they presented a financially (not legally) based definition of distress Their

sample covered 1583 US companies and 2250 companies from other G-7 countries for the

period 1990ndash2002 period They used a re-estimated Z-Score as a benchmark The FDL model

outperformed the Z-Score model in terms of stability and classification power for different

14

countries and time periods In the case of the re-estimated Z-Score model only profitability and

retained earnings kept maintained their significance for different years and countries

Wu et al (2010) evaluated the performance of five models (Altman 1968 Ohlson 1980

Zmijewski 1984 Shumway 2001 and Hillegeist et al 2004) using an up-to-date data set for

US listed firms Based on these models the authors built their own integrated model that is a

multi-period logit model with an expanded set of variables The integrated model which

combined accounting and market data as well as firmsrsquo characteristics outperformed the other

models Altmanrsquos Z-score performed poorly in compared withison to the other 4 models

Shumwayrsquos model performed best Hillegeist et alrsquos model performed adequately and Ohlsonrsquos

and Zmijewskirsquos models performed adequately although their performance deteriorated over

time

Jackson and Wood (2013) tested 13 different models of bankruptcy prediction and assessed their

efficacy using the ROC curve They selected three single- variable models three accounting-based

models (including Altmanrsquos Z-Score) in two versions (with up-dated coefficients and a neural

network approach) and four contingent claims models The contingent claims models and the

latter group outperformed the other models The best four best models were contingent claims

models based on European call and barrier options Although tThe predictive performance

improved withIn the case of the application of the neural network to accounting-based models

the predictive ability improved but it was still lower than within the case of the market-based

models

15

Acosta-Gonzaacutelez and Fernaacutendez-Rodriacuteguez (2014) used genetic algorithms with the Schwarz

information criterion (GASIC) for variable selection combined with the logit model for bankruptcy

prediction Altmanrsquos Z-Score model was used as one of two benchmarks for the authorsrsquo model

evaluation For one- step- ahead forecasting Altmanrsquos model was better in at predicting failed

firms but the type II error was high For two- and three- steps- ahead forecasts the performance

of the models was similar forin terms of failed firms but forin the case of non-failed firms and

the prediction accuracy of Altmanrsquos model was worse For four- steps- ahead forecasts the GASIC

model outperformed the other models forin the case of failed firms and but it performedwas

comparablye forin the case of non-failed firms

In general Altmanrsquos Z-Score model underperformed in compared withison to market-based

models but there was also evidence indicated that it performed well for short-term distress

prediction The question of whether market-based models are perform better than accounting-

based models has been raised many times (eg discussion in Das et al 2009 Bauer and d

Agarwal 2014) Our purpose is not to contribute to this strand of research but rather to focus on

the accounting-based approach In this study we analyze primarily analyze privately held firms

so there are by definition no market data for these firms In this case aAn accounting-based

approach is the only solution that could be appliedapplicable in this case by banks as lenders or

by investors holding debt securities of firms not listed on the stock exchange Thus far most of

the studies have concentrated on the US market only a few of them have used data from other

16

countries such as Japan the UK Lithuania or the G-7 countries Our analysis is a significant

extension of the previous research

4 Methodology and research hypotheses

The literature survey shows that the Z-Score model (publicly traded firms) the Zrsquo-Score model

(private manufacturing firms) and the Zrsquorsquo-Score model (private and publicly

traded manufacturing and non-manufacturing firms) have been adapted for different purposes

In this study we are first interested first in assessing the performance of the original Zrsquorsquo-Score

model in classifying bankrupt and non-bankrupt firms in an international context with special

focus on the European market However we also validate the results in a set of non-European

countries to generalize the results for circumstances outside Europe Second we will re-estimate

the model using extensive international data and then use the re-estimated Zrsquorsquo-Score model as a

benchmark for assessing the effects of different factors on the modelrsquos performance in terms of

classification accuracy We will assess the effects of the following five factors on this

performance year of bankruptcy size of firms age of firms industry and country of origin WIn

all we will test a set of hypotheses based on the effects of the model on performance on two

different levels First we will test a set of hypotheses on a pooled set of all firms and second on

data from each country individually CIn compared withison to the previous research our this

studyrsquos contribution is the focus on an international context not just simply the model

application or re-estimation for of a given countryrsquos data Because weour aim is focusing on the

performance of the Zrsquorsquo-Score mModel when using ain large body of international data the

research hypotheses are of a technical nature and are presented given as follows

17

H1 Obsolescence of the coefficients The Zrsquorsquo-Score mModel was originally estimated using the

same sample of firms that was used to develop the original Z-Score mModel The bankruptcies

in the estimation data occurred during the period 1946ndash-1965 period Thus the oldest

observations are from nearly seventy years ago during the post-war period Altman (1983)

recommended utilizing data as near to the present as possible when developing a bankruptcy

prediction model It is obvious that firmsrsquo the financial behavior of firms and their business

environment have significantly changed since then Thereforen potentially making the

importance of the financial ratios as reflected by the coefficients of the model may differ from

their original importance reflected by the coefficients of the model Therefore we suggest as

the first hypothesis (H1) that the re-estimation of the coefficients of the four original variables

of the Zrsquorsquo-Score model will improve the classification performance of the model in an

international context This hypothesis is supported by the previous research (eg Grice and

Ingram 2001) and by practice H1 aims to support this evidence on the international level and is

expressed as follows

H1 RThe re-estimatingon of the coefficients of the Zrsquorsquo-Score model improves the its

classification accuracy of the model

H2 Method of estimation The original Zrsquorsquo-Score model has been estimated using MDA However

MDA is based on the ordinary least squares (OLS) method and thus requires assumptions of

multinormality homoscedasticity and linearity which are not often met in empirical financial

ratio analysis Therefore we re-estimate the Zrsquorsquo-Score model using logistic regression analysis

(LRA) to assess the effect of the method of estimation method LRA does not require most of the

18

restricting assumptions of MDA In LRA the multivariate normality of the independent variables

is not required nor are homoscedasticity and linearity For the sake of OLS MDA can be more

useful than LRA for small samples such as the original sample of 66 firms used in the estimation

of the Zrsquorsquo-Score mModel However in a large sample LRA may potentially perform better In this

study we use large samples which is advantageous for LRA Therefore our second hypothesis

(H2) suggests is that the classification performance of the re-estimated Zrsquorsquo-Score model will be

improved when it is estimated using LRA instead of MDA The model re-estimated for the original

variables using LRA and all pooled data is called here the Zrsquorsquo-Score LR-modelLR model The

performance of this re-estimated model is used in this study as the benchmark for further

analyses Thus the second hypothesis is as follows

H2 The prediction accuracy of the logistic regression version of the Zrsquorsquo-Score model is higher

than that of the multiple discriminant analysis version

H3 Bankruptcy year The model based on the relationship between bankruptcy and financial

ratios is likely to be affected by the macroeconomic environment These effects may significantly

decrease the classification accuracy of the model If the model is estimated using data from one

year and it will be applied to data from another year the validity of the model can be questioned

Business cycles inIn terms of economic growth credit policy and interest rates business cycles

can ahave an effect on the boundary between bankrupt and non-bankrupt firms The original Zrsquorsquo-

Score model is estimated using data from the period 1946ndash1965 period which includes several

business cycles Therefore the model is not focused on any specific stage of a cycle and does not

explicitly take into account of the bankruptcy year Altman (1983) suggested gathering data from

19

firms for the last most recent few years when developing a prediction model In this study the

benchmark Zrsquorsquo-Score LR -model will beis estimated for a shorter period than in the original

estimation although it covers several recent years on for different stages of the business cycle in

different countries Therefore the third hypothesis (H3) assumes is that the classification

accuracy of the benchmark model can be increased by explicitly taking into account of the year

of bankruptcy in the estimation of the model estimation The third hypothesis is presented as

follows

H3 The prediction accuracy of the modelrsquos prediction accuracy is higher whenith the

effect of the year of bankruptcy is included is higher than that of the model without the

effect

H4 Size of the firm The boundary between bankrupt and non-bankrupt firms is different for small

and larger firms which decreases the performance of the model estimation when estimated for

data from one size category and isare applied to for data from another size category For the

bankrupt and non-bankrupt firms in the original data for Zrsquorsquo-Score model estimation the range

of asset sizes was ranged between approximately 1 and 25 million US dollars The data did not

include very small or very large firms Altman (1983) regarded the suitability of the original Z-

Score model (and likewisein the same way the Zrsquorsquo-Score model) for all firms as debatable

because of this omission it did not scrutinize very large or very small firms In the currentis study

the benchmark Zrsquorsquo-Score LR -model will beis estimated for data from many size categories from

very small firms to very large firms The fourth hypothesis (H4) assumes that the classification

performance of the uniform benchmark LR -model based on the original four financial variables

20

of the Zrsquorsquo-Score model is improved when the size category of the firm is explicitly taken into

account Thus we present the fourth hypothesis as follows

H4 The modelrsquos prediction accuracy of the modelis higher whenith the effect of size is

includede is higher than that of the model without this effect

H5 Age of the firm International insolvency statistics generally show that bankruptcy risk is a

function of the age of the firm Very young firms typically show a very high risk The original Zrsquorsquo-

Score model does not explicitly take age into account age However Altman (1983) noted that

the age of a firm is implicitly considered in the Retained EarningsTotal Assets ratio (X2) which

was regarded as a new ratio in the bankruptcy prediction context A relatively young firm will

probably show a low ratio because it has not had time to build up cumulative profits Thus a

young firm is to some degree discriminated against in the model and its likelihood of being

classified as bankrupt is relatively higher than that of an older firm The incidence of failure is

much higher in the early years of a firm AlthoughDespite that the age of the firm in this way is

implicitly taken into account in X2 we expect that an explicit consideration of the age will improve

the classification accuracy bydue to controlling for the age factor Therefore the fifth hypothesis

(H5) proposes that the performance of the uniform benchmark model based on the original four

financial variables of the Zrsquorsquo-Score model is increasesd when the age of the firm is explicitly taken

into account The fifth hypothesis is can be presented as follows

H5 The modelrsquos prediction accuracy of the model is higher when with the effect of firm

age is higher than that of the model without this effectincluded

21

H6 Industry of the firm The original Zrsquo-Score model was estimated only for manufacturing firms

Altman (1983) stated that it would be ideal to develop a bankruptcy prediction model utilizing a

homogenous group of bankrupt firms If we are interested in a particular industry grouping we

should gather data from bankrupt and non-bankrupt firms in these groupings Previous studies

show that financial distress analysis is influenced by the industry effect (Smith and Liou 2007)

Firms in different industries tend to report different levels of the same financial ratios which may

have an effect on the boundary between bankrupt and non-bankrupt firms This industry effect

may be present in the Zrsquo-Score model especially due to the SalesTotal Assets ratio (X5) This ratio

which showed the least significance on a univariate basis while making but it had made a very

significant contribution to the discriminant power of the multivariate model Altman (1983)

recognized the potential industry effect due to a the wide variation among industries in asset

turnover and specified the Zrsquorsquo-Score model without X5 However also the Zrsquorsquo-Score model was

also estimated using the original sample of manufacturing firms In our analysis the uniform

benchmark model based on the original four financial variables of the Zrsquorsquo-Score model is

estimated for a statistical sample representing different industries Therefore the sixth

hypothesis (H6) assumes that an explicit consideration of industry will improve the classification

accuracy of this benchmark model H6It can be expressed in the following form

H6 The modelrsquos prediction accuracy of the modelis higher whenith the effect of industry is

higher than that of the model without this effectincluded

H7 Country of origin The original Zrsquorsquo-Score model has been estimated only for USUS firms It

can be expected that the international applicability of the model to other countries is affected by

22

country-specific differences The eEconomic environment legislation culture financial markets

and accounting practices in a country may affect the financial behavior of firms and the boundary

between bankrupt and non-bankrupt firms These factors may potentially weaken the

classification performance of the model in for other countries outside other than that e one fore

country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the

seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm

into account will improve the classification accuracy of the benchmark model In our empirical

study the country effect will beis assessed by including a variable of for country risk Therefore

the seventh hypothesis is presented as follows

H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country

risk is higher than that of the model without this effectincluded

5 Empirical data and statistical methods

51 Sample of firms

The principal data of for this study are were extracted from the ORBIS databases of Bureau Van

Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the

moment of sampling contained administrative information on more than 50 million European

firms However income statement and balance sheet information was available for

approximately 8 million companies More than 99 of the companies covered in this database

are private companies from different various industries justifying the use of the Zrsquorsquo-Score model

instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all

industrial groupings and for both private and public entities (Altman 1983 124 Altman and

Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope

23

of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all

industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly

used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that

international comparability may be a problem when administrative firm-level data are

internationally pooled For administrative data Although the definition of variables is usually less

harmonized for administrative data tThis is less of a problem in the ORBIS database because of

the common international format of balance sheets For example although some discrepancies

in profitloss statements may arise because of differences in fiscal systems across countries

balance sheet variables largely adhere to international standards

AThere are a number of factors that influence the international applicability of bankruptcy

prediction models These factors include accounting legislation and practice creditor rights and

investor protection judicial efficiency corporate governance bankruptcy protection and

insolvency management and firm risk-taking These factors strongly differ between European

and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-

Score model outside Europe First it is especiallyparticularly important to include the USUS as

because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It

is include d in our study as well because it has the largest market capitalization in the world

Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it

applicable to emerging market companies Therefore we include firms from China and Colombia

which represent two very culturally and institutionally very dissimilar emerging market

countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt

24

firms ie the limit we set for European countries) from ORBIS World were not

available Thus the results are also estimated and tested for three non-European countries (the

United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-

Score modelrsquos performance The samples of firms from these countries are were extracted from

ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms

from around the world

SFor statistical sampling several requirements are set for the statistical sampling of the empirical

data First we require that the firm to be selected be an industrial (non-financial) company

Second its owners must have limited liability (so partnerships and sole proprietors are left out

of the study) Third we set a minimum requirement for the size of the firm Because the financial

ratios of very small firms are generally too unstable for a failure prediction model these firms

are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have

exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we

include in our estimation sample firms from all European countries and 3 pre-selected non-

European countries where the number of failed firms is more greater than 60 If the number of

failed firms for any European country is less than 60 the firms from this country are only included

only in the test sample For qualifying European countries the firms are randomly classified in

the estimation and test samples so that the number of firms is approximately equal in both

samples Thus our estimation data include firms from 28 European and 3 non-European

countries Fifth all failed firms that fulfill the above requirements are included in our samples

However if the number of non-failed firms in a country is very high a sample is randomly

25

selected from that country Finally the time span of fiscal years potentially available for this study

ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in

the database are from a financial period within 2007 and 2010 earlier years are also excluded

for non-failed firms for comparability All qualifying observations of non-failed firms from the

years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses

of failed firms to the last most recent financial statements available before failure The four

independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize

outliers

Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test

data by country The estimation sample includes data from 2602563 non-failed and 38215

failed firms from 28 European and 3 non-European countries The test sample is slightly larger

because it includes data from 31 European and 3 non-European countries From For the country

of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt

firms The available US data that were available consist only of listed (and delisted) firms From

China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are

included in the estimation sample although there are only 32 such firms altogether7 The Chinese

datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status

are separately analyzed separately only in for the test data8 ST firms are listed firms suffering

6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor

26

from serious financial difficulties Excluding the special US data and the two non-private Chinese

datasets 994 of the observations in the data are private firms

(Table 1 here)

52 Status of failed firms

ORBIS has five classes for potentially active firms (active default of payment receivership

dormant and branch) and seven classes for inactive firms that no longer carry out business

activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation

branch and no precision) From Among these classes only active is selected to represent non-

distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by

considering (with exceptions described below) a firm failed if its status in ORBIS is stated as

bankruptcy However because of the small number of bankrupt firms in some countries we also

consider receivership (active) firms as failed even if they are active These firms generally suffer

from serious financial distress However firms in liquidation are generally not included in the

sample of failed firms Firms in liquidation may depending on the country contain firms that

have ceased activities due to reasons other than failure (mergers discontinuing the operations

of a daughter company or of a foreign branch etc) Therefore for most countries we select only

firms that are coded as being bankrupt or under receivership However there are a number of

special cases where failed firms are coded under a different status heading These countries or

samples are the following

Country Status categories Bulgaria In liquidation Bankruptcy

27

Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the

study (for example Switzerland) If a country had only a very small number of failed firms it was

dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries

including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status

classes (including the bankruptcy category) are not completely homogenous within European

countries due to different legislations although there are obvious similarities in insolvency acts

(Philippe et al 2002) China is a special case because it includes samples with three different

criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are

two different samples (liquidation and receivership)

53 Statistical methods

In this study seven research hypotheses are drawn for statistical testing The statistical analysis

will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The

classification performance of the original model is assessed by the AUC (Area Under Curve)

measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close

connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random

28

model and 05 for a model with an average classification performance In SAS software is used

for all statistical analysess SAS software is used

The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is

tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical

method (multiple discriminant analysis or MDA) The problem is that the estimation sample

includes different numbers of failed and non-failed firms from 31 countries In the original Z-

Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from

the USUS Following the characteristics of these data we weight the firms so that the weights

for the failed and non-failed firms are equally In so doingthis way the non-proportional

sampling in from different countries does will not affect the re-estimated model The number of

firms from different countries however varies significantly leading to greater weights for larger

countries To avoid this problem the observations are also weighted so that each country has an

equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using

these weighted data and the resulting AUC is compared with that based on the original model

The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score

model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is

based on less- restrictive statistical assumptions than MDA In this estimation the dependent

variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA

does not require that independent variables be multivariate normal or that groups have equal

covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA

29

creates a score (logit) score L for every firm It is assumed that the independent variables are

linearly related to L This score or logit is used to determine the conditional probability of failure

as follows

= (eq 4)

where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the

original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing

the statistical significance of the difference between AUCs for this LR model and for the re-

estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it

is used as a benchmark for further statistical AUC comparisons because LR is applied as the

principal method in the testing of the remaining research hypotheses

The third hypothesis (H3) is associated with the performance effect of taking account of the

bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on

the following logit

(eq 5)

where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model

bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and

D1 = 1 when year = 2008 0 otherwise

D2 = 1 when year = 2009 0 otherwise

D3 = 1 when year = 2010 0 otherwise

)|1( XYp )( 441101

1

1

1XbXbbL ee

3

1

4

10

jjji

ii DcXbbL

30

The dummy variables do not directly refer to the bankruptcy year which that is not given in the

data basedatabase but rather to the last available year before bankruptcy For failed firms there

is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is

the base category If the AUC of this extended LR model statistically significantly exceeds the AUC

of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3

Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third

hypothesis above However for each hypothesis appropriate variables are used instead of the

year dummies Hypothesis H4 addresses the performance effect of taking size into account the

size and it will beis tested using by adding two additional variables measuring firm size in to the

LR -model In this LR model size is measured by the natural logarithm of total assets and its

squared form In this way the effect of logarithmic size can be reflected by in a function following

the second-order parabola Hypothesis H5 predicts that the classification performance improves

when the age of the firm is explicitly taken into account When testing this hypothesis the

category 6ndash14 years category is used as the base category and two dummy variables are

incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis

H6 looks at whether is associated with the classification performance is affected bythe effect of

the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt

is tested here using dummy variables for seven industries (D1 restaurants and hotels D2

construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and

water production D7 information technology) with all other industries acting as the base

category

31

Hypothesis H7 predicts that the explicit consideration of the country of origin will improves

classification performance This hypothesis is tested by using country risk measures instead ofnot

using dummy variables for countries but using country risk measures instead The country risk of

each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the

annual closing of accounts The rating is numerically recoded such that the best rating AAA

equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating

D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial

ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-

modelLR models with the original four financial ratios and the additional variables specified in

the hypotheses are estimated for all data In addition an LR model including all additional

variables is estimated for all data to assess the simultaneous effect of all variables Finally six of

the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not

included in this country- level testing hypothesis H7 is not tested because the additional variable

(country risk) is constant within the country

6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models

Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score

model for all data The variation in the ratios is significant as shown by the standard deviation

and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-

failed firms are close to each other indicating a symmetry of distributions However this is not

32

the case for the failed firms For the faileddistressed firms the median exceeds the mean for

these three ratios indicating negatively skewed distributions For X4 (BVETD) the means

significantly exceed the median for both failed and non-failed firms indicating a positively

skewed distribution For each of the four variables both the mean and the median are higher for

non-failed firms than for failed firms which is consistent with the expectations The difference

between the means of the non-failed and failed firms is larger in the original USUS data than in

our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is

approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of

EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may

indicate lower classification accuracy than in the original sample

(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates

(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the

large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model

Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same

statistical method or MDA The coefficients here are negative because the models are estimated

using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight

than in the original USUS data while the weights of WCTA and BVETD have proportionally

decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the

logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR

model These coefficients are directly comparable with those of the MDA model which isas

expected for the this exceptionally large sample The differences in the coefficients of the original

33

four variables among the eight LR-modelLR models (Models 1-8) are small For each model the

coefficient of BVETD is very close to zero This indicatinges that the original four variables and

the additional variables are quite independent of each other

(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-

modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate

that after 2007 (the base category) this risk of failure significantly decreased year by year The

base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due

to as a result of the global financial crisis The crisis played a significant role in the failure of key

businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The

effects were especially felt in Europe The coefficients of Model 4 for the size variables show that

the contribution of size on to the logit (risk measure) is atreaches its maximum value when

logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model

5 confirms the riskiness of young firms because the risk of failure is very high for newly founded

firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The

coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky

industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is

statistically significant (because of the large sample) but negative and very close to zero Finally

the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7

62 All data performance of the Zrsquorsquo-Score models

34

Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1

refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of

all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average

accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)

Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its

performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The

lower part of the table shows the AUCs for the countries included only in the test data The

performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)

samples It is also low and for the liquidation firms in the United Kingdom

The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5

presents the differences of these medians between the non-failed and failed firms by country

This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of

medians This correlation which is high for each financial ratio showing that the effects of the

ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large

except for EBITTA which This implies with the exceptionally high AUC that the ST firms

systematically differ from the non-ST firms although the differences are not extremely large The

differences between the medians are very large in Poland for each ratio justifying the high AUC

They are also large and in Finland and in the Czech Republic where the difference in EBITTA is

average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in

all four ratios are below the average a result thatwhich is obviously associated with a low AUC

35

In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even

negative In Iceland and Ireland the differences only in EBITTA are exceptionally small

(Table 4 here)

(Table 5 here)

Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by

MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)

supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is

at approximately the average level The re-estimation of the coefficients has led to improved

classification accuracy in a number of countries especially in Bosnia China (ST) Norway and

Greece However it has impaired the classification accuracy in the UK and China (delisted)

Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-

modelLR model) showing the effect of the estimation method For the benchmark model the

AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between

among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR

model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country

This result was expected because the coefficients of the models are directly comparable The

similar results for the models may also indicate that the independent variables conform to

multinormality This last latter result confirms what most researchers in the field of default

classification models have concluded that the accuracy levels of MDA and logistical regression

36

models are extremely similar despite any statistical violations such as normality between MDA

and logistical regression models

Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)

than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects

are not positive for all countries The effects are positive for example for Russia Estonia

Germany Ireland and Latvia but these effects are not statistically significant There are

statistically significant negative effects for China (ST) and Serbia There are notable negative

effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are

due to the exceptional annual distributions of failed firms in these countries For the countries

with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the

countries with positive effects it is only a few percent For each group non-failed firms are quite

equally distributed over years When the coefficient of D3 is very low (-0666) it strongly

decreases the risk estimates of most failed firms in the former countries but of only a few failed

firms in the latter countries This which leads to the observed effects H3 is supported by

the level of the whole sample However in some country samples (at the level of individual

levelies) H3 is not supported

Model 4 (LR-modelLR model with size variables) performs better than the benchmark model

which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads

and to significant improvements in the AUC for eg China (delisted) and Austria For China

(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC

37

also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs

in a few countries but thise decrease is not significant Model 4 is based on the four original

variables and the size effect following a second-order parabola This kind of size effect is very

small for micro firms but increases when approaching its maximum value in middle-sized firms

The countries with a positive change in AUC typically have data where in which the percent of

failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized

failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In

contrast the countries with a negative change in AUC have data where in which the percentage

of failed micro firms is exceptionally high (70-80)

Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly

the same AUC as the benchmark model (AUC = 0748) However the difference is positive and

statistically significant giving at least marginal support to H5 (age effect) For nearly all countries

the effect of the age on AUC is small For Austria however thise effect is however positive and

significant For Austria the percentage of non-failed young firms (less than 6 years) among non-

failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed

firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong

positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only

a few non-failed firms which This obviously leads to an improvement in AUC

Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC

supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its

38

AUC is notably higher than the benchmark AUC only in only a few countries such asfor example

France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg

restaurants and hotels and the information technology industryies but it has a positive effect

for on the construction and manufacturing industries For the countries with a positive effect on

AUC the percent of non-failed firms in restaurants and hotels and information technology is

high while that of failed firms is low For the risky industries (construction and manufacturing)

these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many

failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)

firms Therefore the AUC will be increasesd This situation is reversed for the countries with a

negative effect on AUC (Austria and Slovenia) The samples of these countries include a high

percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC

Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher

classification performance (AUC = 0749) than the benchmark model This result gives only very

weak support for H7 (country of origin effect) However for each country the resultant AUC is

nearly identical to that given by the benchmark model This result was expected due to the

negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7

This result implies that country risk hasdoes not have noany effect on the boundary between

bankrupt and non-bankrupt firms

Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional

variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to

39

the benchmark AUC in all test data However the effect on the AUCs largely varies and is either

negative or positive in different countries The positive effect is large in several countries such

as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative

effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show

that the inclusion of additional variables in the original model will usually increase the AUC but

not in every country

The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with

the USUS firms being unlike the majority of other firms in this study being listed or delisted

companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese

firm samples is associated with very small differences between the medians of the non-failed and

failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable

with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed

firms is very high Prior studies based on Chinese ST -firms have also demonstrated good

predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for

additional research and modeling work regarding unlisted and delisted Chinese firms

63 Country-level data performance of the Zrsquorsquo-Score models

The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform

all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the

different models estimated for each country separately (country-level models) In this table the

ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated

40

from the country data this benchmark will beis clearly outperformed by the resulting MDA

(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and

Sweden) However these results give only weak support for H1 at the country level because the

effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and

2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs

given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed

by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation

method)

Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC

than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3

leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United

StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in

nearly every country supporting H4 This improvement is significant however only in the United

StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark

model but for which it is only 0710 for the benchmark model It This improvement is also notable

in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age

category dummies) gives higher AUCs than the benchmark model for several countries but none

of the improvements are significant The positive effect is particularly strong especially in

Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only

weakly supports H5 because these negative effects are relatively small

41

Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects

on AUCs when compared with the benchmark However the only significant effect is the negative

effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the

industry dummies However there are notable positive effects on the AUC in for example

France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak

support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in

AUC compared with the benchmark model in Colombia Iceland and the Unites States It also

leads and to notable improvements in AUC at least in Belgium Denmark Estonia France

Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are

found only in Austria and Slovenia Thus as a conclusion we can concludestate that the

classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can

remarkably increases in most countries when different effects are taken into account by

additional variables This increase is found in most European countries and in Colombia and the

United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-

modelLR model and it can only be slightly improved only slightly by additional variables

(Table 6 here)

7 Summary of the study and a suggested extension

The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model

originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is

assessed using a very large international data set We test how the original version of the Zrsquorsquo-

42

Score model performs in different countries and how re-estimations using another statistical

method and different additional variables affect the classification performance when the data

are very heterogeneous For this kind of testing seven research hypotheses on classification

performance are extractedformulated These research hypotheses are tested for all data and

separately for country data (country-level analysis) The estimation data are from 31 countries

and the results are validated for 34 countries The countries are mainly from Europe but three

non-European countries are included (China Colombia and the United StatesUS) The statuses

used in the classification are mainly bankruptcyactive but receivership firms are also considered

to have failed In the Chinese data ST (special treatment) and delisted firms are also separately

analyzed as failed firms

The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very

satisfactorily in an international context The effects of the four financial ratios on performance

are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small

contribution in re-estimation The original model performs very well in several countries such as

Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only

marginally improved the classification performance thus weakly supporting the obsolescence

hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust

across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion

holds for the re-estimation of the model using LRA because the performance results are very

similar with to those of MDA (H2) The use of additional variables in the model generally

improvesd the classification accuracy of the original model but the results for countries are

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

14

countries and time periods In the case of the re-estimated Z-Score model only profitability and

retained earnings kept maintained their significance for different years and countries

Wu et al (2010) evaluated the performance of five models (Altman 1968 Ohlson 1980

Zmijewski 1984 Shumway 2001 and Hillegeist et al 2004) using an up-to-date data set for

US listed firms Based on these models the authors built their own integrated model that is a

multi-period logit model with an expanded set of variables The integrated model which

combined accounting and market data as well as firmsrsquo characteristics outperformed the other

models Altmanrsquos Z-score performed poorly in compared withison to the other 4 models

Shumwayrsquos model performed best Hillegeist et alrsquos model performed adequately and Ohlsonrsquos

and Zmijewskirsquos models performed adequately although their performance deteriorated over

time

Jackson and Wood (2013) tested 13 different models of bankruptcy prediction and assessed their

efficacy using the ROC curve They selected three single- variable models three accounting-based

models (including Altmanrsquos Z-Score) in two versions (with up-dated coefficients and a neural

network approach) and four contingent claims models The contingent claims models and the

latter group outperformed the other models The best four best models were contingent claims

models based on European call and barrier options Although tThe predictive performance

improved withIn the case of the application of the neural network to accounting-based models

the predictive ability improved but it was still lower than within the case of the market-based

models

15

Acosta-Gonzaacutelez and Fernaacutendez-Rodriacuteguez (2014) used genetic algorithms with the Schwarz

information criterion (GASIC) for variable selection combined with the logit model for bankruptcy

prediction Altmanrsquos Z-Score model was used as one of two benchmarks for the authorsrsquo model

evaluation For one- step- ahead forecasting Altmanrsquos model was better in at predicting failed

firms but the type II error was high For two- and three- steps- ahead forecasts the performance

of the models was similar forin terms of failed firms but forin the case of non-failed firms and

the prediction accuracy of Altmanrsquos model was worse For four- steps- ahead forecasts the GASIC

model outperformed the other models forin the case of failed firms and but it performedwas

comparablye forin the case of non-failed firms

In general Altmanrsquos Z-Score model underperformed in compared withison to market-based

models but there was also evidence indicated that it performed well for short-term distress

prediction The question of whether market-based models are perform better than accounting-

based models has been raised many times (eg discussion in Das et al 2009 Bauer and d

Agarwal 2014) Our purpose is not to contribute to this strand of research but rather to focus on

the accounting-based approach In this study we analyze primarily analyze privately held firms

so there are by definition no market data for these firms In this case aAn accounting-based

approach is the only solution that could be appliedapplicable in this case by banks as lenders or

by investors holding debt securities of firms not listed on the stock exchange Thus far most of

the studies have concentrated on the US market only a few of them have used data from other

16

countries such as Japan the UK Lithuania or the G-7 countries Our analysis is a significant

extension of the previous research

4 Methodology and research hypotheses

The literature survey shows that the Z-Score model (publicly traded firms) the Zrsquo-Score model

(private manufacturing firms) and the Zrsquorsquo-Score model (private and publicly

traded manufacturing and non-manufacturing firms) have been adapted for different purposes

In this study we are first interested first in assessing the performance of the original Zrsquorsquo-Score

model in classifying bankrupt and non-bankrupt firms in an international context with special

focus on the European market However we also validate the results in a set of non-European

countries to generalize the results for circumstances outside Europe Second we will re-estimate

the model using extensive international data and then use the re-estimated Zrsquorsquo-Score model as a

benchmark for assessing the effects of different factors on the modelrsquos performance in terms of

classification accuracy We will assess the effects of the following five factors on this

performance year of bankruptcy size of firms age of firms industry and country of origin WIn

all we will test a set of hypotheses based on the effects of the model on performance on two

different levels First we will test a set of hypotheses on a pooled set of all firms and second on

data from each country individually CIn compared withison to the previous research our this

studyrsquos contribution is the focus on an international context not just simply the model

application or re-estimation for of a given countryrsquos data Because weour aim is focusing on the

performance of the Zrsquorsquo-Score mModel when using ain large body of international data the

research hypotheses are of a technical nature and are presented given as follows

17

H1 Obsolescence of the coefficients The Zrsquorsquo-Score mModel was originally estimated using the

same sample of firms that was used to develop the original Z-Score mModel The bankruptcies

in the estimation data occurred during the period 1946ndash-1965 period Thus the oldest

observations are from nearly seventy years ago during the post-war period Altman (1983)

recommended utilizing data as near to the present as possible when developing a bankruptcy

prediction model It is obvious that firmsrsquo the financial behavior of firms and their business

environment have significantly changed since then Thereforen potentially making the

importance of the financial ratios as reflected by the coefficients of the model may differ from

their original importance reflected by the coefficients of the model Therefore we suggest as

the first hypothesis (H1) that the re-estimation of the coefficients of the four original variables

of the Zrsquorsquo-Score model will improve the classification performance of the model in an

international context This hypothesis is supported by the previous research (eg Grice and

Ingram 2001) and by practice H1 aims to support this evidence on the international level and is

expressed as follows

H1 RThe re-estimatingon of the coefficients of the Zrsquorsquo-Score model improves the its

classification accuracy of the model

H2 Method of estimation The original Zrsquorsquo-Score model has been estimated using MDA However

MDA is based on the ordinary least squares (OLS) method and thus requires assumptions of

multinormality homoscedasticity and linearity which are not often met in empirical financial

ratio analysis Therefore we re-estimate the Zrsquorsquo-Score model using logistic regression analysis

(LRA) to assess the effect of the method of estimation method LRA does not require most of the

18

restricting assumptions of MDA In LRA the multivariate normality of the independent variables

is not required nor are homoscedasticity and linearity For the sake of OLS MDA can be more

useful than LRA for small samples such as the original sample of 66 firms used in the estimation

of the Zrsquorsquo-Score mModel However in a large sample LRA may potentially perform better In this

study we use large samples which is advantageous for LRA Therefore our second hypothesis

(H2) suggests is that the classification performance of the re-estimated Zrsquorsquo-Score model will be

improved when it is estimated using LRA instead of MDA The model re-estimated for the original

variables using LRA and all pooled data is called here the Zrsquorsquo-Score LR-modelLR model The

performance of this re-estimated model is used in this study as the benchmark for further

analyses Thus the second hypothesis is as follows

H2 The prediction accuracy of the logistic regression version of the Zrsquorsquo-Score model is higher

than that of the multiple discriminant analysis version

H3 Bankruptcy year The model based on the relationship between bankruptcy and financial

ratios is likely to be affected by the macroeconomic environment These effects may significantly

decrease the classification accuracy of the model If the model is estimated using data from one

year and it will be applied to data from another year the validity of the model can be questioned

Business cycles inIn terms of economic growth credit policy and interest rates business cycles

can ahave an effect on the boundary between bankrupt and non-bankrupt firms The original Zrsquorsquo-

Score model is estimated using data from the period 1946ndash1965 period which includes several

business cycles Therefore the model is not focused on any specific stage of a cycle and does not

explicitly take into account of the bankruptcy year Altman (1983) suggested gathering data from

19

firms for the last most recent few years when developing a prediction model In this study the

benchmark Zrsquorsquo-Score LR -model will beis estimated for a shorter period than in the original

estimation although it covers several recent years on for different stages of the business cycle in

different countries Therefore the third hypothesis (H3) assumes is that the classification

accuracy of the benchmark model can be increased by explicitly taking into account of the year

of bankruptcy in the estimation of the model estimation The third hypothesis is presented as

follows

H3 The prediction accuracy of the modelrsquos prediction accuracy is higher whenith the

effect of the year of bankruptcy is included is higher than that of the model without the

effect

H4 Size of the firm The boundary between bankrupt and non-bankrupt firms is different for small

and larger firms which decreases the performance of the model estimation when estimated for

data from one size category and isare applied to for data from another size category For the

bankrupt and non-bankrupt firms in the original data for Zrsquorsquo-Score model estimation the range

of asset sizes was ranged between approximately 1 and 25 million US dollars The data did not

include very small or very large firms Altman (1983) regarded the suitability of the original Z-

Score model (and likewisein the same way the Zrsquorsquo-Score model) for all firms as debatable

because of this omission it did not scrutinize very large or very small firms In the currentis study

the benchmark Zrsquorsquo-Score LR -model will beis estimated for data from many size categories from

very small firms to very large firms The fourth hypothesis (H4) assumes that the classification

performance of the uniform benchmark LR -model based on the original four financial variables

20

of the Zrsquorsquo-Score model is improved when the size category of the firm is explicitly taken into

account Thus we present the fourth hypothesis as follows

H4 The modelrsquos prediction accuracy of the modelis higher whenith the effect of size is

includede is higher than that of the model without this effect

H5 Age of the firm International insolvency statistics generally show that bankruptcy risk is a

function of the age of the firm Very young firms typically show a very high risk The original Zrsquorsquo-

Score model does not explicitly take age into account age However Altman (1983) noted that

the age of a firm is implicitly considered in the Retained EarningsTotal Assets ratio (X2) which

was regarded as a new ratio in the bankruptcy prediction context A relatively young firm will

probably show a low ratio because it has not had time to build up cumulative profits Thus a

young firm is to some degree discriminated against in the model and its likelihood of being

classified as bankrupt is relatively higher than that of an older firm The incidence of failure is

much higher in the early years of a firm AlthoughDespite that the age of the firm in this way is

implicitly taken into account in X2 we expect that an explicit consideration of the age will improve

the classification accuracy bydue to controlling for the age factor Therefore the fifth hypothesis

(H5) proposes that the performance of the uniform benchmark model based on the original four

financial variables of the Zrsquorsquo-Score model is increasesd when the age of the firm is explicitly taken

into account The fifth hypothesis is can be presented as follows

H5 The modelrsquos prediction accuracy of the model is higher when with the effect of firm

age is higher than that of the model without this effectincluded

21

H6 Industry of the firm The original Zrsquo-Score model was estimated only for manufacturing firms

Altman (1983) stated that it would be ideal to develop a bankruptcy prediction model utilizing a

homogenous group of bankrupt firms If we are interested in a particular industry grouping we

should gather data from bankrupt and non-bankrupt firms in these groupings Previous studies

show that financial distress analysis is influenced by the industry effect (Smith and Liou 2007)

Firms in different industries tend to report different levels of the same financial ratios which may

have an effect on the boundary between bankrupt and non-bankrupt firms This industry effect

may be present in the Zrsquo-Score model especially due to the SalesTotal Assets ratio (X5) This ratio

which showed the least significance on a univariate basis while making but it had made a very

significant contribution to the discriminant power of the multivariate model Altman (1983)

recognized the potential industry effect due to a the wide variation among industries in asset

turnover and specified the Zrsquorsquo-Score model without X5 However also the Zrsquorsquo-Score model was

also estimated using the original sample of manufacturing firms In our analysis the uniform

benchmark model based on the original four financial variables of the Zrsquorsquo-Score model is

estimated for a statistical sample representing different industries Therefore the sixth

hypothesis (H6) assumes that an explicit consideration of industry will improve the classification

accuracy of this benchmark model H6It can be expressed in the following form

H6 The modelrsquos prediction accuracy of the modelis higher whenith the effect of industry is

higher than that of the model without this effectincluded

H7 Country of origin The original Zrsquorsquo-Score model has been estimated only for USUS firms It

can be expected that the international applicability of the model to other countries is affected by

22

country-specific differences The eEconomic environment legislation culture financial markets

and accounting practices in a country may affect the financial behavior of firms and the boundary

between bankrupt and non-bankrupt firms These factors may potentially weaken the

classification performance of the model in for other countries outside other than that e one fore

country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the

seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm

into account will improve the classification accuracy of the benchmark model In our empirical

study the country effect will beis assessed by including a variable of for country risk Therefore

the seventh hypothesis is presented as follows

H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country

risk is higher than that of the model without this effectincluded

5 Empirical data and statistical methods

51 Sample of firms

The principal data of for this study are were extracted from the ORBIS databases of Bureau Van

Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the

moment of sampling contained administrative information on more than 50 million European

firms However income statement and balance sheet information was available for

approximately 8 million companies More than 99 of the companies covered in this database

are private companies from different various industries justifying the use of the Zrsquorsquo-Score model

instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all

industrial groupings and for both private and public entities (Altman 1983 124 Altman and

Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope

23

of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all

industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly

used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that

international comparability may be a problem when administrative firm-level data are

internationally pooled For administrative data Although the definition of variables is usually less

harmonized for administrative data tThis is less of a problem in the ORBIS database because of

the common international format of balance sheets For example although some discrepancies

in profitloss statements may arise because of differences in fiscal systems across countries

balance sheet variables largely adhere to international standards

AThere are a number of factors that influence the international applicability of bankruptcy

prediction models These factors include accounting legislation and practice creditor rights and

investor protection judicial efficiency corporate governance bankruptcy protection and

insolvency management and firm risk-taking These factors strongly differ between European

and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-

Score model outside Europe First it is especiallyparticularly important to include the USUS as

because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It

is include d in our study as well because it has the largest market capitalization in the world

Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it

applicable to emerging market companies Therefore we include firms from China and Colombia

which represent two very culturally and institutionally very dissimilar emerging market

countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt

24

firms ie the limit we set for European countries) from ORBIS World were not

available Thus the results are also estimated and tested for three non-European countries (the

United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-

Score modelrsquos performance The samples of firms from these countries are were extracted from

ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms

from around the world

SFor statistical sampling several requirements are set for the statistical sampling of the empirical

data First we require that the firm to be selected be an industrial (non-financial) company

Second its owners must have limited liability (so partnerships and sole proprietors are left out

of the study) Third we set a minimum requirement for the size of the firm Because the financial

ratios of very small firms are generally too unstable for a failure prediction model these firms

are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have

exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we

include in our estimation sample firms from all European countries and 3 pre-selected non-

European countries where the number of failed firms is more greater than 60 If the number of

failed firms for any European country is less than 60 the firms from this country are only included

only in the test sample For qualifying European countries the firms are randomly classified in

the estimation and test samples so that the number of firms is approximately equal in both

samples Thus our estimation data include firms from 28 European and 3 non-European

countries Fifth all failed firms that fulfill the above requirements are included in our samples

However if the number of non-failed firms in a country is very high a sample is randomly

25

selected from that country Finally the time span of fiscal years potentially available for this study

ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in

the database are from a financial period within 2007 and 2010 earlier years are also excluded

for non-failed firms for comparability All qualifying observations of non-failed firms from the

years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses

of failed firms to the last most recent financial statements available before failure The four

independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize

outliers

Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test

data by country The estimation sample includes data from 2602563 non-failed and 38215

failed firms from 28 European and 3 non-European countries The test sample is slightly larger

because it includes data from 31 European and 3 non-European countries From For the country

of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt

firms The available US data that were available consist only of listed (and delisted) firms From

China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are

included in the estimation sample although there are only 32 such firms altogether7 The Chinese

datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status

are separately analyzed separately only in for the test data8 ST firms are listed firms suffering

6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor

26

from serious financial difficulties Excluding the special US data and the two non-private Chinese

datasets 994 of the observations in the data are private firms

(Table 1 here)

52 Status of failed firms

ORBIS has five classes for potentially active firms (active default of payment receivership

dormant and branch) and seven classes for inactive firms that no longer carry out business

activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation

branch and no precision) From Among these classes only active is selected to represent non-

distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by

considering (with exceptions described below) a firm failed if its status in ORBIS is stated as

bankruptcy However because of the small number of bankrupt firms in some countries we also

consider receivership (active) firms as failed even if they are active These firms generally suffer

from serious financial distress However firms in liquidation are generally not included in the

sample of failed firms Firms in liquidation may depending on the country contain firms that

have ceased activities due to reasons other than failure (mergers discontinuing the operations

of a daughter company or of a foreign branch etc) Therefore for most countries we select only

firms that are coded as being bankrupt or under receivership However there are a number of

special cases where failed firms are coded under a different status heading These countries or

samples are the following

Country Status categories Bulgaria In liquidation Bankruptcy

27

Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the

study (for example Switzerland) If a country had only a very small number of failed firms it was

dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries

including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status

classes (including the bankruptcy category) are not completely homogenous within European

countries due to different legislations although there are obvious similarities in insolvency acts

(Philippe et al 2002) China is a special case because it includes samples with three different

criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are

two different samples (liquidation and receivership)

53 Statistical methods

In this study seven research hypotheses are drawn for statistical testing The statistical analysis

will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The

classification performance of the original model is assessed by the AUC (Area Under Curve)

measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close

connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random

28

model and 05 for a model with an average classification performance In SAS software is used

for all statistical analysess SAS software is used

The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is

tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical

method (multiple discriminant analysis or MDA) The problem is that the estimation sample

includes different numbers of failed and non-failed firms from 31 countries In the original Z-

Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from

the USUS Following the characteristics of these data we weight the firms so that the weights

for the failed and non-failed firms are equally In so doingthis way the non-proportional

sampling in from different countries does will not affect the re-estimated model The number of

firms from different countries however varies significantly leading to greater weights for larger

countries To avoid this problem the observations are also weighted so that each country has an

equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using

these weighted data and the resulting AUC is compared with that based on the original model

The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score

model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is

based on less- restrictive statistical assumptions than MDA In this estimation the dependent

variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA

does not require that independent variables be multivariate normal or that groups have equal

covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA

29

creates a score (logit) score L for every firm It is assumed that the independent variables are

linearly related to L This score or logit is used to determine the conditional probability of failure

as follows

= (eq 4)

where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the

original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing

the statistical significance of the difference between AUCs for this LR model and for the re-

estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it

is used as a benchmark for further statistical AUC comparisons because LR is applied as the

principal method in the testing of the remaining research hypotheses

The third hypothesis (H3) is associated with the performance effect of taking account of the

bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on

the following logit

(eq 5)

where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model

bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and

D1 = 1 when year = 2008 0 otherwise

D2 = 1 when year = 2009 0 otherwise

D3 = 1 when year = 2010 0 otherwise

)|1( XYp )( 441101

1

1

1XbXbbL ee

3

1

4

10

jjji

ii DcXbbL

30

The dummy variables do not directly refer to the bankruptcy year which that is not given in the

data basedatabase but rather to the last available year before bankruptcy For failed firms there

is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is

the base category If the AUC of this extended LR model statistically significantly exceeds the AUC

of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3

Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third

hypothesis above However for each hypothesis appropriate variables are used instead of the

year dummies Hypothesis H4 addresses the performance effect of taking size into account the

size and it will beis tested using by adding two additional variables measuring firm size in to the

LR -model In this LR model size is measured by the natural logarithm of total assets and its

squared form In this way the effect of logarithmic size can be reflected by in a function following

the second-order parabola Hypothesis H5 predicts that the classification performance improves

when the age of the firm is explicitly taken into account When testing this hypothesis the

category 6ndash14 years category is used as the base category and two dummy variables are

incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis

H6 looks at whether is associated with the classification performance is affected bythe effect of

the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt

is tested here using dummy variables for seven industries (D1 restaurants and hotels D2

construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and

water production D7 information technology) with all other industries acting as the base

category

31

Hypothesis H7 predicts that the explicit consideration of the country of origin will improves

classification performance This hypothesis is tested by using country risk measures instead ofnot

using dummy variables for countries but using country risk measures instead The country risk of

each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the

annual closing of accounts The rating is numerically recoded such that the best rating AAA

equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating

D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial

ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-

modelLR models with the original four financial ratios and the additional variables specified in

the hypotheses are estimated for all data In addition an LR model including all additional

variables is estimated for all data to assess the simultaneous effect of all variables Finally six of

the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not

included in this country- level testing hypothesis H7 is not tested because the additional variable

(country risk) is constant within the country

6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models

Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score

model for all data The variation in the ratios is significant as shown by the standard deviation

and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-

failed firms are close to each other indicating a symmetry of distributions However this is not

32

the case for the failed firms For the faileddistressed firms the median exceeds the mean for

these three ratios indicating negatively skewed distributions For X4 (BVETD) the means

significantly exceed the median for both failed and non-failed firms indicating a positively

skewed distribution For each of the four variables both the mean and the median are higher for

non-failed firms than for failed firms which is consistent with the expectations The difference

between the means of the non-failed and failed firms is larger in the original USUS data than in

our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is

approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of

EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may

indicate lower classification accuracy than in the original sample

(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates

(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the

large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model

Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same

statistical method or MDA The coefficients here are negative because the models are estimated

using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight

than in the original USUS data while the weights of WCTA and BVETD have proportionally

decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the

logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR

model These coefficients are directly comparable with those of the MDA model which isas

expected for the this exceptionally large sample The differences in the coefficients of the original

33

four variables among the eight LR-modelLR models (Models 1-8) are small For each model the

coefficient of BVETD is very close to zero This indicatinges that the original four variables and

the additional variables are quite independent of each other

(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-

modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate

that after 2007 (the base category) this risk of failure significantly decreased year by year The

base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due

to as a result of the global financial crisis The crisis played a significant role in the failure of key

businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The

effects were especially felt in Europe The coefficients of Model 4 for the size variables show that

the contribution of size on to the logit (risk measure) is atreaches its maximum value when

logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model

5 confirms the riskiness of young firms because the risk of failure is very high for newly founded

firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The

coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky

industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is

statistically significant (because of the large sample) but negative and very close to zero Finally

the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7

62 All data performance of the Zrsquorsquo-Score models

34

Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1

refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of

all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average

accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)

Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its

performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The

lower part of the table shows the AUCs for the countries included only in the test data The

performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)

samples It is also low and for the liquidation firms in the United Kingdom

The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5

presents the differences of these medians between the non-failed and failed firms by country

This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of

medians This correlation which is high for each financial ratio showing that the effects of the

ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large

except for EBITTA which This implies with the exceptionally high AUC that the ST firms

systematically differ from the non-ST firms although the differences are not extremely large The

differences between the medians are very large in Poland for each ratio justifying the high AUC

They are also large and in Finland and in the Czech Republic where the difference in EBITTA is

average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in

all four ratios are below the average a result thatwhich is obviously associated with a low AUC

35

In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even

negative In Iceland and Ireland the differences only in EBITTA are exceptionally small

(Table 4 here)

(Table 5 here)

Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by

MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)

supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is

at approximately the average level The re-estimation of the coefficients has led to improved

classification accuracy in a number of countries especially in Bosnia China (ST) Norway and

Greece However it has impaired the classification accuracy in the UK and China (delisted)

Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-

modelLR model) showing the effect of the estimation method For the benchmark model the

AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between

among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR

model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country

This result was expected because the coefficients of the models are directly comparable The

similar results for the models may also indicate that the independent variables conform to

multinormality This last latter result confirms what most researchers in the field of default

classification models have concluded that the accuracy levels of MDA and logistical regression

36

models are extremely similar despite any statistical violations such as normality between MDA

and logistical regression models

Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)

than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects

are not positive for all countries The effects are positive for example for Russia Estonia

Germany Ireland and Latvia but these effects are not statistically significant There are

statistically significant negative effects for China (ST) and Serbia There are notable negative

effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are

due to the exceptional annual distributions of failed firms in these countries For the countries

with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the

countries with positive effects it is only a few percent For each group non-failed firms are quite

equally distributed over years When the coefficient of D3 is very low (-0666) it strongly

decreases the risk estimates of most failed firms in the former countries but of only a few failed

firms in the latter countries This which leads to the observed effects H3 is supported by

the level of the whole sample However in some country samples (at the level of individual

levelies) H3 is not supported

Model 4 (LR-modelLR model with size variables) performs better than the benchmark model

which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads

and to significant improvements in the AUC for eg China (delisted) and Austria For China

(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC

37

also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs

in a few countries but thise decrease is not significant Model 4 is based on the four original

variables and the size effect following a second-order parabola This kind of size effect is very

small for micro firms but increases when approaching its maximum value in middle-sized firms

The countries with a positive change in AUC typically have data where in which the percent of

failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized

failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In

contrast the countries with a negative change in AUC have data where in which the percentage

of failed micro firms is exceptionally high (70-80)

Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly

the same AUC as the benchmark model (AUC = 0748) However the difference is positive and

statistically significant giving at least marginal support to H5 (age effect) For nearly all countries

the effect of the age on AUC is small For Austria however thise effect is however positive and

significant For Austria the percentage of non-failed young firms (less than 6 years) among non-

failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed

firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong

positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only

a few non-failed firms which This obviously leads to an improvement in AUC

Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC

supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its

38

AUC is notably higher than the benchmark AUC only in only a few countries such asfor example

France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg

restaurants and hotels and the information technology industryies but it has a positive effect

for on the construction and manufacturing industries For the countries with a positive effect on

AUC the percent of non-failed firms in restaurants and hotels and information technology is

high while that of failed firms is low For the risky industries (construction and manufacturing)

these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many

failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)

firms Therefore the AUC will be increasesd This situation is reversed for the countries with a

negative effect on AUC (Austria and Slovenia) The samples of these countries include a high

percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC

Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher

classification performance (AUC = 0749) than the benchmark model This result gives only very

weak support for H7 (country of origin effect) However for each country the resultant AUC is

nearly identical to that given by the benchmark model This result was expected due to the

negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7

This result implies that country risk hasdoes not have noany effect on the boundary between

bankrupt and non-bankrupt firms

Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional

variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to

39

the benchmark AUC in all test data However the effect on the AUCs largely varies and is either

negative or positive in different countries The positive effect is large in several countries such

as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative

effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show

that the inclusion of additional variables in the original model will usually increase the AUC but

not in every country

The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with

the USUS firms being unlike the majority of other firms in this study being listed or delisted

companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese

firm samples is associated with very small differences between the medians of the non-failed and

failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable

with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed

firms is very high Prior studies based on Chinese ST -firms have also demonstrated good

predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for

additional research and modeling work regarding unlisted and delisted Chinese firms

63 Country-level data performance of the Zrsquorsquo-Score models

The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform

all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the

different models estimated for each country separately (country-level models) In this table the

ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated

40

from the country data this benchmark will beis clearly outperformed by the resulting MDA

(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and

Sweden) However these results give only weak support for H1 at the country level because the

effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and

2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs

given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed

by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation

method)

Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC

than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3

leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United

StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in

nearly every country supporting H4 This improvement is significant however only in the United

StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark

model but for which it is only 0710 for the benchmark model It This improvement is also notable

in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age

category dummies) gives higher AUCs than the benchmark model for several countries but none

of the improvements are significant The positive effect is particularly strong especially in

Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only

weakly supports H5 because these negative effects are relatively small

41

Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects

on AUCs when compared with the benchmark However the only significant effect is the negative

effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the

industry dummies However there are notable positive effects on the AUC in for example

France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak

support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in

AUC compared with the benchmark model in Colombia Iceland and the Unites States It also

leads and to notable improvements in AUC at least in Belgium Denmark Estonia France

Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are

found only in Austria and Slovenia Thus as a conclusion we can concludestate that the

classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can

remarkably increases in most countries when different effects are taken into account by

additional variables This increase is found in most European countries and in Colombia and the

United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-

modelLR model and it can only be slightly improved only slightly by additional variables

(Table 6 here)

7 Summary of the study and a suggested extension

The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model

originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is

assessed using a very large international data set We test how the original version of the Zrsquorsquo-

42

Score model performs in different countries and how re-estimations using another statistical

method and different additional variables affect the classification performance when the data

are very heterogeneous For this kind of testing seven research hypotheses on classification

performance are extractedformulated These research hypotheses are tested for all data and

separately for country data (country-level analysis) The estimation data are from 31 countries

and the results are validated for 34 countries The countries are mainly from Europe but three

non-European countries are included (China Colombia and the United StatesUS) The statuses

used in the classification are mainly bankruptcyactive but receivership firms are also considered

to have failed In the Chinese data ST (special treatment) and delisted firms are also separately

analyzed as failed firms

The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very

satisfactorily in an international context The effects of the four financial ratios on performance

are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small

contribution in re-estimation The original model performs very well in several countries such as

Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only

marginally improved the classification performance thus weakly supporting the obsolescence

hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust

across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion

holds for the re-estimation of the model using LRA because the performance results are very

similar with to those of MDA (H2) The use of additional variables in the model generally

improvesd the classification accuracy of the original model but the results for countries are

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

15

Acosta-Gonzaacutelez and Fernaacutendez-Rodriacuteguez (2014) used genetic algorithms with the Schwarz

information criterion (GASIC) for variable selection combined with the logit model for bankruptcy

prediction Altmanrsquos Z-Score model was used as one of two benchmarks for the authorsrsquo model

evaluation For one- step- ahead forecasting Altmanrsquos model was better in at predicting failed

firms but the type II error was high For two- and three- steps- ahead forecasts the performance

of the models was similar forin terms of failed firms but forin the case of non-failed firms and

the prediction accuracy of Altmanrsquos model was worse For four- steps- ahead forecasts the GASIC

model outperformed the other models forin the case of failed firms and but it performedwas

comparablye forin the case of non-failed firms

In general Altmanrsquos Z-Score model underperformed in compared withison to market-based

models but there was also evidence indicated that it performed well for short-term distress

prediction The question of whether market-based models are perform better than accounting-

based models has been raised many times (eg discussion in Das et al 2009 Bauer and d

Agarwal 2014) Our purpose is not to contribute to this strand of research but rather to focus on

the accounting-based approach In this study we analyze primarily analyze privately held firms

so there are by definition no market data for these firms In this case aAn accounting-based

approach is the only solution that could be appliedapplicable in this case by banks as lenders or

by investors holding debt securities of firms not listed on the stock exchange Thus far most of

the studies have concentrated on the US market only a few of them have used data from other

16

countries such as Japan the UK Lithuania or the G-7 countries Our analysis is a significant

extension of the previous research

4 Methodology and research hypotheses

The literature survey shows that the Z-Score model (publicly traded firms) the Zrsquo-Score model

(private manufacturing firms) and the Zrsquorsquo-Score model (private and publicly

traded manufacturing and non-manufacturing firms) have been adapted for different purposes

In this study we are first interested first in assessing the performance of the original Zrsquorsquo-Score

model in classifying bankrupt and non-bankrupt firms in an international context with special

focus on the European market However we also validate the results in a set of non-European

countries to generalize the results for circumstances outside Europe Second we will re-estimate

the model using extensive international data and then use the re-estimated Zrsquorsquo-Score model as a

benchmark for assessing the effects of different factors on the modelrsquos performance in terms of

classification accuracy We will assess the effects of the following five factors on this

performance year of bankruptcy size of firms age of firms industry and country of origin WIn

all we will test a set of hypotheses based on the effects of the model on performance on two

different levels First we will test a set of hypotheses on a pooled set of all firms and second on

data from each country individually CIn compared withison to the previous research our this

studyrsquos contribution is the focus on an international context not just simply the model

application or re-estimation for of a given countryrsquos data Because weour aim is focusing on the

performance of the Zrsquorsquo-Score mModel when using ain large body of international data the

research hypotheses are of a technical nature and are presented given as follows

17

H1 Obsolescence of the coefficients The Zrsquorsquo-Score mModel was originally estimated using the

same sample of firms that was used to develop the original Z-Score mModel The bankruptcies

in the estimation data occurred during the period 1946ndash-1965 period Thus the oldest

observations are from nearly seventy years ago during the post-war period Altman (1983)

recommended utilizing data as near to the present as possible when developing a bankruptcy

prediction model It is obvious that firmsrsquo the financial behavior of firms and their business

environment have significantly changed since then Thereforen potentially making the

importance of the financial ratios as reflected by the coefficients of the model may differ from

their original importance reflected by the coefficients of the model Therefore we suggest as

the first hypothesis (H1) that the re-estimation of the coefficients of the four original variables

of the Zrsquorsquo-Score model will improve the classification performance of the model in an

international context This hypothesis is supported by the previous research (eg Grice and

Ingram 2001) and by practice H1 aims to support this evidence on the international level and is

expressed as follows

H1 RThe re-estimatingon of the coefficients of the Zrsquorsquo-Score model improves the its

classification accuracy of the model

H2 Method of estimation The original Zrsquorsquo-Score model has been estimated using MDA However

MDA is based on the ordinary least squares (OLS) method and thus requires assumptions of

multinormality homoscedasticity and linearity which are not often met in empirical financial

ratio analysis Therefore we re-estimate the Zrsquorsquo-Score model using logistic regression analysis

(LRA) to assess the effect of the method of estimation method LRA does not require most of the

18

restricting assumptions of MDA In LRA the multivariate normality of the independent variables

is not required nor are homoscedasticity and linearity For the sake of OLS MDA can be more

useful than LRA for small samples such as the original sample of 66 firms used in the estimation

of the Zrsquorsquo-Score mModel However in a large sample LRA may potentially perform better In this

study we use large samples which is advantageous for LRA Therefore our second hypothesis

(H2) suggests is that the classification performance of the re-estimated Zrsquorsquo-Score model will be

improved when it is estimated using LRA instead of MDA The model re-estimated for the original

variables using LRA and all pooled data is called here the Zrsquorsquo-Score LR-modelLR model The

performance of this re-estimated model is used in this study as the benchmark for further

analyses Thus the second hypothesis is as follows

H2 The prediction accuracy of the logistic regression version of the Zrsquorsquo-Score model is higher

than that of the multiple discriminant analysis version

H3 Bankruptcy year The model based on the relationship between bankruptcy and financial

ratios is likely to be affected by the macroeconomic environment These effects may significantly

decrease the classification accuracy of the model If the model is estimated using data from one

year and it will be applied to data from another year the validity of the model can be questioned

Business cycles inIn terms of economic growth credit policy and interest rates business cycles

can ahave an effect on the boundary between bankrupt and non-bankrupt firms The original Zrsquorsquo-

Score model is estimated using data from the period 1946ndash1965 period which includes several

business cycles Therefore the model is not focused on any specific stage of a cycle and does not

explicitly take into account of the bankruptcy year Altman (1983) suggested gathering data from

19

firms for the last most recent few years when developing a prediction model In this study the

benchmark Zrsquorsquo-Score LR -model will beis estimated for a shorter period than in the original

estimation although it covers several recent years on for different stages of the business cycle in

different countries Therefore the third hypothesis (H3) assumes is that the classification

accuracy of the benchmark model can be increased by explicitly taking into account of the year

of bankruptcy in the estimation of the model estimation The third hypothesis is presented as

follows

H3 The prediction accuracy of the modelrsquos prediction accuracy is higher whenith the

effect of the year of bankruptcy is included is higher than that of the model without the

effect

H4 Size of the firm The boundary between bankrupt and non-bankrupt firms is different for small

and larger firms which decreases the performance of the model estimation when estimated for

data from one size category and isare applied to for data from another size category For the

bankrupt and non-bankrupt firms in the original data for Zrsquorsquo-Score model estimation the range

of asset sizes was ranged between approximately 1 and 25 million US dollars The data did not

include very small or very large firms Altman (1983) regarded the suitability of the original Z-

Score model (and likewisein the same way the Zrsquorsquo-Score model) for all firms as debatable

because of this omission it did not scrutinize very large or very small firms In the currentis study

the benchmark Zrsquorsquo-Score LR -model will beis estimated for data from many size categories from

very small firms to very large firms The fourth hypothesis (H4) assumes that the classification

performance of the uniform benchmark LR -model based on the original four financial variables

20

of the Zrsquorsquo-Score model is improved when the size category of the firm is explicitly taken into

account Thus we present the fourth hypothesis as follows

H4 The modelrsquos prediction accuracy of the modelis higher whenith the effect of size is

includede is higher than that of the model without this effect

H5 Age of the firm International insolvency statistics generally show that bankruptcy risk is a

function of the age of the firm Very young firms typically show a very high risk The original Zrsquorsquo-

Score model does not explicitly take age into account age However Altman (1983) noted that

the age of a firm is implicitly considered in the Retained EarningsTotal Assets ratio (X2) which

was regarded as a new ratio in the bankruptcy prediction context A relatively young firm will

probably show a low ratio because it has not had time to build up cumulative profits Thus a

young firm is to some degree discriminated against in the model and its likelihood of being

classified as bankrupt is relatively higher than that of an older firm The incidence of failure is

much higher in the early years of a firm AlthoughDespite that the age of the firm in this way is

implicitly taken into account in X2 we expect that an explicit consideration of the age will improve

the classification accuracy bydue to controlling for the age factor Therefore the fifth hypothesis

(H5) proposes that the performance of the uniform benchmark model based on the original four

financial variables of the Zrsquorsquo-Score model is increasesd when the age of the firm is explicitly taken

into account The fifth hypothesis is can be presented as follows

H5 The modelrsquos prediction accuracy of the model is higher when with the effect of firm

age is higher than that of the model without this effectincluded

21

H6 Industry of the firm The original Zrsquo-Score model was estimated only for manufacturing firms

Altman (1983) stated that it would be ideal to develop a bankruptcy prediction model utilizing a

homogenous group of bankrupt firms If we are interested in a particular industry grouping we

should gather data from bankrupt and non-bankrupt firms in these groupings Previous studies

show that financial distress analysis is influenced by the industry effect (Smith and Liou 2007)

Firms in different industries tend to report different levels of the same financial ratios which may

have an effect on the boundary between bankrupt and non-bankrupt firms This industry effect

may be present in the Zrsquo-Score model especially due to the SalesTotal Assets ratio (X5) This ratio

which showed the least significance on a univariate basis while making but it had made a very

significant contribution to the discriminant power of the multivariate model Altman (1983)

recognized the potential industry effect due to a the wide variation among industries in asset

turnover and specified the Zrsquorsquo-Score model without X5 However also the Zrsquorsquo-Score model was

also estimated using the original sample of manufacturing firms In our analysis the uniform

benchmark model based on the original four financial variables of the Zrsquorsquo-Score model is

estimated for a statistical sample representing different industries Therefore the sixth

hypothesis (H6) assumes that an explicit consideration of industry will improve the classification

accuracy of this benchmark model H6It can be expressed in the following form

H6 The modelrsquos prediction accuracy of the modelis higher whenith the effect of industry is

higher than that of the model without this effectincluded

H7 Country of origin The original Zrsquorsquo-Score model has been estimated only for USUS firms It

can be expected that the international applicability of the model to other countries is affected by

22

country-specific differences The eEconomic environment legislation culture financial markets

and accounting practices in a country may affect the financial behavior of firms and the boundary

between bankrupt and non-bankrupt firms These factors may potentially weaken the

classification performance of the model in for other countries outside other than that e one fore

country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the

seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm

into account will improve the classification accuracy of the benchmark model In our empirical

study the country effect will beis assessed by including a variable of for country risk Therefore

the seventh hypothesis is presented as follows

H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country

risk is higher than that of the model without this effectincluded

5 Empirical data and statistical methods

51 Sample of firms

The principal data of for this study are were extracted from the ORBIS databases of Bureau Van

Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the

moment of sampling contained administrative information on more than 50 million European

firms However income statement and balance sheet information was available for

approximately 8 million companies More than 99 of the companies covered in this database

are private companies from different various industries justifying the use of the Zrsquorsquo-Score model

instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all

industrial groupings and for both private and public entities (Altman 1983 124 Altman and

Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope

23

of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all

industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly

used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that

international comparability may be a problem when administrative firm-level data are

internationally pooled For administrative data Although the definition of variables is usually less

harmonized for administrative data tThis is less of a problem in the ORBIS database because of

the common international format of balance sheets For example although some discrepancies

in profitloss statements may arise because of differences in fiscal systems across countries

balance sheet variables largely adhere to international standards

AThere are a number of factors that influence the international applicability of bankruptcy

prediction models These factors include accounting legislation and practice creditor rights and

investor protection judicial efficiency corporate governance bankruptcy protection and

insolvency management and firm risk-taking These factors strongly differ between European

and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-

Score model outside Europe First it is especiallyparticularly important to include the USUS as

because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It

is include d in our study as well because it has the largest market capitalization in the world

Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it

applicable to emerging market companies Therefore we include firms from China and Colombia

which represent two very culturally and institutionally very dissimilar emerging market

countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt

24

firms ie the limit we set for European countries) from ORBIS World were not

available Thus the results are also estimated and tested for three non-European countries (the

United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-

Score modelrsquos performance The samples of firms from these countries are were extracted from

ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms

from around the world

SFor statistical sampling several requirements are set for the statistical sampling of the empirical

data First we require that the firm to be selected be an industrial (non-financial) company

Second its owners must have limited liability (so partnerships and sole proprietors are left out

of the study) Third we set a minimum requirement for the size of the firm Because the financial

ratios of very small firms are generally too unstable for a failure prediction model these firms

are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have

exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we

include in our estimation sample firms from all European countries and 3 pre-selected non-

European countries where the number of failed firms is more greater than 60 If the number of

failed firms for any European country is less than 60 the firms from this country are only included

only in the test sample For qualifying European countries the firms are randomly classified in

the estimation and test samples so that the number of firms is approximately equal in both

samples Thus our estimation data include firms from 28 European and 3 non-European

countries Fifth all failed firms that fulfill the above requirements are included in our samples

However if the number of non-failed firms in a country is very high a sample is randomly

25

selected from that country Finally the time span of fiscal years potentially available for this study

ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in

the database are from a financial period within 2007 and 2010 earlier years are also excluded

for non-failed firms for comparability All qualifying observations of non-failed firms from the

years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses

of failed firms to the last most recent financial statements available before failure The four

independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize

outliers

Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test

data by country The estimation sample includes data from 2602563 non-failed and 38215

failed firms from 28 European and 3 non-European countries The test sample is slightly larger

because it includes data from 31 European and 3 non-European countries From For the country

of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt

firms The available US data that were available consist only of listed (and delisted) firms From

China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are

included in the estimation sample although there are only 32 such firms altogether7 The Chinese

datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status

are separately analyzed separately only in for the test data8 ST firms are listed firms suffering

6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor

26

from serious financial difficulties Excluding the special US data and the two non-private Chinese

datasets 994 of the observations in the data are private firms

(Table 1 here)

52 Status of failed firms

ORBIS has five classes for potentially active firms (active default of payment receivership

dormant and branch) and seven classes for inactive firms that no longer carry out business

activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation

branch and no precision) From Among these classes only active is selected to represent non-

distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by

considering (with exceptions described below) a firm failed if its status in ORBIS is stated as

bankruptcy However because of the small number of bankrupt firms in some countries we also

consider receivership (active) firms as failed even if they are active These firms generally suffer

from serious financial distress However firms in liquidation are generally not included in the

sample of failed firms Firms in liquidation may depending on the country contain firms that

have ceased activities due to reasons other than failure (mergers discontinuing the operations

of a daughter company or of a foreign branch etc) Therefore for most countries we select only

firms that are coded as being bankrupt or under receivership However there are a number of

special cases where failed firms are coded under a different status heading These countries or

samples are the following

Country Status categories Bulgaria In liquidation Bankruptcy

27

Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the

study (for example Switzerland) If a country had only a very small number of failed firms it was

dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries

including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status

classes (including the bankruptcy category) are not completely homogenous within European

countries due to different legislations although there are obvious similarities in insolvency acts

(Philippe et al 2002) China is a special case because it includes samples with three different

criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are

two different samples (liquidation and receivership)

53 Statistical methods

In this study seven research hypotheses are drawn for statistical testing The statistical analysis

will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The

classification performance of the original model is assessed by the AUC (Area Under Curve)

measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close

connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random

28

model and 05 for a model with an average classification performance In SAS software is used

for all statistical analysess SAS software is used

The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is

tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical

method (multiple discriminant analysis or MDA) The problem is that the estimation sample

includes different numbers of failed and non-failed firms from 31 countries In the original Z-

Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from

the USUS Following the characteristics of these data we weight the firms so that the weights

for the failed and non-failed firms are equally In so doingthis way the non-proportional

sampling in from different countries does will not affect the re-estimated model The number of

firms from different countries however varies significantly leading to greater weights for larger

countries To avoid this problem the observations are also weighted so that each country has an

equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using

these weighted data and the resulting AUC is compared with that based on the original model

The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score

model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is

based on less- restrictive statistical assumptions than MDA In this estimation the dependent

variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA

does not require that independent variables be multivariate normal or that groups have equal

covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA

29

creates a score (logit) score L for every firm It is assumed that the independent variables are

linearly related to L This score or logit is used to determine the conditional probability of failure

as follows

= (eq 4)

where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the

original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing

the statistical significance of the difference between AUCs for this LR model and for the re-

estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it

is used as a benchmark for further statistical AUC comparisons because LR is applied as the

principal method in the testing of the remaining research hypotheses

The third hypothesis (H3) is associated with the performance effect of taking account of the

bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on

the following logit

(eq 5)

where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model

bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and

D1 = 1 when year = 2008 0 otherwise

D2 = 1 when year = 2009 0 otherwise

D3 = 1 when year = 2010 0 otherwise

)|1( XYp )( 441101

1

1

1XbXbbL ee

3

1

4

10

jjji

ii DcXbbL

30

The dummy variables do not directly refer to the bankruptcy year which that is not given in the

data basedatabase but rather to the last available year before bankruptcy For failed firms there

is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is

the base category If the AUC of this extended LR model statistically significantly exceeds the AUC

of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3

Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third

hypothesis above However for each hypothesis appropriate variables are used instead of the

year dummies Hypothesis H4 addresses the performance effect of taking size into account the

size and it will beis tested using by adding two additional variables measuring firm size in to the

LR -model In this LR model size is measured by the natural logarithm of total assets and its

squared form In this way the effect of logarithmic size can be reflected by in a function following

the second-order parabola Hypothesis H5 predicts that the classification performance improves

when the age of the firm is explicitly taken into account When testing this hypothesis the

category 6ndash14 years category is used as the base category and two dummy variables are

incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis

H6 looks at whether is associated with the classification performance is affected bythe effect of

the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt

is tested here using dummy variables for seven industries (D1 restaurants and hotels D2

construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and

water production D7 information technology) with all other industries acting as the base

category

31

Hypothesis H7 predicts that the explicit consideration of the country of origin will improves

classification performance This hypothesis is tested by using country risk measures instead ofnot

using dummy variables for countries but using country risk measures instead The country risk of

each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the

annual closing of accounts The rating is numerically recoded such that the best rating AAA

equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating

D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial

ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-

modelLR models with the original four financial ratios and the additional variables specified in

the hypotheses are estimated for all data In addition an LR model including all additional

variables is estimated for all data to assess the simultaneous effect of all variables Finally six of

the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not

included in this country- level testing hypothesis H7 is not tested because the additional variable

(country risk) is constant within the country

6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models

Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score

model for all data The variation in the ratios is significant as shown by the standard deviation

and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-

failed firms are close to each other indicating a symmetry of distributions However this is not

32

the case for the failed firms For the faileddistressed firms the median exceeds the mean for

these three ratios indicating negatively skewed distributions For X4 (BVETD) the means

significantly exceed the median for both failed and non-failed firms indicating a positively

skewed distribution For each of the four variables both the mean and the median are higher for

non-failed firms than for failed firms which is consistent with the expectations The difference

between the means of the non-failed and failed firms is larger in the original USUS data than in

our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is

approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of

EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may

indicate lower classification accuracy than in the original sample

(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates

(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the

large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model

Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same

statistical method or MDA The coefficients here are negative because the models are estimated

using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight

than in the original USUS data while the weights of WCTA and BVETD have proportionally

decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the

logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR

model These coefficients are directly comparable with those of the MDA model which isas

expected for the this exceptionally large sample The differences in the coefficients of the original

33

four variables among the eight LR-modelLR models (Models 1-8) are small For each model the

coefficient of BVETD is very close to zero This indicatinges that the original four variables and

the additional variables are quite independent of each other

(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-

modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate

that after 2007 (the base category) this risk of failure significantly decreased year by year The

base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due

to as a result of the global financial crisis The crisis played a significant role in the failure of key

businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The

effects were especially felt in Europe The coefficients of Model 4 for the size variables show that

the contribution of size on to the logit (risk measure) is atreaches its maximum value when

logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model

5 confirms the riskiness of young firms because the risk of failure is very high for newly founded

firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The

coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky

industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is

statistically significant (because of the large sample) but negative and very close to zero Finally

the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7

62 All data performance of the Zrsquorsquo-Score models

34

Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1

refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of

all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average

accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)

Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its

performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The

lower part of the table shows the AUCs for the countries included only in the test data The

performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)

samples It is also low and for the liquidation firms in the United Kingdom

The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5

presents the differences of these medians between the non-failed and failed firms by country

This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of

medians This correlation which is high for each financial ratio showing that the effects of the

ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large

except for EBITTA which This implies with the exceptionally high AUC that the ST firms

systematically differ from the non-ST firms although the differences are not extremely large The

differences between the medians are very large in Poland for each ratio justifying the high AUC

They are also large and in Finland and in the Czech Republic where the difference in EBITTA is

average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in

all four ratios are below the average a result thatwhich is obviously associated with a low AUC

35

In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even

negative In Iceland and Ireland the differences only in EBITTA are exceptionally small

(Table 4 here)

(Table 5 here)

Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by

MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)

supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is

at approximately the average level The re-estimation of the coefficients has led to improved

classification accuracy in a number of countries especially in Bosnia China (ST) Norway and

Greece However it has impaired the classification accuracy in the UK and China (delisted)

Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-

modelLR model) showing the effect of the estimation method For the benchmark model the

AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between

among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR

model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country

This result was expected because the coefficients of the models are directly comparable The

similar results for the models may also indicate that the independent variables conform to

multinormality This last latter result confirms what most researchers in the field of default

classification models have concluded that the accuracy levels of MDA and logistical regression

36

models are extremely similar despite any statistical violations such as normality between MDA

and logistical regression models

Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)

than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects

are not positive for all countries The effects are positive for example for Russia Estonia

Germany Ireland and Latvia but these effects are not statistically significant There are

statistically significant negative effects for China (ST) and Serbia There are notable negative

effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are

due to the exceptional annual distributions of failed firms in these countries For the countries

with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the

countries with positive effects it is only a few percent For each group non-failed firms are quite

equally distributed over years When the coefficient of D3 is very low (-0666) it strongly

decreases the risk estimates of most failed firms in the former countries but of only a few failed

firms in the latter countries This which leads to the observed effects H3 is supported by

the level of the whole sample However in some country samples (at the level of individual

levelies) H3 is not supported

Model 4 (LR-modelLR model with size variables) performs better than the benchmark model

which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads

and to significant improvements in the AUC for eg China (delisted) and Austria For China

(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC

37

also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs

in a few countries but thise decrease is not significant Model 4 is based on the four original

variables and the size effect following a second-order parabola This kind of size effect is very

small for micro firms but increases when approaching its maximum value in middle-sized firms

The countries with a positive change in AUC typically have data where in which the percent of

failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized

failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In

contrast the countries with a negative change in AUC have data where in which the percentage

of failed micro firms is exceptionally high (70-80)

Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly

the same AUC as the benchmark model (AUC = 0748) However the difference is positive and

statistically significant giving at least marginal support to H5 (age effect) For nearly all countries

the effect of the age on AUC is small For Austria however thise effect is however positive and

significant For Austria the percentage of non-failed young firms (less than 6 years) among non-

failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed

firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong

positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only

a few non-failed firms which This obviously leads to an improvement in AUC

Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC

supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its

38

AUC is notably higher than the benchmark AUC only in only a few countries such asfor example

France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg

restaurants and hotels and the information technology industryies but it has a positive effect

for on the construction and manufacturing industries For the countries with a positive effect on

AUC the percent of non-failed firms in restaurants and hotels and information technology is

high while that of failed firms is low For the risky industries (construction and manufacturing)

these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many

failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)

firms Therefore the AUC will be increasesd This situation is reversed for the countries with a

negative effect on AUC (Austria and Slovenia) The samples of these countries include a high

percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC

Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher

classification performance (AUC = 0749) than the benchmark model This result gives only very

weak support for H7 (country of origin effect) However for each country the resultant AUC is

nearly identical to that given by the benchmark model This result was expected due to the

negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7

This result implies that country risk hasdoes not have noany effect on the boundary between

bankrupt and non-bankrupt firms

Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional

variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to

39

the benchmark AUC in all test data However the effect on the AUCs largely varies and is either

negative or positive in different countries The positive effect is large in several countries such

as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative

effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show

that the inclusion of additional variables in the original model will usually increase the AUC but

not in every country

The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with

the USUS firms being unlike the majority of other firms in this study being listed or delisted

companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese

firm samples is associated with very small differences between the medians of the non-failed and

failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable

with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed

firms is very high Prior studies based on Chinese ST -firms have also demonstrated good

predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for

additional research and modeling work regarding unlisted and delisted Chinese firms

63 Country-level data performance of the Zrsquorsquo-Score models

The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform

all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the

different models estimated for each country separately (country-level models) In this table the

ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated

40

from the country data this benchmark will beis clearly outperformed by the resulting MDA

(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and

Sweden) However these results give only weak support for H1 at the country level because the

effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and

2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs

given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed

by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation

method)

Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC

than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3

leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United

StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in

nearly every country supporting H4 This improvement is significant however only in the United

StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark

model but for which it is only 0710 for the benchmark model It This improvement is also notable

in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age

category dummies) gives higher AUCs than the benchmark model for several countries but none

of the improvements are significant The positive effect is particularly strong especially in

Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only

weakly supports H5 because these negative effects are relatively small

41

Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects

on AUCs when compared with the benchmark However the only significant effect is the negative

effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the

industry dummies However there are notable positive effects on the AUC in for example

France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak

support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in

AUC compared with the benchmark model in Colombia Iceland and the Unites States It also

leads and to notable improvements in AUC at least in Belgium Denmark Estonia France

Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are

found only in Austria and Slovenia Thus as a conclusion we can concludestate that the

classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can

remarkably increases in most countries when different effects are taken into account by

additional variables This increase is found in most European countries and in Colombia and the

United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-

modelLR model and it can only be slightly improved only slightly by additional variables

(Table 6 here)

7 Summary of the study and a suggested extension

The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model

originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is

assessed using a very large international data set We test how the original version of the Zrsquorsquo-

42

Score model performs in different countries and how re-estimations using another statistical

method and different additional variables affect the classification performance when the data

are very heterogeneous For this kind of testing seven research hypotheses on classification

performance are extractedformulated These research hypotheses are tested for all data and

separately for country data (country-level analysis) The estimation data are from 31 countries

and the results are validated for 34 countries The countries are mainly from Europe but three

non-European countries are included (China Colombia and the United StatesUS) The statuses

used in the classification are mainly bankruptcyactive but receivership firms are also considered

to have failed In the Chinese data ST (special treatment) and delisted firms are also separately

analyzed as failed firms

The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very

satisfactorily in an international context The effects of the four financial ratios on performance

are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small

contribution in re-estimation The original model performs very well in several countries such as

Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only

marginally improved the classification performance thus weakly supporting the obsolescence

hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust

across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion

holds for the re-estimation of the model using LRA because the performance results are very

similar with to those of MDA (H2) The use of additional variables in the model generally

improvesd the classification accuracy of the original model but the results for countries are

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

16

countries such as Japan the UK Lithuania or the G-7 countries Our analysis is a significant

extension of the previous research

4 Methodology and research hypotheses

The literature survey shows that the Z-Score model (publicly traded firms) the Zrsquo-Score model

(private manufacturing firms) and the Zrsquorsquo-Score model (private and publicly

traded manufacturing and non-manufacturing firms) have been adapted for different purposes

In this study we are first interested first in assessing the performance of the original Zrsquorsquo-Score

model in classifying bankrupt and non-bankrupt firms in an international context with special

focus on the European market However we also validate the results in a set of non-European

countries to generalize the results for circumstances outside Europe Second we will re-estimate

the model using extensive international data and then use the re-estimated Zrsquorsquo-Score model as a

benchmark for assessing the effects of different factors on the modelrsquos performance in terms of

classification accuracy We will assess the effects of the following five factors on this

performance year of bankruptcy size of firms age of firms industry and country of origin WIn

all we will test a set of hypotheses based on the effects of the model on performance on two

different levels First we will test a set of hypotheses on a pooled set of all firms and second on

data from each country individually CIn compared withison to the previous research our this

studyrsquos contribution is the focus on an international context not just simply the model

application or re-estimation for of a given countryrsquos data Because weour aim is focusing on the

performance of the Zrsquorsquo-Score mModel when using ain large body of international data the

research hypotheses are of a technical nature and are presented given as follows

17

H1 Obsolescence of the coefficients The Zrsquorsquo-Score mModel was originally estimated using the

same sample of firms that was used to develop the original Z-Score mModel The bankruptcies

in the estimation data occurred during the period 1946ndash-1965 period Thus the oldest

observations are from nearly seventy years ago during the post-war period Altman (1983)

recommended utilizing data as near to the present as possible when developing a bankruptcy

prediction model It is obvious that firmsrsquo the financial behavior of firms and their business

environment have significantly changed since then Thereforen potentially making the

importance of the financial ratios as reflected by the coefficients of the model may differ from

their original importance reflected by the coefficients of the model Therefore we suggest as

the first hypothesis (H1) that the re-estimation of the coefficients of the four original variables

of the Zrsquorsquo-Score model will improve the classification performance of the model in an

international context This hypothesis is supported by the previous research (eg Grice and

Ingram 2001) and by practice H1 aims to support this evidence on the international level and is

expressed as follows

H1 RThe re-estimatingon of the coefficients of the Zrsquorsquo-Score model improves the its

classification accuracy of the model

H2 Method of estimation The original Zrsquorsquo-Score model has been estimated using MDA However

MDA is based on the ordinary least squares (OLS) method and thus requires assumptions of

multinormality homoscedasticity and linearity which are not often met in empirical financial

ratio analysis Therefore we re-estimate the Zrsquorsquo-Score model using logistic regression analysis

(LRA) to assess the effect of the method of estimation method LRA does not require most of the

18

restricting assumptions of MDA In LRA the multivariate normality of the independent variables

is not required nor are homoscedasticity and linearity For the sake of OLS MDA can be more

useful than LRA for small samples such as the original sample of 66 firms used in the estimation

of the Zrsquorsquo-Score mModel However in a large sample LRA may potentially perform better In this

study we use large samples which is advantageous for LRA Therefore our second hypothesis

(H2) suggests is that the classification performance of the re-estimated Zrsquorsquo-Score model will be

improved when it is estimated using LRA instead of MDA The model re-estimated for the original

variables using LRA and all pooled data is called here the Zrsquorsquo-Score LR-modelLR model The

performance of this re-estimated model is used in this study as the benchmark for further

analyses Thus the second hypothesis is as follows

H2 The prediction accuracy of the logistic regression version of the Zrsquorsquo-Score model is higher

than that of the multiple discriminant analysis version

H3 Bankruptcy year The model based on the relationship between bankruptcy and financial

ratios is likely to be affected by the macroeconomic environment These effects may significantly

decrease the classification accuracy of the model If the model is estimated using data from one

year and it will be applied to data from another year the validity of the model can be questioned

Business cycles inIn terms of economic growth credit policy and interest rates business cycles

can ahave an effect on the boundary between bankrupt and non-bankrupt firms The original Zrsquorsquo-

Score model is estimated using data from the period 1946ndash1965 period which includes several

business cycles Therefore the model is not focused on any specific stage of a cycle and does not

explicitly take into account of the bankruptcy year Altman (1983) suggested gathering data from

19

firms for the last most recent few years when developing a prediction model In this study the

benchmark Zrsquorsquo-Score LR -model will beis estimated for a shorter period than in the original

estimation although it covers several recent years on for different stages of the business cycle in

different countries Therefore the third hypothesis (H3) assumes is that the classification

accuracy of the benchmark model can be increased by explicitly taking into account of the year

of bankruptcy in the estimation of the model estimation The third hypothesis is presented as

follows

H3 The prediction accuracy of the modelrsquos prediction accuracy is higher whenith the

effect of the year of bankruptcy is included is higher than that of the model without the

effect

H4 Size of the firm The boundary between bankrupt and non-bankrupt firms is different for small

and larger firms which decreases the performance of the model estimation when estimated for

data from one size category and isare applied to for data from another size category For the

bankrupt and non-bankrupt firms in the original data for Zrsquorsquo-Score model estimation the range

of asset sizes was ranged between approximately 1 and 25 million US dollars The data did not

include very small or very large firms Altman (1983) regarded the suitability of the original Z-

Score model (and likewisein the same way the Zrsquorsquo-Score model) for all firms as debatable

because of this omission it did not scrutinize very large or very small firms In the currentis study

the benchmark Zrsquorsquo-Score LR -model will beis estimated for data from many size categories from

very small firms to very large firms The fourth hypothesis (H4) assumes that the classification

performance of the uniform benchmark LR -model based on the original four financial variables

20

of the Zrsquorsquo-Score model is improved when the size category of the firm is explicitly taken into

account Thus we present the fourth hypothesis as follows

H4 The modelrsquos prediction accuracy of the modelis higher whenith the effect of size is

includede is higher than that of the model without this effect

H5 Age of the firm International insolvency statistics generally show that bankruptcy risk is a

function of the age of the firm Very young firms typically show a very high risk The original Zrsquorsquo-

Score model does not explicitly take age into account age However Altman (1983) noted that

the age of a firm is implicitly considered in the Retained EarningsTotal Assets ratio (X2) which

was regarded as a new ratio in the bankruptcy prediction context A relatively young firm will

probably show a low ratio because it has not had time to build up cumulative profits Thus a

young firm is to some degree discriminated against in the model and its likelihood of being

classified as bankrupt is relatively higher than that of an older firm The incidence of failure is

much higher in the early years of a firm AlthoughDespite that the age of the firm in this way is

implicitly taken into account in X2 we expect that an explicit consideration of the age will improve

the classification accuracy bydue to controlling for the age factor Therefore the fifth hypothesis

(H5) proposes that the performance of the uniform benchmark model based on the original four

financial variables of the Zrsquorsquo-Score model is increasesd when the age of the firm is explicitly taken

into account The fifth hypothesis is can be presented as follows

H5 The modelrsquos prediction accuracy of the model is higher when with the effect of firm

age is higher than that of the model without this effectincluded

21

H6 Industry of the firm The original Zrsquo-Score model was estimated only for manufacturing firms

Altman (1983) stated that it would be ideal to develop a bankruptcy prediction model utilizing a

homogenous group of bankrupt firms If we are interested in a particular industry grouping we

should gather data from bankrupt and non-bankrupt firms in these groupings Previous studies

show that financial distress analysis is influenced by the industry effect (Smith and Liou 2007)

Firms in different industries tend to report different levels of the same financial ratios which may

have an effect on the boundary between bankrupt and non-bankrupt firms This industry effect

may be present in the Zrsquo-Score model especially due to the SalesTotal Assets ratio (X5) This ratio

which showed the least significance on a univariate basis while making but it had made a very

significant contribution to the discriminant power of the multivariate model Altman (1983)

recognized the potential industry effect due to a the wide variation among industries in asset

turnover and specified the Zrsquorsquo-Score model without X5 However also the Zrsquorsquo-Score model was

also estimated using the original sample of manufacturing firms In our analysis the uniform

benchmark model based on the original four financial variables of the Zrsquorsquo-Score model is

estimated for a statistical sample representing different industries Therefore the sixth

hypothesis (H6) assumes that an explicit consideration of industry will improve the classification

accuracy of this benchmark model H6It can be expressed in the following form

H6 The modelrsquos prediction accuracy of the modelis higher whenith the effect of industry is

higher than that of the model without this effectincluded

H7 Country of origin The original Zrsquorsquo-Score model has been estimated only for USUS firms It

can be expected that the international applicability of the model to other countries is affected by

22

country-specific differences The eEconomic environment legislation culture financial markets

and accounting practices in a country may affect the financial behavior of firms and the boundary

between bankrupt and non-bankrupt firms These factors may potentially weaken the

classification performance of the model in for other countries outside other than that e one fore

country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the

seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm

into account will improve the classification accuracy of the benchmark model In our empirical

study the country effect will beis assessed by including a variable of for country risk Therefore

the seventh hypothesis is presented as follows

H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country

risk is higher than that of the model without this effectincluded

5 Empirical data and statistical methods

51 Sample of firms

The principal data of for this study are were extracted from the ORBIS databases of Bureau Van

Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the

moment of sampling contained administrative information on more than 50 million European

firms However income statement and balance sheet information was available for

approximately 8 million companies More than 99 of the companies covered in this database

are private companies from different various industries justifying the use of the Zrsquorsquo-Score model

instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all

industrial groupings and for both private and public entities (Altman 1983 124 Altman and

Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope

23

of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all

industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly

used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that

international comparability may be a problem when administrative firm-level data are

internationally pooled For administrative data Although the definition of variables is usually less

harmonized for administrative data tThis is less of a problem in the ORBIS database because of

the common international format of balance sheets For example although some discrepancies

in profitloss statements may arise because of differences in fiscal systems across countries

balance sheet variables largely adhere to international standards

AThere are a number of factors that influence the international applicability of bankruptcy

prediction models These factors include accounting legislation and practice creditor rights and

investor protection judicial efficiency corporate governance bankruptcy protection and

insolvency management and firm risk-taking These factors strongly differ between European

and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-

Score model outside Europe First it is especiallyparticularly important to include the USUS as

because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It

is include d in our study as well because it has the largest market capitalization in the world

Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it

applicable to emerging market companies Therefore we include firms from China and Colombia

which represent two very culturally and institutionally very dissimilar emerging market

countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt

24

firms ie the limit we set for European countries) from ORBIS World were not

available Thus the results are also estimated and tested for three non-European countries (the

United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-

Score modelrsquos performance The samples of firms from these countries are were extracted from

ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms

from around the world

SFor statistical sampling several requirements are set for the statistical sampling of the empirical

data First we require that the firm to be selected be an industrial (non-financial) company

Second its owners must have limited liability (so partnerships and sole proprietors are left out

of the study) Third we set a minimum requirement for the size of the firm Because the financial

ratios of very small firms are generally too unstable for a failure prediction model these firms

are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have

exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we

include in our estimation sample firms from all European countries and 3 pre-selected non-

European countries where the number of failed firms is more greater than 60 If the number of

failed firms for any European country is less than 60 the firms from this country are only included

only in the test sample For qualifying European countries the firms are randomly classified in

the estimation and test samples so that the number of firms is approximately equal in both

samples Thus our estimation data include firms from 28 European and 3 non-European

countries Fifth all failed firms that fulfill the above requirements are included in our samples

However if the number of non-failed firms in a country is very high a sample is randomly

25

selected from that country Finally the time span of fiscal years potentially available for this study

ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in

the database are from a financial period within 2007 and 2010 earlier years are also excluded

for non-failed firms for comparability All qualifying observations of non-failed firms from the

years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses

of failed firms to the last most recent financial statements available before failure The four

independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize

outliers

Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test

data by country The estimation sample includes data from 2602563 non-failed and 38215

failed firms from 28 European and 3 non-European countries The test sample is slightly larger

because it includes data from 31 European and 3 non-European countries From For the country

of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt

firms The available US data that were available consist only of listed (and delisted) firms From

China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are

included in the estimation sample although there are only 32 such firms altogether7 The Chinese

datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status

are separately analyzed separately only in for the test data8 ST firms are listed firms suffering

6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor

26

from serious financial difficulties Excluding the special US data and the two non-private Chinese

datasets 994 of the observations in the data are private firms

(Table 1 here)

52 Status of failed firms

ORBIS has five classes for potentially active firms (active default of payment receivership

dormant and branch) and seven classes for inactive firms that no longer carry out business

activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation

branch and no precision) From Among these classes only active is selected to represent non-

distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by

considering (with exceptions described below) a firm failed if its status in ORBIS is stated as

bankruptcy However because of the small number of bankrupt firms in some countries we also

consider receivership (active) firms as failed even if they are active These firms generally suffer

from serious financial distress However firms in liquidation are generally not included in the

sample of failed firms Firms in liquidation may depending on the country contain firms that

have ceased activities due to reasons other than failure (mergers discontinuing the operations

of a daughter company or of a foreign branch etc) Therefore for most countries we select only

firms that are coded as being bankrupt or under receivership However there are a number of

special cases where failed firms are coded under a different status heading These countries or

samples are the following

Country Status categories Bulgaria In liquidation Bankruptcy

27

Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the

study (for example Switzerland) If a country had only a very small number of failed firms it was

dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries

including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status

classes (including the bankruptcy category) are not completely homogenous within European

countries due to different legislations although there are obvious similarities in insolvency acts

(Philippe et al 2002) China is a special case because it includes samples with three different

criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are

two different samples (liquidation and receivership)

53 Statistical methods

In this study seven research hypotheses are drawn for statistical testing The statistical analysis

will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The

classification performance of the original model is assessed by the AUC (Area Under Curve)

measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close

connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random

28

model and 05 for a model with an average classification performance In SAS software is used

for all statistical analysess SAS software is used

The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is

tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical

method (multiple discriminant analysis or MDA) The problem is that the estimation sample

includes different numbers of failed and non-failed firms from 31 countries In the original Z-

Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from

the USUS Following the characteristics of these data we weight the firms so that the weights

for the failed and non-failed firms are equally In so doingthis way the non-proportional

sampling in from different countries does will not affect the re-estimated model The number of

firms from different countries however varies significantly leading to greater weights for larger

countries To avoid this problem the observations are also weighted so that each country has an

equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using

these weighted data and the resulting AUC is compared with that based on the original model

The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score

model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is

based on less- restrictive statistical assumptions than MDA In this estimation the dependent

variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA

does not require that independent variables be multivariate normal or that groups have equal

covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA

29

creates a score (logit) score L for every firm It is assumed that the independent variables are

linearly related to L This score or logit is used to determine the conditional probability of failure

as follows

= (eq 4)

where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the

original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing

the statistical significance of the difference between AUCs for this LR model and for the re-

estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it

is used as a benchmark for further statistical AUC comparisons because LR is applied as the

principal method in the testing of the remaining research hypotheses

The third hypothesis (H3) is associated with the performance effect of taking account of the

bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on

the following logit

(eq 5)

where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model

bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and

D1 = 1 when year = 2008 0 otherwise

D2 = 1 when year = 2009 0 otherwise

D3 = 1 when year = 2010 0 otherwise

)|1( XYp )( 441101

1

1

1XbXbbL ee

3

1

4

10

jjji

ii DcXbbL

30

The dummy variables do not directly refer to the bankruptcy year which that is not given in the

data basedatabase but rather to the last available year before bankruptcy For failed firms there

is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is

the base category If the AUC of this extended LR model statistically significantly exceeds the AUC

of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3

Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third

hypothesis above However for each hypothesis appropriate variables are used instead of the

year dummies Hypothesis H4 addresses the performance effect of taking size into account the

size and it will beis tested using by adding two additional variables measuring firm size in to the

LR -model In this LR model size is measured by the natural logarithm of total assets and its

squared form In this way the effect of logarithmic size can be reflected by in a function following

the second-order parabola Hypothesis H5 predicts that the classification performance improves

when the age of the firm is explicitly taken into account When testing this hypothesis the

category 6ndash14 years category is used as the base category and two dummy variables are

incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis

H6 looks at whether is associated with the classification performance is affected bythe effect of

the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt

is tested here using dummy variables for seven industries (D1 restaurants and hotels D2

construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and

water production D7 information technology) with all other industries acting as the base

category

31

Hypothesis H7 predicts that the explicit consideration of the country of origin will improves

classification performance This hypothesis is tested by using country risk measures instead ofnot

using dummy variables for countries but using country risk measures instead The country risk of

each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the

annual closing of accounts The rating is numerically recoded such that the best rating AAA

equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating

D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial

ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-

modelLR models with the original four financial ratios and the additional variables specified in

the hypotheses are estimated for all data In addition an LR model including all additional

variables is estimated for all data to assess the simultaneous effect of all variables Finally six of

the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not

included in this country- level testing hypothesis H7 is not tested because the additional variable

(country risk) is constant within the country

6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models

Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score

model for all data The variation in the ratios is significant as shown by the standard deviation

and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-

failed firms are close to each other indicating a symmetry of distributions However this is not

32

the case for the failed firms For the faileddistressed firms the median exceeds the mean for

these three ratios indicating negatively skewed distributions For X4 (BVETD) the means

significantly exceed the median for both failed and non-failed firms indicating a positively

skewed distribution For each of the four variables both the mean and the median are higher for

non-failed firms than for failed firms which is consistent with the expectations The difference

between the means of the non-failed and failed firms is larger in the original USUS data than in

our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is

approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of

EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may

indicate lower classification accuracy than in the original sample

(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates

(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the

large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model

Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same

statistical method or MDA The coefficients here are negative because the models are estimated

using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight

than in the original USUS data while the weights of WCTA and BVETD have proportionally

decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the

logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR

model These coefficients are directly comparable with those of the MDA model which isas

expected for the this exceptionally large sample The differences in the coefficients of the original

33

four variables among the eight LR-modelLR models (Models 1-8) are small For each model the

coefficient of BVETD is very close to zero This indicatinges that the original four variables and

the additional variables are quite independent of each other

(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-

modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate

that after 2007 (the base category) this risk of failure significantly decreased year by year The

base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due

to as a result of the global financial crisis The crisis played a significant role in the failure of key

businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The

effects were especially felt in Europe The coefficients of Model 4 for the size variables show that

the contribution of size on to the logit (risk measure) is atreaches its maximum value when

logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model

5 confirms the riskiness of young firms because the risk of failure is very high for newly founded

firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The

coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky

industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is

statistically significant (because of the large sample) but negative and very close to zero Finally

the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7

62 All data performance of the Zrsquorsquo-Score models

34

Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1

refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of

all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average

accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)

Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its

performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The

lower part of the table shows the AUCs for the countries included only in the test data The

performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)

samples It is also low and for the liquidation firms in the United Kingdom

The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5

presents the differences of these medians between the non-failed and failed firms by country

This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of

medians This correlation which is high for each financial ratio showing that the effects of the

ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large

except for EBITTA which This implies with the exceptionally high AUC that the ST firms

systematically differ from the non-ST firms although the differences are not extremely large The

differences between the medians are very large in Poland for each ratio justifying the high AUC

They are also large and in Finland and in the Czech Republic where the difference in EBITTA is

average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in

all four ratios are below the average a result thatwhich is obviously associated with a low AUC

35

In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even

negative In Iceland and Ireland the differences only in EBITTA are exceptionally small

(Table 4 here)

(Table 5 here)

Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by

MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)

supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is

at approximately the average level The re-estimation of the coefficients has led to improved

classification accuracy in a number of countries especially in Bosnia China (ST) Norway and

Greece However it has impaired the classification accuracy in the UK and China (delisted)

Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-

modelLR model) showing the effect of the estimation method For the benchmark model the

AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between

among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR

model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country

This result was expected because the coefficients of the models are directly comparable The

similar results for the models may also indicate that the independent variables conform to

multinormality This last latter result confirms what most researchers in the field of default

classification models have concluded that the accuracy levels of MDA and logistical regression

36

models are extremely similar despite any statistical violations such as normality between MDA

and logistical regression models

Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)

than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects

are not positive for all countries The effects are positive for example for Russia Estonia

Germany Ireland and Latvia but these effects are not statistically significant There are

statistically significant negative effects for China (ST) and Serbia There are notable negative

effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are

due to the exceptional annual distributions of failed firms in these countries For the countries

with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the

countries with positive effects it is only a few percent For each group non-failed firms are quite

equally distributed over years When the coefficient of D3 is very low (-0666) it strongly

decreases the risk estimates of most failed firms in the former countries but of only a few failed

firms in the latter countries This which leads to the observed effects H3 is supported by

the level of the whole sample However in some country samples (at the level of individual

levelies) H3 is not supported

Model 4 (LR-modelLR model with size variables) performs better than the benchmark model

which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads

and to significant improvements in the AUC for eg China (delisted) and Austria For China

(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC

37

also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs

in a few countries but thise decrease is not significant Model 4 is based on the four original

variables and the size effect following a second-order parabola This kind of size effect is very

small for micro firms but increases when approaching its maximum value in middle-sized firms

The countries with a positive change in AUC typically have data where in which the percent of

failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized

failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In

contrast the countries with a negative change in AUC have data where in which the percentage

of failed micro firms is exceptionally high (70-80)

Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly

the same AUC as the benchmark model (AUC = 0748) However the difference is positive and

statistically significant giving at least marginal support to H5 (age effect) For nearly all countries

the effect of the age on AUC is small For Austria however thise effect is however positive and

significant For Austria the percentage of non-failed young firms (less than 6 years) among non-

failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed

firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong

positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only

a few non-failed firms which This obviously leads to an improvement in AUC

Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC

supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its

38

AUC is notably higher than the benchmark AUC only in only a few countries such asfor example

France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg

restaurants and hotels and the information technology industryies but it has a positive effect

for on the construction and manufacturing industries For the countries with a positive effect on

AUC the percent of non-failed firms in restaurants and hotels and information technology is

high while that of failed firms is low For the risky industries (construction and manufacturing)

these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many

failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)

firms Therefore the AUC will be increasesd This situation is reversed for the countries with a

negative effect on AUC (Austria and Slovenia) The samples of these countries include a high

percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC

Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher

classification performance (AUC = 0749) than the benchmark model This result gives only very

weak support for H7 (country of origin effect) However for each country the resultant AUC is

nearly identical to that given by the benchmark model This result was expected due to the

negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7

This result implies that country risk hasdoes not have noany effect on the boundary between

bankrupt and non-bankrupt firms

Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional

variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to

39

the benchmark AUC in all test data However the effect on the AUCs largely varies and is either

negative or positive in different countries The positive effect is large in several countries such

as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative

effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show

that the inclusion of additional variables in the original model will usually increase the AUC but

not in every country

The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with

the USUS firms being unlike the majority of other firms in this study being listed or delisted

companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese

firm samples is associated with very small differences between the medians of the non-failed and

failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable

with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed

firms is very high Prior studies based on Chinese ST -firms have also demonstrated good

predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for

additional research and modeling work regarding unlisted and delisted Chinese firms

63 Country-level data performance of the Zrsquorsquo-Score models

The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform

all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the

different models estimated for each country separately (country-level models) In this table the

ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated

40

from the country data this benchmark will beis clearly outperformed by the resulting MDA

(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and

Sweden) However these results give only weak support for H1 at the country level because the

effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and

2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs

given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed

by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation

method)

Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC

than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3

leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United

StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in

nearly every country supporting H4 This improvement is significant however only in the United

StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark

model but for which it is only 0710 for the benchmark model It This improvement is also notable

in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age

category dummies) gives higher AUCs than the benchmark model for several countries but none

of the improvements are significant The positive effect is particularly strong especially in

Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only

weakly supports H5 because these negative effects are relatively small

41

Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects

on AUCs when compared with the benchmark However the only significant effect is the negative

effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the

industry dummies However there are notable positive effects on the AUC in for example

France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak

support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in

AUC compared with the benchmark model in Colombia Iceland and the Unites States It also

leads and to notable improvements in AUC at least in Belgium Denmark Estonia France

Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are

found only in Austria and Slovenia Thus as a conclusion we can concludestate that the

classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can

remarkably increases in most countries when different effects are taken into account by

additional variables This increase is found in most European countries and in Colombia and the

United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-

modelLR model and it can only be slightly improved only slightly by additional variables

(Table 6 here)

7 Summary of the study and a suggested extension

The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model

originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is

assessed using a very large international data set We test how the original version of the Zrsquorsquo-

42

Score model performs in different countries and how re-estimations using another statistical

method and different additional variables affect the classification performance when the data

are very heterogeneous For this kind of testing seven research hypotheses on classification

performance are extractedformulated These research hypotheses are tested for all data and

separately for country data (country-level analysis) The estimation data are from 31 countries

and the results are validated for 34 countries The countries are mainly from Europe but three

non-European countries are included (China Colombia and the United StatesUS) The statuses

used in the classification are mainly bankruptcyactive but receivership firms are also considered

to have failed In the Chinese data ST (special treatment) and delisted firms are also separately

analyzed as failed firms

The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very

satisfactorily in an international context The effects of the four financial ratios on performance

are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small

contribution in re-estimation The original model performs very well in several countries such as

Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only

marginally improved the classification performance thus weakly supporting the obsolescence

hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust

across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion

holds for the re-estimation of the model using LRA because the performance results are very

similar with to those of MDA (H2) The use of additional variables in the model generally

improvesd the classification accuracy of the original model but the results for countries are

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

17

H1 Obsolescence of the coefficients The Zrsquorsquo-Score mModel was originally estimated using the

same sample of firms that was used to develop the original Z-Score mModel The bankruptcies

in the estimation data occurred during the period 1946ndash-1965 period Thus the oldest

observations are from nearly seventy years ago during the post-war period Altman (1983)

recommended utilizing data as near to the present as possible when developing a bankruptcy

prediction model It is obvious that firmsrsquo the financial behavior of firms and their business

environment have significantly changed since then Thereforen potentially making the

importance of the financial ratios as reflected by the coefficients of the model may differ from

their original importance reflected by the coefficients of the model Therefore we suggest as

the first hypothesis (H1) that the re-estimation of the coefficients of the four original variables

of the Zrsquorsquo-Score model will improve the classification performance of the model in an

international context This hypothesis is supported by the previous research (eg Grice and

Ingram 2001) and by practice H1 aims to support this evidence on the international level and is

expressed as follows

H1 RThe re-estimatingon of the coefficients of the Zrsquorsquo-Score model improves the its

classification accuracy of the model

H2 Method of estimation The original Zrsquorsquo-Score model has been estimated using MDA However

MDA is based on the ordinary least squares (OLS) method and thus requires assumptions of

multinormality homoscedasticity and linearity which are not often met in empirical financial

ratio analysis Therefore we re-estimate the Zrsquorsquo-Score model using logistic regression analysis

(LRA) to assess the effect of the method of estimation method LRA does not require most of the

18

restricting assumptions of MDA In LRA the multivariate normality of the independent variables

is not required nor are homoscedasticity and linearity For the sake of OLS MDA can be more

useful than LRA for small samples such as the original sample of 66 firms used in the estimation

of the Zrsquorsquo-Score mModel However in a large sample LRA may potentially perform better In this

study we use large samples which is advantageous for LRA Therefore our second hypothesis

(H2) suggests is that the classification performance of the re-estimated Zrsquorsquo-Score model will be

improved when it is estimated using LRA instead of MDA The model re-estimated for the original

variables using LRA and all pooled data is called here the Zrsquorsquo-Score LR-modelLR model The

performance of this re-estimated model is used in this study as the benchmark for further

analyses Thus the second hypothesis is as follows

H2 The prediction accuracy of the logistic regression version of the Zrsquorsquo-Score model is higher

than that of the multiple discriminant analysis version

H3 Bankruptcy year The model based on the relationship between bankruptcy and financial

ratios is likely to be affected by the macroeconomic environment These effects may significantly

decrease the classification accuracy of the model If the model is estimated using data from one

year and it will be applied to data from another year the validity of the model can be questioned

Business cycles inIn terms of economic growth credit policy and interest rates business cycles

can ahave an effect on the boundary between bankrupt and non-bankrupt firms The original Zrsquorsquo-

Score model is estimated using data from the period 1946ndash1965 period which includes several

business cycles Therefore the model is not focused on any specific stage of a cycle and does not

explicitly take into account of the bankruptcy year Altman (1983) suggested gathering data from

19

firms for the last most recent few years when developing a prediction model In this study the

benchmark Zrsquorsquo-Score LR -model will beis estimated for a shorter period than in the original

estimation although it covers several recent years on for different stages of the business cycle in

different countries Therefore the third hypothesis (H3) assumes is that the classification

accuracy of the benchmark model can be increased by explicitly taking into account of the year

of bankruptcy in the estimation of the model estimation The third hypothesis is presented as

follows

H3 The prediction accuracy of the modelrsquos prediction accuracy is higher whenith the

effect of the year of bankruptcy is included is higher than that of the model without the

effect

H4 Size of the firm The boundary between bankrupt and non-bankrupt firms is different for small

and larger firms which decreases the performance of the model estimation when estimated for

data from one size category and isare applied to for data from another size category For the

bankrupt and non-bankrupt firms in the original data for Zrsquorsquo-Score model estimation the range

of asset sizes was ranged between approximately 1 and 25 million US dollars The data did not

include very small or very large firms Altman (1983) regarded the suitability of the original Z-

Score model (and likewisein the same way the Zrsquorsquo-Score model) for all firms as debatable

because of this omission it did not scrutinize very large or very small firms In the currentis study

the benchmark Zrsquorsquo-Score LR -model will beis estimated for data from many size categories from

very small firms to very large firms The fourth hypothesis (H4) assumes that the classification

performance of the uniform benchmark LR -model based on the original four financial variables

20

of the Zrsquorsquo-Score model is improved when the size category of the firm is explicitly taken into

account Thus we present the fourth hypothesis as follows

H4 The modelrsquos prediction accuracy of the modelis higher whenith the effect of size is

includede is higher than that of the model without this effect

H5 Age of the firm International insolvency statistics generally show that bankruptcy risk is a

function of the age of the firm Very young firms typically show a very high risk The original Zrsquorsquo-

Score model does not explicitly take age into account age However Altman (1983) noted that

the age of a firm is implicitly considered in the Retained EarningsTotal Assets ratio (X2) which

was regarded as a new ratio in the bankruptcy prediction context A relatively young firm will

probably show a low ratio because it has not had time to build up cumulative profits Thus a

young firm is to some degree discriminated against in the model and its likelihood of being

classified as bankrupt is relatively higher than that of an older firm The incidence of failure is

much higher in the early years of a firm AlthoughDespite that the age of the firm in this way is

implicitly taken into account in X2 we expect that an explicit consideration of the age will improve

the classification accuracy bydue to controlling for the age factor Therefore the fifth hypothesis

(H5) proposes that the performance of the uniform benchmark model based on the original four

financial variables of the Zrsquorsquo-Score model is increasesd when the age of the firm is explicitly taken

into account The fifth hypothesis is can be presented as follows

H5 The modelrsquos prediction accuracy of the model is higher when with the effect of firm

age is higher than that of the model without this effectincluded

21

H6 Industry of the firm The original Zrsquo-Score model was estimated only for manufacturing firms

Altman (1983) stated that it would be ideal to develop a bankruptcy prediction model utilizing a

homogenous group of bankrupt firms If we are interested in a particular industry grouping we

should gather data from bankrupt and non-bankrupt firms in these groupings Previous studies

show that financial distress analysis is influenced by the industry effect (Smith and Liou 2007)

Firms in different industries tend to report different levels of the same financial ratios which may

have an effect on the boundary between bankrupt and non-bankrupt firms This industry effect

may be present in the Zrsquo-Score model especially due to the SalesTotal Assets ratio (X5) This ratio

which showed the least significance on a univariate basis while making but it had made a very

significant contribution to the discriminant power of the multivariate model Altman (1983)

recognized the potential industry effect due to a the wide variation among industries in asset

turnover and specified the Zrsquorsquo-Score model without X5 However also the Zrsquorsquo-Score model was

also estimated using the original sample of manufacturing firms In our analysis the uniform

benchmark model based on the original four financial variables of the Zrsquorsquo-Score model is

estimated for a statistical sample representing different industries Therefore the sixth

hypothesis (H6) assumes that an explicit consideration of industry will improve the classification

accuracy of this benchmark model H6It can be expressed in the following form

H6 The modelrsquos prediction accuracy of the modelis higher whenith the effect of industry is

higher than that of the model without this effectincluded

H7 Country of origin The original Zrsquorsquo-Score model has been estimated only for USUS firms It

can be expected that the international applicability of the model to other countries is affected by

22

country-specific differences The eEconomic environment legislation culture financial markets

and accounting practices in a country may affect the financial behavior of firms and the boundary

between bankrupt and non-bankrupt firms These factors may potentially weaken the

classification performance of the model in for other countries outside other than that e one fore

country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the

seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm

into account will improve the classification accuracy of the benchmark model In our empirical

study the country effect will beis assessed by including a variable of for country risk Therefore

the seventh hypothesis is presented as follows

H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country

risk is higher than that of the model without this effectincluded

5 Empirical data and statistical methods

51 Sample of firms

The principal data of for this study are were extracted from the ORBIS databases of Bureau Van

Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the

moment of sampling contained administrative information on more than 50 million European

firms However income statement and balance sheet information was available for

approximately 8 million companies More than 99 of the companies covered in this database

are private companies from different various industries justifying the use of the Zrsquorsquo-Score model

instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all

industrial groupings and for both private and public entities (Altman 1983 124 Altman and

Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope

23

of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all

industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly

used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that

international comparability may be a problem when administrative firm-level data are

internationally pooled For administrative data Although the definition of variables is usually less

harmonized for administrative data tThis is less of a problem in the ORBIS database because of

the common international format of balance sheets For example although some discrepancies

in profitloss statements may arise because of differences in fiscal systems across countries

balance sheet variables largely adhere to international standards

AThere are a number of factors that influence the international applicability of bankruptcy

prediction models These factors include accounting legislation and practice creditor rights and

investor protection judicial efficiency corporate governance bankruptcy protection and

insolvency management and firm risk-taking These factors strongly differ between European

and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-

Score model outside Europe First it is especiallyparticularly important to include the USUS as

because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It

is include d in our study as well because it has the largest market capitalization in the world

Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it

applicable to emerging market companies Therefore we include firms from China and Colombia

which represent two very culturally and institutionally very dissimilar emerging market

countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt

24

firms ie the limit we set for European countries) from ORBIS World were not

available Thus the results are also estimated and tested for three non-European countries (the

United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-

Score modelrsquos performance The samples of firms from these countries are were extracted from

ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms

from around the world

SFor statistical sampling several requirements are set for the statistical sampling of the empirical

data First we require that the firm to be selected be an industrial (non-financial) company

Second its owners must have limited liability (so partnerships and sole proprietors are left out

of the study) Third we set a minimum requirement for the size of the firm Because the financial

ratios of very small firms are generally too unstable for a failure prediction model these firms

are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have

exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we

include in our estimation sample firms from all European countries and 3 pre-selected non-

European countries where the number of failed firms is more greater than 60 If the number of

failed firms for any European country is less than 60 the firms from this country are only included

only in the test sample For qualifying European countries the firms are randomly classified in

the estimation and test samples so that the number of firms is approximately equal in both

samples Thus our estimation data include firms from 28 European and 3 non-European

countries Fifth all failed firms that fulfill the above requirements are included in our samples

However if the number of non-failed firms in a country is very high a sample is randomly

25

selected from that country Finally the time span of fiscal years potentially available for this study

ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in

the database are from a financial period within 2007 and 2010 earlier years are also excluded

for non-failed firms for comparability All qualifying observations of non-failed firms from the

years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses

of failed firms to the last most recent financial statements available before failure The four

independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize

outliers

Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test

data by country The estimation sample includes data from 2602563 non-failed and 38215

failed firms from 28 European and 3 non-European countries The test sample is slightly larger

because it includes data from 31 European and 3 non-European countries From For the country

of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt

firms The available US data that were available consist only of listed (and delisted) firms From

China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are

included in the estimation sample although there are only 32 such firms altogether7 The Chinese

datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status

are separately analyzed separately only in for the test data8 ST firms are listed firms suffering

6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor

26

from serious financial difficulties Excluding the special US data and the two non-private Chinese

datasets 994 of the observations in the data are private firms

(Table 1 here)

52 Status of failed firms

ORBIS has five classes for potentially active firms (active default of payment receivership

dormant and branch) and seven classes for inactive firms that no longer carry out business

activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation

branch and no precision) From Among these classes only active is selected to represent non-

distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by

considering (with exceptions described below) a firm failed if its status in ORBIS is stated as

bankruptcy However because of the small number of bankrupt firms in some countries we also

consider receivership (active) firms as failed even if they are active These firms generally suffer

from serious financial distress However firms in liquidation are generally not included in the

sample of failed firms Firms in liquidation may depending on the country contain firms that

have ceased activities due to reasons other than failure (mergers discontinuing the operations

of a daughter company or of a foreign branch etc) Therefore for most countries we select only

firms that are coded as being bankrupt or under receivership However there are a number of

special cases where failed firms are coded under a different status heading These countries or

samples are the following

Country Status categories Bulgaria In liquidation Bankruptcy

27

Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the

study (for example Switzerland) If a country had only a very small number of failed firms it was

dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries

including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status

classes (including the bankruptcy category) are not completely homogenous within European

countries due to different legislations although there are obvious similarities in insolvency acts

(Philippe et al 2002) China is a special case because it includes samples with three different

criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are

two different samples (liquidation and receivership)

53 Statistical methods

In this study seven research hypotheses are drawn for statistical testing The statistical analysis

will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The

classification performance of the original model is assessed by the AUC (Area Under Curve)

measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close

connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random

28

model and 05 for a model with an average classification performance In SAS software is used

for all statistical analysess SAS software is used

The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is

tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical

method (multiple discriminant analysis or MDA) The problem is that the estimation sample

includes different numbers of failed and non-failed firms from 31 countries In the original Z-

Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from

the USUS Following the characteristics of these data we weight the firms so that the weights

for the failed and non-failed firms are equally In so doingthis way the non-proportional

sampling in from different countries does will not affect the re-estimated model The number of

firms from different countries however varies significantly leading to greater weights for larger

countries To avoid this problem the observations are also weighted so that each country has an

equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using

these weighted data and the resulting AUC is compared with that based on the original model

The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score

model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is

based on less- restrictive statistical assumptions than MDA In this estimation the dependent

variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA

does not require that independent variables be multivariate normal or that groups have equal

covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA

29

creates a score (logit) score L for every firm It is assumed that the independent variables are

linearly related to L This score or logit is used to determine the conditional probability of failure

as follows

= (eq 4)

where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the

original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing

the statistical significance of the difference between AUCs for this LR model and for the re-

estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it

is used as a benchmark for further statistical AUC comparisons because LR is applied as the

principal method in the testing of the remaining research hypotheses

The third hypothesis (H3) is associated with the performance effect of taking account of the

bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on

the following logit

(eq 5)

where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model

bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and

D1 = 1 when year = 2008 0 otherwise

D2 = 1 when year = 2009 0 otherwise

D3 = 1 when year = 2010 0 otherwise

)|1( XYp )( 441101

1

1

1XbXbbL ee

3

1

4

10

jjji

ii DcXbbL

30

The dummy variables do not directly refer to the bankruptcy year which that is not given in the

data basedatabase but rather to the last available year before bankruptcy For failed firms there

is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is

the base category If the AUC of this extended LR model statistically significantly exceeds the AUC

of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3

Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third

hypothesis above However for each hypothesis appropriate variables are used instead of the

year dummies Hypothesis H4 addresses the performance effect of taking size into account the

size and it will beis tested using by adding two additional variables measuring firm size in to the

LR -model In this LR model size is measured by the natural logarithm of total assets and its

squared form In this way the effect of logarithmic size can be reflected by in a function following

the second-order parabola Hypothesis H5 predicts that the classification performance improves

when the age of the firm is explicitly taken into account When testing this hypothesis the

category 6ndash14 years category is used as the base category and two dummy variables are

incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis

H6 looks at whether is associated with the classification performance is affected bythe effect of

the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt

is tested here using dummy variables for seven industries (D1 restaurants and hotels D2

construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and

water production D7 information technology) with all other industries acting as the base

category

31

Hypothesis H7 predicts that the explicit consideration of the country of origin will improves

classification performance This hypothesis is tested by using country risk measures instead ofnot

using dummy variables for countries but using country risk measures instead The country risk of

each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the

annual closing of accounts The rating is numerically recoded such that the best rating AAA

equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating

D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial

ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-

modelLR models with the original four financial ratios and the additional variables specified in

the hypotheses are estimated for all data In addition an LR model including all additional

variables is estimated for all data to assess the simultaneous effect of all variables Finally six of

the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not

included in this country- level testing hypothesis H7 is not tested because the additional variable

(country risk) is constant within the country

6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models

Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score

model for all data The variation in the ratios is significant as shown by the standard deviation

and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-

failed firms are close to each other indicating a symmetry of distributions However this is not

32

the case for the failed firms For the faileddistressed firms the median exceeds the mean for

these three ratios indicating negatively skewed distributions For X4 (BVETD) the means

significantly exceed the median for both failed and non-failed firms indicating a positively

skewed distribution For each of the four variables both the mean and the median are higher for

non-failed firms than for failed firms which is consistent with the expectations The difference

between the means of the non-failed and failed firms is larger in the original USUS data than in

our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is

approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of

EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may

indicate lower classification accuracy than in the original sample

(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates

(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the

large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model

Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same

statistical method or MDA The coefficients here are negative because the models are estimated

using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight

than in the original USUS data while the weights of WCTA and BVETD have proportionally

decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the

logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR

model These coefficients are directly comparable with those of the MDA model which isas

expected for the this exceptionally large sample The differences in the coefficients of the original

33

four variables among the eight LR-modelLR models (Models 1-8) are small For each model the

coefficient of BVETD is very close to zero This indicatinges that the original four variables and

the additional variables are quite independent of each other

(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-

modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate

that after 2007 (the base category) this risk of failure significantly decreased year by year The

base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due

to as a result of the global financial crisis The crisis played a significant role in the failure of key

businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The

effects were especially felt in Europe The coefficients of Model 4 for the size variables show that

the contribution of size on to the logit (risk measure) is atreaches its maximum value when

logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model

5 confirms the riskiness of young firms because the risk of failure is very high for newly founded

firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The

coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky

industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is

statistically significant (because of the large sample) but negative and very close to zero Finally

the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7

62 All data performance of the Zrsquorsquo-Score models

34

Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1

refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of

all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average

accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)

Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its

performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The

lower part of the table shows the AUCs for the countries included only in the test data The

performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)

samples It is also low and for the liquidation firms in the United Kingdom

The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5

presents the differences of these medians between the non-failed and failed firms by country

This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of

medians This correlation which is high for each financial ratio showing that the effects of the

ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large

except for EBITTA which This implies with the exceptionally high AUC that the ST firms

systematically differ from the non-ST firms although the differences are not extremely large The

differences between the medians are very large in Poland for each ratio justifying the high AUC

They are also large and in Finland and in the Czech Republic where the difference in EBITTA is

average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in

all four ratios are below the average a result thatwhich is obviously associated with a low AUC

35

In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even

negative In Iceland and Ireland the differences only in EBITTA are exceptionally small

(Table 4 here)

(Table 5 here)

Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by

MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)

supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is

at approximately the average level The re-estimation of the coefficients has led to improved

classification accuracy in a number of countries especially in Bosnia China (ST) Norway and

Greece However it has impaired the classification accuracy in the UK and China (delisted)

Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-

modelLR model) showing the effect of the estimation method For the benchmark model the

AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between

among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR

model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country

This result was expected because the coefficients of the models are directly comparable The

similar results for the models may also indicate that the independent variables conform to

multinormality This last latter result confirms what most researchers in the field of default

classification models have concluded that the accuracy levels of MDA and logistical regression

36

models are extremely similar despite any statistical violations such as normality between MDA

and logistical regression models

Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)

than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects

are not positive for all countries The effects are positive for example for Russia Estonia

Germany Ireland and Latvia but these effects are not statistically significant There are

statistically significant negative effects for China (ST) and Serbia There are notable negative

effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are

due to the exceptional annual distributions of failed firms in these countries For the countries

with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the

countries with positive effects it is only a few percent For each group non-failed firms are quite

equally distributed over years When the coefficient of D3 is very low (-0666) it strongly

decreases the risk estimates of most failed firms in the former countries but of only a few failed

firms in the latter countries This which leads to the observed effects H3 is supported by

the level of the whole sample However in some country samples (at the level of individual

levelies) H3 is not supported

Model 4 (LR-modelLR model with size variables) performs better than the benchmark model

which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads

and to significant improvements in the AUC for eg China (delisted) and Austria For China

(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC

37

also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs

in a few countries but thise decrease is not significant Model 4 is based on the four original

variables and the size effect following a second-order parabola This kind of size effect is very

small for micro firms but increases when approaching its maximum value in middle-sized firms

The countries with a positive change in AUC typically have data where in which the percent of

failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized

failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In

contrast the countries with a negative change in AUC have data where in which the percentage

of failed micro firms is exceptionally high (70-80)

Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly

the same AUC as the benchmark model (AUC = 0748) However the difference is positive and

statistically significant giving at least marginal support to H5 (age effect) For nearly all countries

the effect of the age on AUC is small For Austria however thise effect is however positive and

significant For Austria the percentage of non-failed young firms (less than 6 years) among non-

failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed

firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong

positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only

a few non-failed firms which This obviously leads to an improvement in AUC

Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC

supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its

38

AUC is notably higher than the benchmark AUC only in only a few countries such asfor example

France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg

restaurants and hotels and the information technology industryies but it has a positive effect

for on the construction and manufacturing industries For the countries with a positive effect on

AUC the percent of non-failed firms in restaurants and hotels and information technology is

high while that of failed firms is low For the risky industries (construction and manufacturing)

these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many

failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)

firms Therefore the AUC will be increasesd This situation is reversed for the countries with a

negative effect on AUC (Austria and Slovenia) The samples of these countries include a high

percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC

Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher

classification performance (AUC = 0749) than the benchmark model This result gives only very

weak support for H7 (country of origin effect) However for each country the resultant AUC is

nearly identical to that given by the benchmark model This result was expected due to the

negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7

This result implies that country risk hasdoes not have noany effect on the boundary between

bankrupt and non-bankrupt firms

Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional

variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to

39

the benchmark AUC in all test data However the effect on the AUCs largely varies and is either

negative or positive in different countries The positive effect is large in several countries such

as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative

effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show

that the inclusion of additional variables in the original model will usually increase the AUC but

not in every country

The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with

the USUS firms being unlike the majority of other firms in this study being listed or delisted

companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese

firm samples is associated with very small differences between the medians of the non-failed and

failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable

with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed

firms is very high Prior studies based on Chinese ST -firms have also demonstrated good

predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for

additional research and modeling work regarding unlisted and delisted Chinese firms

63 Country-level data performance of the Zrsquorsquo-Score models

The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform

all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the

different models estimated for each country separately (country-level models) In this table the

ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated

40

from the country data this benchmark will beis clearly outperformed by the resulting MDA

(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and

Sweden) However these results give only weak support for H1 at the country level because the

effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and

2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs

given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed

by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation

method)

Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC

than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3

leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United

StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in

nearly every country supporting H4 This improvement is significant however only in the United

StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark

model but for which it is only 0710 for the benchmark model It This improvement is also notable

in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age

category dummies) gives higher AUCs than the benchmark model for several countries but none

of the improvements are significant The positive effect is particularly strong especially in

Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only

weakly supports H5 because these negative effects are relatively small

41

Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects

on AUCs when compared with the benchmark However the only significant effect is the negative

effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the

industry dummies However there are notable positive effects on the AUC in for example

France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak

support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in

AUC compared with the benchmark model in Colombia Iceland and the Unites States It also

leads and to notable improvements in AUC at least in Belgium Denmark Estonia France

Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are

found only in Austria and Slovenia Thus as a conclusion we can concludestate that the

classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can

remarkably increases in most countries when different effects are taken into account by

additional variables This increase is found in most European countries and in Colombia and the

United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-

modelLR model and it can only be slightly improved only slightly by additional variables

(Table 6 here)

7 Summary of the study and a suggested extension

The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model

originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is

assessed using a very large international data set We test how the original version of the Zrsquorsquo-

42

Score model performs in different countries and how re-estimations using another statistical

method and different additional variables affect the classification performance when the data

are very heterogeneous For this kind of testing seven research hypotheses on classification

performance are extractedformulated These research hypotheses are tested for all data and

separately for country data (country-level analysis) The estimation data are from 31 countries

and the results are validated for 34 countries The countries are mainly from Europe but three

non-European countries are included (China Colombia and the United StatesUS) The statuses

used in the classification are mainly bankruptcyactive but receivership firms are also considered

to have failed In the Chinese data ST (special treatment) and delisted firms are also separately

analyzed as failed firms

The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very

satisfactorily in an international context The effects of the four financial ratios on performance

are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small

contribution in re-estimation The original model performs very well in several countries such as

Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only

marginally improved the classification performance thus weakly supporting the obsolescence

hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust

across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion

holds for the re-estimation of the model using LRA because the performance results are very

similar with to those of MDA (H2) The use of additional variables in the model generally

improvesd the classification accuracy of the original model but the results for countries are

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

18

restricting assumptions of MDA In LRA the multivariate normality of the independent variables

is not required nor are homoscedasticity and linearity For the sake of OLS MDA can be more

useful than LRA for small samples such as the original sample of 66 firms used in the estimation

of the Zrsquorsquo-Score mModel However in a large sample LRA may potentially perform better In this

study we use large samples which is advantageous for LRA Therefore our second hypothesis

(H2) suggests is that the classification performance of the re-estimated Zrsquorsquo-Score model will be

improved when it is estimated using LRA instead of MDA The model re-estimated for the original

variables using LRA and all pooled data is called here the Zrsquorsquo-Score LR-modelLR model The

performance of this re-estimated model is used in this study as the benchmark for further

analyses Thus the second hypothesis is as follows

H2 The prediction accuracy of the logistic regression version of the Zrsquorsquo-Score model is higher

than that of the multiple discriminant analysis version

H3 Bankruptcy year The model based on the relationship between bankruptcy and financial

ratios is likely to be affected by the macroeconomic environment These effects may significantly

decrease the classification accuracy of the model If the model is estimated using data from one

year and it will be applied to data from another year the validity of the model can be questioned

Business cycles inIn terms of economic growth credit policy and interest rates business cycles

can ahave an effect on the boundary between bankrupt and non-bankrupt firms The original Zrsquorsquo-

Score model is estimated using data from the period 1946ndash1965 period which includes several

business cycles Therefore the model is not focused on any specific stage of a cycle and does not

explicitly take into account of the bankruptcy year Altman (1983) suggested gathering data from

19

firms for the last most recent few years when developing a prediction model In this study the

benchmark Zrsquorsquo-Score LR -model will beis estimated for a shorter period than in the original

estimation although it covers several recent years on for different stages of the business cycle in

different countries Therefore the third hypothesis (H3) assumes is that the classification

accuracy of the benchmark model can be increased by explicitly taking into account of the year

of bankruptcy in the estimation of the model estimation The third hypothesis is presented as

follows

H3 The prediction accuracy of the modelrsquos prediction accuracy is higher whenith the

effect of the year of bankruptcy is included is higher than that of the model without the

effect

H4 Size of the firm The boundary between bankrupt and non-bankrupt firms is different for small

and larger firms which decreases the performance of the model estimation when estimated for

data from one size category and isare applied to for data from another size category For the

bankrupt and non-bankrupt firms in the original data for Zrsquorsquo-Score model estimation the range

of asset sizes was ranged between approximately 1 and 25 million US dollars The data did not

include very small or very large firms Altman (1983) regarded the suitability of the original Z-

Score model (and likewisein the same way the Zrsquorsquo-Score model) for all firms as debatable

because of this omission it did not scrutinize very large or very small firms In the currentis study

the benchmark Zrsquorsquo-Score LR -model will beis estimated for data from many size categories from

very small firms to very large firms The fourth hypothesis (H4) assumes that the classification

performance of the uniform benchmark LR -model based on the original four financial variables

20

of the Zrsquorsquo-Score model is improved when the size category of the firm is explicitly taken into

account Thus we present the fourth hypothesis as follows

H4 The modelrsquos prediction accuracy of the modelis higher whenith the effect of size is

includede is higher than that of the model without this effect

H5 Age of the firm International insolvency statistics generally show that bankruptcy risk is a

function of the age of the firm Very young firms typically show a very high risk The original Zrsquorsquo-

Score model does not explicitly take age into account age However Altman (1983) noted that

the age of a firm is implicitly considered in the Retained EarningsTotal Assets ratio (X2) which

was regarded as a new ratio in the bankruptcy prediction context A relatively young firm will

probably show a low ratio because it has not had time to build up cumulative profits Thus a

young firm is to some degree discriminated against in the model and its likelihood of being

classified as bankrupt is relatively higher than that of an older firm The incidence of failure is

much higher in the early years of a firm AlthoughDespite that the age of the firm in this way is

implicitly taken into account in X2 we expect that an explicit consideration of the age will improve

the classification accuracy bydue to controlling for the age factor Therefore the fifth hypothesis

(H5) proposes that the performance of the uniform benchmark model based on the original four

financial variables of the Zrsquorsquo-Score model is increasesd when the age of the firm is explicitly taken

into account The fifth hypothesis is can be presented as follows

H5 The modelrsquos prediction accuracy of the model is higher when with the effect of firm

age is higher than that of the model without this effectincluded

21

H6 Industry of the firm The original Zrsquo-Score model was estimated only for manufacturing firms

Altman (1983) stated that it would be ideal to develop a bankruptcy prediction model utilizing a

homogenous group of bankrupt firms If we are interested in a particular industry grouping we

should gather data from bankrupt and non-bankrupt firms in these groupings Previous studies

show that financial distress analysis is influenced by the industry effect (Smith and Liou 2007)

Firms in different industries tend to report different levels of the same financial ratios which may

have an effect on the boundary between bankrupt and non-bankrupt firms This industry effect

may be present in the Zrsquo-Score model especially due to the SalesTotal Assets ratio (X5) This ratio

which showed the least significance on a univariate basis while making but it had made a very

significant contribution to the discriminant power of the multivariate model Altman (1983)

recognized the potential industry effect due to a the wide variation among industries in asset

turnover and specified the Zrsquorsquo-Score model without X5 However also the Zrsquorsquo-Score model was

also estimated using the original sample of manufacturing firms In our analysis the uniform

benchmark model based on the original four financial variables of the Zrsquorsquo-Score model is

estimated for a statistical sample representing different industries Therefore the sixth

hypothesis (H6) assumes that an explicit consideration of industry will improve the classification

accuracy of this benchmark model H6It can be expressed in the following form

H6 The modelrsquos prediction accuracy of the modelis higher whenith the effect of industry is

higher than that of the model without this effectincluded

H7 Country of origin The original Zrsquorsquo-Score model has been estimated only for USUS firms It

can be expected that the international applicability of the model to other countries is affected by

22

country-specific differences The eEconomic environment legislation culture financial markets

and accounting practices in a country may affect the financial behavior of firms and the boundary

between bankrupt and non-bankrupt firms These factors may potentially weaken the

classification performance of the model in for other countries outside other than that e one fore

country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the

seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm

into account will improve the classification accuracy of the benchmark model In our empirical

study the country effect will beis assessed by including a variable of for country risk Therefore

the seventh hypothesis is presented as follows

H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country

risk is higher than that of the model without this effectincluded

5 Empirical data and statistical methods

51 Sample of firms

The principal data of for this study are were extracted from the ORBIS databases of Bureau Van

Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the

moment of sampling contained administrative information on more than 50 million European

firms However income statement and balance sheet information was available for

approximately 8 million companies More than 99 of the companies covered in this database

are private companies from different various industries justifying the use of the Zrsquorsquo-Score model

instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all

industrial groupings and for both private and public entities (Altman 1983 124 Altman and

Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope

23

of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all

industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly

used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that

international comparability may be a problem when administrative firm-level data are

internationally pooled For administrative data Although the definition of variables is usually less

harmonized for administrative data tThis is less of a problem in the ORBIS database because of

the common international format of balance sheets For example although some discrepancies

in profitloss statements may arise because of differences in fiscal systems across countries

balance sheet variables largely adhere to international standards

AThere are a number of factors that influence the international applicability of bankruptcy

prediction models These factors include accounting legislation and practice creditor rights and

investor protection judicial efficiency corporate governance bankruptcy protection and

insolvency management and firm risk-taking These factors strongly differ between European

and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-

Score model outside Europe First it is especiallyparticularly important to include the USUS as

because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It

is include d in our study as well because it has the largest market capitalization in the world

Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it

applicable to emerging market companies Therefore we include firms from China and Colombia

which represent two very culturally and institutionally very dissimilar emerging market

countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt

24

firms ie the limit we set for European countries) from ORBIS World were not

available Thus the results are also estimated and tested for three non-European countries (the

United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-

Score modelrsquos performance The samples of firms from these countries are were extracted from

ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms

from around the world

SFor statistical sampling several requirements are set for the statistical sampling of the empirical

data First we require that the firm to be selected be an industrial (non-financial) company

Second its owners must have limited liability (so partnerships and sole proprietors are left out

of the study) Third we set a minimum requirement for the size of the firm Because the financial

ratios of very small firms are generally too unstable for a failure prediction model these firms

are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have

exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we

include in our estimation sample firms from all European countries and 3 pre-selected non-

European countries where the number of failed firms is more greater than 60 If the number of

failed firms for any European country is less than 60 the firms from this country are only included

only in the test sample For qualifying European countries the firms are randomly classified in

the estimation and test samples so that the number of firms is approximately equal in both

samples Thus our estimation data include firms from 28 European and 3 non-European

countries Fifth all failed firms that fulfill the above requirements are included in our samples

However if the number of non-failed firms in a country is very high a sample is randomly

25

selected from that country Finally the time span of fiscal years potentially available for this study

ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in

the database are from a financial period within 2007 and 2010 earlier years are also excluded

for non-failed firms for comparability All qualifying observations of non-failed firms from the

years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses

of failed firms to the last most recent financial statements available before failure The four

independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize

outliers

Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test

data by country The estimation sample includes data from 2602563 non-failed and 38215

failed firms from 28 European and 3 non-European countries The test sample is slightly larger

because it includes data from 31 European and 3 non-European countries From For the country

of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt

firms The available US data that were available consist only of listed (and delisted) firms From

China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are

included in the estimation sample although there are only 32 such firms altogether7 The Chinese

datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status

are separately analyzed separately only in for the test data8 ST firms are listed firms suffering

6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor

26

from serious financial difficulties Excluding the special US data and the two non-private Chinese

datasets 994 of the observations in the data are private firms

(Table 1 here)

52 Status of failed firms

ORBIS has five classes for potentially active firms (active default of payment receivership

dormant and branch) and seven classes for inactive firms that no longer carry out business

activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation

branch and no precision) From Among these classes only active is selected to represent non-

distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by

considering (with exceptions described below) a firm failed if its status in ORBIS is stated as

bankruptcy However because of the small number of bankrupt firms in some countries we also

consider receivership (active) firms as failed even if they are active These firms generally suffer

from serious financial distress However firms in liquidation are generally not included in the

sample of failed firms Firms in liquidation may depending on the country contain firms that

have ceased activities due to reasons other than failure (mergers discontinuing the operations

of a daughter company or of a foreign branch etc) Therefore for most countries we select only

firms that are coded as being bankrupt or under receivership However there are a number of

special cases where failed firms are coded under a different status heading These countries or

samples are the following

Country Status categories Bulgaria In liquidation Bankruptcy

27

Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the

study (for example Switzerland) If a country had only a very small number of failed firms it was

dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries

including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status

classes (including the bankruptcy category) are not completely homogenous within European

countries due to different legislations although there are obvious similarities in insolvency acts

(Philippe et al 2002) China is a special case because it includes samples with three different

criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are

two different samples (liquidation and receivership)

53 Statistical methods

In this study seven research hypotheses are drawn for statistical testing The statistical analysis

will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The

classification performance of the original model is assessed by the AUC (Area Under Curve)

measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close

connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random

28

model and 05 for a model with an average classification performance In SAS software is used

for all statistical analysess SAS software is used

The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is

tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical

method (multiple discriminant analysis or MDA) The problem is that the estimation sample

includes different numbers of failed and non-failed firms from 31 countries In the original Z-

Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from

the USUS Following the characteristics of these data we weight the firms so that the weights

for the failed and non-failed firms are equally In so doingthis way the non-proportional

sampling in from different countries does will not affect the re-estimated model The number of

firms from different countries however varies significantly leading to greater weights for larger

countries To avoid this problem the observations are also weighted so that each country has an

equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using

these weighted data and the resulting AUC is compared with that based on the original model

The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score

model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is

based on less- restrictive statistical assumptions than MDA In this estimation the dependent

variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA

does not require that independent variables be multivariate normal or that groups have equal

covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA

29

creates a score (logit) score L for every firm It is assumed that the independent variables are

linearly related to L This score or logit is used to determine the conditional probability of failure

as follows

= (eq 4)

where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the

original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing

the statistical significance of the difference between AUCs for this LR model and for the re-

estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it

is used as a benchmark for further statistical AUC comparisons because LR is applied as the

principal method in the testing of the remaining research hypotheses

The third hypothesis (H3) is associated with the performance effect of taking account of the

bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on

the following logit

(eq 5)

where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model

bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and

D1 = 1 when year = 2008 0 otherwise

D2 = 1 when year = 2009 0 otherwise

D3 = 1 when year = 2010 0 otherwise

)|1( XYp )( 441101

1

1

1XbXbbL ee

3

1

4

10

jjji

ii DcXbbL

30

The dummy variables do not directly refer to the bankruptcy year which that is not given in the

data basedatabase but rather to the last available year before bankruptcy For failed firms there

is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is

the base category If the AUC of this extended LR model statistically significantly exceeds the AUC

of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3

Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third

hypothesis above However for each hypothesis appropriate variables are used instead of the

year dummies Hypothesis H4 addresses the performance effect of taking size into account the

size and it will beis tested using by adding two additional variables measuring firm size in to the

LR -model In this LR model size is measured by the natural logarithm of total assets and its

squared form In this way the effect of logarithmic size can be reflected by in a function following

the second-order parabola Hypothesis H5 predicts that the classification performance improves

when the age of the firm is explicitly taken into account When testing this hypothesis the

category 6ndash14 years category is used as the base category and two dummy variables are

incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis

H6 looks at whether is associated with the classification performance is affected bythe effect of

the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt

is tested here using dummy variables for seven industries (D1 restaurants and hotels D2

construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and

water production D7 information technology) with all other industries acting as the base

category

31

Hypothesis H7 predicts that the explicit consideration of the country of origin will improves

classification performance This hypothesis is tested by using country risk measures instead ofnot

using dummy variables for countries but using country risk measures instead The country risk of

each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the

annual closing of accounts The rating is numerically recoded such that the best rating AAA

equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating

D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial

ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-

modelLR models with the original four financial ratios and the additional variables specified in

the hypotheses are estimated for all data In addition an LR model including all additional

variables is estimated for all data to assess the simultaneous effect of all variables Finally six of

the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not

included in this country- level testing hypothesis H7 is not tested because the additional variable

(country risk) is constant within the country

6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models

Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score

model for all data The variation in the ratios is significant as shown by the standard deviation

and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-

failed firms are close to each other indicating a symmetry of distributions However this is not

32

the case for the failed firms For the faileddistressed firms the median exceeds the mean for

these three ratios indicating negatively skewed distributions For X4 (BVETD) the means

significantly exceed the median for both failed and non-failed firms indicating a positively

skewed distribution For each of the four variables both the mean and the median are higher for

non-failed firms than for failed firms which is consistent with the expectations The difference

between the means of the non-failed and failed firms is larger in the original USUS data than in

our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is

approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of

EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may

indicate lower classification accuracy than in the original sample

(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates

(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the

large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model

Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same

statistical method or MDA The coefficients here are negative because the models are estimated

using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight

than in the original USUS data while the weights of WCTA and BVETD have proportionally

decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the

logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR

model These coefficients are directly comparable with those of the MDA model which isas

expected for the this exceptionally large sample The differences in the coefficients of the original

33

four variables among the eight LR-modelLR models (Models 1-8) are small For each model the

coefficient of BVETD is very close to zero This indicatinges that the original four variables and

the additional variables are quite independent of each other

(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-

modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate

that after 2007 (the base category) this risk of failure significantly decreased year by year The

base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due

to as a result of the global financial crisis The crisis played a significant role in the failure of key

businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The

effects were especially felt in Europe The coefficients of Model 4 for the size variables show that

the contribution of size on to the logit (risk measure) is atreaches its maximum value when

logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model

5 confirms the riskiness of young firms because the risk of failure is very high for newly founded

firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The

coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky

industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is

statistically significant (because of the large sample) but negative and very close to zero Finally

the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7

62 All data performance of the Zrsquorsquo-Score models

34

Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1

refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of

all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average

accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)

Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its

performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The

lower part of the table shows the AUCs for the countries included only in the test data The

performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)

samples It is also low and for the liquidation firms in the United Kingdom

The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5

presents the differences of these medians between the non-failed and failed firms by country

This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of

medians This correlation which is high for each financial ratio showing that the effects of the

ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large

except for EBITTA which This implies with the exceptionally high AUC that the ST firms

systematically differ from the non-ST firms although the differences are not extremely large The

differences between the medians are very large in Poland for each ratio justifying the high AUC

They are also large and in Finland and in the Czech Republic where the difference in EBITTA is

average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in

all four ratios are below the average a result thatwhich is obviously associated with a low AUC

35

In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even

negative In Iceland and Ireland the differences only in EBITTA are exceptionally small

(Table 4 here)

(Table 5 here)

Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by

MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)

supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is

at approximately the average level The re-estimation of the coefficients has led to improved

classification accuracy in a number of countries especially in Bosnia China (ST) Norway and

Greece However it has impaired the classification accuracy in the UK and China (delisted)

Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-

modelLR model) showing the effect of the estimation method For the benchmark model the

AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between

among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR

model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country

This result was expected because the coefficients of the models are directly comparable The

similar results for the models may also indicate that the independent variables conform to

multinormality This last latter result confirms what most researchers in the field of default

classification models have concluded that the accuracy levels of MDA and logistical regression

36

models are extremely similar despite any statistical violations such as normality between MDA

and logistical regression models

Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)

than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects

are not positive for all countries The effects are positive for example for Russia Estonia

Germany Ireland and Latvia but these effects are not statistically significant There are

statistically significant negative effects for China (ST) and Serbia There are notable negative

effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are

due to the exceptional annual distributions of failed firms in these countries For the countries

with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the

countries with positive effects it is only a few percent For each group non-failed firms are quite

equally distributed over years When the coefficient of D3 is very low (-0666) it strongly

decreases the risk estimates of most failed firms in the former countries but of only a few failed

firms in the latter countries This which leads to the observed effects H3 is supported by

the level of the whole sample However in some country samples (at the level of individual

levelies) H3 is not supported

Model 4 (LR-modelLR model with size variables) performs better than the benchmark model

which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads

and to significant improvements in the AUC for eg China (delisted) and Austria For China

(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC

37

also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs

in a few countries but thise decrease is not significant Model 4 is based on the four original

variables and the size effect following a second-order parabola This kind of size effect is very

small for micro firms but increases when approaching its maximum value in middle-sized firms

The countries with a positive change in AUC typically have data where in which the percent of

failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized

failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In

contrast the countries with a negative change in AUC have data where in which the percentage

of failed micro firms is exceptionally high (70-80)

Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly

the same AUC as the benchmark model (AUC = 0748) However the difference is positive and

statistically significant giving at least marginal support to H5 (age effect) For nearly all countries

the effect of the age on AUC is small For Austria however thise effect is however positive and

significant For Austria the percentage of non-failed young firms (less than 6 years) among non-

failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed

firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong

positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only

a few non-failed firms which This obviously leads to an improvement in AUC

Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC

supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its

38

AUC is notably higher than the benchmark AUC only in only a few countries such asfor example

France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg

restaurants and hotels and the information technology industryies but it has a positive effect

for on the construction and manufacturing industries For the countries with a positive effect on

AUC the percent of non-failed firms in restaurants and hotels and information technology is

high while that of failed firms is low For the risky industries (construction and manufacturing)

these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many

failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)

firms Therefore the AUC will be increasesd This situation is reversed for the countries with a

negative effect on AUC (Austria and Slovenia) The samples of these countries include a high

percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC

Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher

classification performance (AUC = 0749) than the benchmark model This result gives only very

weak support for H7 (country of origin effect) However for each country the resultant AUC is

nearly identical to that given by the benchmark model This result was expected due to the

negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7

This result implies that country risk hasdoes not have noany effect on the boundary between

bankrupt and non-bankrupt firms

Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional

variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to

39

the benchmark AUC in all test data However the effect on the AUCs largely varies and is either

negative or positive in different countries The positive effect is large in several countries such

as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative

effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show

that the inclusion of additional variables in the original model will usually increase the AUC but

not in every country

The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with

the USUS firms being unlike the majority of other firms in this study being listed or delisted

companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese

firm samples is associated with very small differences between the medians of the non-failed and

failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable

with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed

firms is very high Prior studies based on Chinese ST -firms have also demonstrated good

predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for

additional research and modeling work regarding unlisted and delisted Chinese firms

63 Country-level data performance of the Zrsquorsquo-Score models

The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform

all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the

different models estimated for each country separately (country-level models) In this table the

ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated

40

from the country data this benchmark will beis clearly outperformed by the resulting MDA

(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and

Sweden) However these results give only weak support for H1 at the country level because the

effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and

2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs

given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed

by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation

method)

Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC

than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3

leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United

StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in

nearly every country supporting H4 This improvement is significant however only in the United

StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark

model but for which it is only 0710 for the benchmark model It This improvement is also notable

in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age

category dummies) gives higher AUCs than the benchmark model for several countries but none

of the improvements are significant The positive effect is particularly strong especially in

Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only

weakly supports H5 because these negative effects are relatively small

41

Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects

on AUCs when compared with the benchmark However the only significant effect is the negative

effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the

industry dummies However there are notable positive effects on the AUC in for example

France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak

support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in

AUC compared with the benchmark model in Colombia Iceland and the Unites States It also

leads and to notable improvements in AUC at least in Belgium Denmark Estonia France

Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are

found only in Austria and Slovenia Thus as a conclusion we can concludestate that the

classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can

remarkably increases in most countries when different effects are taken into account by

additional variables This increase is found in most European countries and in Colombia and the

United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-

modelLR model and it can only be slightly improved only slightly by additional variables

(Table 6 here)

7 Summary of the study and a suggested extension

The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model

originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is

assessed using a very large international data set We test how the original version of the Zrsquorsquo-

42

Score model performs in different countries and how re-estimations using another statistical

method and different additional variables affect the classification performance when the data

are very heterogeneous For this kind of testing seven research hypotheses on classification

performance are extractedformulated These research hypotheses are tested for all data and

separately for country data (country-level analysis) The estimation data are from 31 countries

and the results are validated for 34 countries The countries are mainly from Europe but three

non-European countries are included (China Colombia and the United StatesUS) The statuses

used in the classification are mainly bankruptcyactive but receivership firms are also considered

to have failed In the Chinese data ST (special treatment) and delisted firms are also separately

analyzed as failed firms

The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very

satisfactorily in an international context The effects of the four financial ratios on performance

are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small

contribution in re-estimation The original model performs very well in several countries such as

Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only

marginally improved the classification performance thus weakly supporting the obsolescence

hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust

across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion

holds for the re-estimation of the model using LRA because the performance results are very

similar with to those of MDA (H2) The use of additional variables in the model generally

improvesd the classification accuracy of the original model but the results for countries are

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

19

firms for the last most recent few years when developing a prediction model In this study the

benchmark Zrsquorsquo-Score LR -model will beis estimated for a shorter period than in the original

estimation although it covers several recent years on for different stages of the business cycle in

different countries Therefore the third hypothesis (H3) assumes is that the classification

accuracy of the benchmark model can be increased by explicitly taking into account of the year

of bankruptcy in the estimation of the model estimation The third hypothesis is presented as

follows

H3 The prediction accuracy of the modelrsquos prediction accuracy is higher whenith the

effect of the year of bankruptcy is included is higher than that of the model without the

effect

H4 Size of the firm The boundary between bankrupt and non-bankrupt firms is different for small

and larger firms which decreases the performance of the model estimation when estimated for

data from one size category and isare applied to for data from another size category For the

bankrupt and non-bankrupt firms in the original data for Zrsquorsquo-Score model estimation the range

of asset sizes was ranged between approximately 1 and 25 million US dollars The data did not

include very small or very large firms Altman (1983) regarded the suitability of the original Z-

Score model (and likewisein the same way the Zrsquorsquo-Score model) for all firms as debatable

because of this omission it did not scrutinize very large or very small firms In the currentis study

the benchmark Zrsquorsquo-Score LR -model will beis estimated for data from many size categories from

very small firms to very large firms The fourth hypothesis (H4) assumes that the classification

performance of the uniform benchmark LR -model based on the original four financial variables

20

of the Zrsquorsquo-Score model is improved when the size category of the firm is explicitly taken into

account Thus we present the fourth hypothesis as follows

H4 The modelrsquos prediction accuracy of the modelis higher whenith the effect of size is

includede is higher than that of the model without this effect

H5 Age of the firm International insolvency statistics generally show that bankruptcy risk is a

function of the age of the firm Very young firms typically show a very high risk The original Zrsquorsquo-

Score model does not explicitly take age into account age However Altman (1983) noted that

the age of a firm is implicitly considered in the Retained EarningsTotal Assets ratio (X2) which

was regarded as a new ratio in the bankruptcy prediction context A relatively young firm will

probably show a low ratio because it has not had time to build up cumulative profits Thus a

young firm is to some degree discriminated against in the model and its likelihood of being

classified as bankrupt is relatively higher than that of an older firm The incidence of failure is

much higher in the early years of a firm AlthoughDespite that the age of the firm in this way is

implicitly taken into account in X2 we expect that an explicit consideration of the age will improve

the classification accuracy bydue to controlling for the age factor Therefore the fifth hypothesis

(H5) proposes that the performance of the uniform benchmark model based on the original four

financial variables of the Zrsquorsquo-Score model is increasesd when the age of the firm is explicitly taken

into account The fifth hypothesis is can be presented as follows

H5 The modelrsquos prediction accuracy of the model is higher when with the effect of firm

age is higher than that of the model without this effectincluded

21

H6 Industry of the firm The original Zrsquo-Score model was estimated only for manufacturing firms

Altman (1983) stated that it would be ideal to develop a bankruptcy prediction model utilizing a

homogenous group of bankrupt firms If we are interested in a particular industry grouping we

should gather data from bankrupt and non-bankrupt firms in these groupings Previous studies

show that financial distress analysis is influenced by the industry effect (Smith and Liou 2007)

Firms in different industries tend to report different levels of the same financial ratios which may

have an effect on the boundary between bankrupt and non-bankrupt firms This industry effect

may be present in the Zrsquo-Score model especially due to the SalesTotal Assets ratio (X5) This ratio

which showed the least significance on a univariate basis while making but it had made a very

significant contribution to the discriminant power of the multivariate model Altman (1983)

recognized the potential industry effect due to a the wide variation among industries in asset

turnover and specified the Zrsquorsquo-Score model without X5 However also the Zrsquorsquo-Score model was

also estimated using the original sample of manufacturing firms In our analysis the uniform

benchmark model based on the original four financial variables of the Zrsquorsquo-Score model is

estimated for a statistical sample representing different industries Therefore the sixth

hypothesis (H6) assumes that an explicit consideration of industry will improve the classification

accuracy of this benchmark model H6It can be expressed in the following form

H6 The modelrsquos prediction accuracy of the modelis higher whenith the effect of industry is

higher than that of the model without this effectincluded

H7 Country of origin The original Zrsquorsquo-Score model has been estimated only for USUS firms It

can be expected that the international applicability of the model to other countries is affected by

22

country-specific differences The eEconomic environment legislation culture financial markets

and accounting practices in a country may affect the financial behavior of firms and the boundary

between bankrupt and non-bankrupt firms These factors may potentially weaken the

classification performance of the model in for other countries outside other than that e one fore

country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the

seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm

into account will improve the classification accuracy of the benchmark model In our empirical

study the country effect will beis assessed by including a variable of for country risk Therefore

the seventh hypothesis is presented as follows

H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country

risk is higher than that of the model without this effectincluded

5 Empirical data and statistical methods

51 Sample of firms

The principal data of for this study are were extracted from the ORBIS databases of Bureau Van

Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the

moment of sampling contained administrative information on more than 50 million European

firms However income statement and balance sheet information was available for

approximately 8 million companies More than 99 of the companies covered in this database

are private companies from different various industries justifying the use of the Zrsquorsquo-Score model

instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all

industrial groupings and for both private and public entities (Altman 1983 124 Altman and

Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope

23

of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all

industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly

used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that

international comparability may be a problem when administrative firm-level data are

internationally pooled For administrative data Although the definition of variables is usually less

harmonized for administrative data tThis is less of a problem in the ORBIS database because of

the common international format of balance sheets For example although some discrepancies

in profitloss statements may arise because of differences in fiscal systems across countries

balance sheet variables largely adhere to international standards

AThere are a number of factors that influence the international applicability of bankruptcy

prediction models These factors include accounting legislation and practice creditor rights and

investor protection judicial efficiency corporate governance bankruptcy protection and

insolvency management and firm risk-taking These factors strongly differ between European

and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-

Score model outside Europe First it is especiallyparticularly important to include the USUS as

because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It

is include d in our study as well because it has the largest market capitalization in the world

Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it

applicable to emerging market companies Therefore we include firms from China and Colombia

which represent two very culturally and institutionally very dissimilar emerging market

countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt

24

firms ie the limit we set for European countries) from ORBIS World were not

available Thus the results are also estimated and tested for three non-European countries (the

United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-

Score modelrsquos performance The samples of firms from these countries are were extracted from

ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms

from around the world

SFor statistical sampling several requirements are set for the statistical sampling of the empirical

data First we require that the firm to be selected be an industrial (non-financial) company

Second its owners must have limited liability (so partnerships and sole proprietors are left out

of the study) Third we set a minimum requirement for the size of the firm Because the financial

ratios of very small firms are generally too unstable for a failure prediction model these firms

are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have

exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we

include in our estimation sample firms from all European countries and 3 pre-selected non-

European countries where the number of failed firms is more greater than 60 If the number of

failed firms for any European country is less than 60 the firms from this country are only included

only in the test sample For qualifying European countries the firms are randomly classified in

the estimation and test samples so that the number of firms is approximately equal in both

samples Thus our estimation data include firms from 28 European and 3 non-European

countries Fifth all failed firms that fulfill the above requirements are included in our samples

However if the number of non-failed firms in a country is very high a sample is randomly

25

selected from that country Finally the time span of fiscal years potentially available for this study

ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in

the database are from a financial period within 2007 and 2010 earlier years are also excluded

for non-failed firms for comparability All qualifying observations of non-failed firms from the

years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses

of failed firms to the last most recent financial statements available before failure The four

independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize

outliers

Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test

data by country The estimation sample includes data from 2602563 non-failed and 38215

failed firms from 28 European and 3 non-European countries The test sample is slightly larger

because it includes data from 31 European and 3 non-European countries From For the country

of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt

firms The available US data that were available consist only of listed (and delisted) firms From

China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are

included in the estimation sample although there are only 32 such firms altogether7 The Chinese

datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status

are separately analyzed separately only in for the test data8 ST firms are listed firms suffering

6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor

26

from serious financial difficulties Excluding the special US data and the two non-private Chinese

datasets 994 of the observations in the data are private firms

(Table 1 here)

52 Status of failed firms

ORBIS has five classes for potentially active firms (active default of payment receivership

dormant and branch) and seven classes for inactive firms that no longer carry out business

activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation

branch and no precision) From Among these classes only active is selected to represent non-

distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by

considering (with exceptions described below) a firm failed if its status in ORBIS is stated as

bankruptcy However because of the small number of bankrupt firms in some countries we also

consider receivership (active) firms as failed even if they are active These firms generally suffer

from serious financial distress However firms in liquidation are generally not included in the

sample of failed firms Firms in liquidation may depending on the country contain firms that

have ceased activities due to reasons other than failure (mergers discontinuing the operations

of a daughter company or of a foreign branch etc) Therefore for most countries we select only

firms that are coded as being bankrupt or under receivership However there are a number of

special cases where failed firms are coded under a different status heading These countries or

samples are the following

Country Status categories Bulgaria In liquidation Bankruptcy

27

Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the

study (for example Switzerland) If a country had only a very small number of failed firms it was

dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries

including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status

classes (including the bankruptcy category) are not completely homogenous within European

countries due to different legislations although there are obvious similarities in insolvency acts

(Philippe et al 2002) China is a special case because it includes samples with three different

criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are

two different samples (liquidation and receivership)

53 Statistical methods

In this study seven research hypotheses are drawn for statistical testing The statistical analysis

will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The

classification performance of the original model is assessed by the AUC (Area Under Curve)

measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close

connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random

28

model and 05 for a model with an average classification performance In SAS software is used

for all statistical analysess SAS software is used

The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is

tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical

method (multiple discriminant analysis or MDA) The problem is that the estimation sample

includes different numbers of failed and non-failed firms from 31 countries In the original Z-

Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from

the USUS Following the characteristics of these data we weight the firms so that the weights

for the failed and non-failed firms are equally In so doingthis way the non-proportional

sampling in from different countries does will not affect the re-estimated model The number of

firms from different countries however varies significantly leading to greater weights for larger

countries To avoid this problem the observations are also weighted so that each country has an

equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using

these weighted data and the resulting AUC is compared with that based on the original model

The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score

model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is

based on less- restrictive statistical assumptions than MDA In this estimation the dependent

variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA

does not require that independent variables be multivariate normal or that groups have equal

covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA

29

creates a score (logit) score L for every firm It is assumed that the independent variables are

linearly related to L This score or logit is used to determine the conditional probability of failure

as follows

= (eq 4)

where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the

original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing

the statistical significance of the difference between AUCs for this LR model and for the re-

estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it

is used as a benchmark for further statistical AUC comparisons because LR is applied as the

principal method in the testing of the remaining research hypotheses

The third hypothesis (H3) is associated with the performance effect of taking account of the

bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on

the following logit

(eq 5)

where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model

bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and

D1 = 1 when year = 2008 0 otherwise

D2 = 1 when year = 2009 0 otherwise

D3 = 1 when year = 2010 0 otherwise

)|1( XYp )( 441101

1

1

1XbXbbL ee

3

1

4

10

jjji

ii DcXbbL

30

The dummy variables do not directly refer to the bankruptcy year which that is not given in the

data basedatabase but rather to the last available year before bankruptcy For failed firms there

is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is

the base category If the AUC of this extended LR model statistically significantly exceeds the AUC

of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3

Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third

hypothesis above However for each hypothesis appropriate variables are used instead of the

year dummies Hypothesis H4 addresses the performance effect of taking size into account the

size and it will beis tested using by adding two additional variables measuring firm size in to the

LR -model In this LR model size is measured by the natural logarithm of total assets and its

squared form In this way the effect of logarithmic size can be reflected by in a function following

the second-order parabola Hypothesis H5 predicts that the classification performance improves

when the age of the firm is explicitly taken into account When testing this hypothesis the

category 6ndash14 years category is used as the base category and two dummy variables are

incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis

H6 looks at whether is associated with the classification performance is affected bythe effect of

the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt

is tested here using dummy variables for seven industries (D1 restaurants and hotels D2

construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and

water production D7 information technology) with all other industries acting as the base

category

31

Hypothesis H7 predicts that the explicit consideration of the country of origin will improves

classification performance This hypothesis is tested by using country risk measures instead ofnot

using dummy variables for countries but using country risk measures instead The country risk of

each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the

annual closing of accounts The rating is numerically recoded such that the best rating AAA

equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating

D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial

ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-

modelLR models with the original four financial ratios and the additional variables specified in

the hypotheses are estimated for all data In addition an LR model including all additional

variables is estimated for all data to assess the simultaneous effect of all variables Finally six of

the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not

included in this country- level testing hypothesis H7 is not tested because the additional variable

(country risk) is constant within the country

6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models

Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score

model for all data The variation in the ratios is significant as shown by the standard deviation

and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-

failed firms are close to each other indicating a symmetry of distributions However this is not

32

the case for the failed firms For the faileddistressed firms the median exceeds the mean for

these three ratios indicating negatively skewed distributions For X4 (BVETD) the means

significantly exceed the median for both failed and non-failed firms indicating a positively

skewed distribution For each of the four variables both the mean and the median are higher for

non-failed firms than for failed firms which is consistent with the expectations The difference

between the means of the non-failed and failed firms is larger in the original USUS data than in

our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is

approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of

EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may

indicate lower classification accuracy than in the original sample

(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates

(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the

large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model

Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same

statistical method or MDA The coefficients here are negative because the models are estimated

using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight

than in the original USUS data while the weights of WCTA and BVETD have proportionally

decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the

logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR

model These coefficients are directly comparable with those of the MDA model which isas

expected for the this exceptionally large sample The differences in the coefficients of the original

33

four variables among the eight LR-modelLR models (Models 1-8) are small For each model the

coefficient of BVETD is very close to zero This indicatinges that the original four variables and

the additional variables are quite independent of each other

(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-

modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate

that after 2007 (the base category) this risk of failure significantly decreased year by year The

base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due

to as a result of the global financial crisis The crisis played a significant role in the failure of key

businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The

effects were especially felt in Europe The coefficients of Model 4 for the size variables show that

the contribution of size on to the logit (risk measure) is atreaches its maximum value when

logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model

5 confirms the riskiness of young firms because the risk of failure is very high for newly founded

firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The

coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky

industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is

statistically significant (because of the large sample) but negative and very close to zero Finally

the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7

62 All data performance of the Zrsquorsquo-Score models

34

Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1

refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of

all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average

accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)

Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its

performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The

lower part of the table shows the AUCs for the countries included only in the test data The

performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)

samples It is also low and for the liquidation firms in the United Kingdom

The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5

presents the differences of these medians between the non-failed and failed firms by country

This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of

medians This correlation which is high for each financial ratio showing that the effects of the

ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large

except for EBITTA which This implies with the exceptionally high AUC that the ST firms

systematically differ from the non-ST firms although the differences are not extremely large The

differences between the medians are very large in Poland for each ratio justifying the high AUC

They are also large and in Finland and in the Czech Republic where the difference in EBITTA is

average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in

all four ratios are below the average a result thatwhich is obviously associated with a low AUC

35

In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even

negative In Iceland and Ireland the differences only in EBITTA are exceptionally small

(Table 4 here)

(Table 5 here)

Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by

MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)

supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is

at approximately the average level The re-estimation of the coefficients has led to improved

classification accuracy in a number of countries especially in Bosnia China (ST) Norway and

Greece However it has impaired the classification accuracy in the UK and China (delisted)

Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-

modelLR model) showing the effect of the estimation method For the benchmark model the

AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between

among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR

model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country

This result was expected because the coefficients of the models are directly comparable The

similar results for the models may also indicate that the independent variables conform to

multinormality This last latter result confirms what most researchers in the field of default

classification models have concluded that the accuracy levels of MDA and logistical regression

36

models are extremely similar despite any statistical violations such as normality between MDA

and logistical regression models

Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)

than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects

are not positive for all countries The effects are positive for example for Russia Estonia

Germany Ireland and Latvia but these effects are not statistically significant There are

statistically significant negative effects for China (ST) and Serbia There are notable negative

effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are

due to the exceptional annual distributions of failed firms in these countries For the countries

with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the

countries with positive effects it is only a few percent For each group non-failed firms are quite

equally distributed over years When the coefficient of D3 is very low (-0666) it strongly

decreases the risk estimates of most failed firms in the former countries but of only a few failed

firms in the latter countries This which leads to the observed effects H3 is supported by

the level of the whole sample However in some country samples (at the level of individual

levelies) H3 is not supported

Model 4 (LR-modelLR model with size variables) performs better than the benchmark model

which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads

and to significant improvements in the AUC for eg China (delisted) and Austria For China

(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC

37

also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs

in a few countries but thise decrease is not significant Model 4 is based on the four original

variables and the size effect following a second-order parabola This kind of size effect is very

small for micro firms but increases when approaching its maximum value in middle-sized firms

The countries with a positive change in AUC typically have data where in which the percent of

failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized

failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In

contrast the countries with a negative change in AUC have data where in which the percentage

of failed micro firms is exceptionally high (70-80)

Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly

the same AUC as the benchmark model (AUC = 0748) However the difference is positive and

statistically significant giving at least marginal support to H5 (age effect) For nearly all countries

the effect of the age on AUC is small For Austria however thise effect is however positive and

significant For Austria the percentage of non-failed young firms (less than 6 years) among non-

failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed

firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong

positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only

a few non-failed firms which This obviously leads to an improvement in AUC

Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC

supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its

38

AUC is notably higher than the benchmark AUC only in only a few countries such asfor example

France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg

restaurants and hotels and the information technology industryies but it has a positive effect

for on the construction and manufacturing industries For the countries with a positive effect on

AUC the percent of non-failed firms in restaurants and hotels and information technology is

high while that of failed firms is low For the risky industries (construction and manufacturing)

these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many

failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)

firms Therefore the AUC will be increasesd This situation is reversed for the countries with a

negative effect on AUC (Austria and Slovenia) The samples of these countries include a high

percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC

Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher

classification performance (AUC = 0749) than the benchmark model This result gives only very

weak support for H7 (country of origin effect) However for each country the resultant AUC is

nearly identical to that given by the benchmark model This result was expected due to the

negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7

This result implies that country risk hasdoes not have noany effect on the boundary between

bankrupt and non-bankrupt firms

Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional

variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to

39

the benchmark AUC in all test data However the effect on the AUCs largely varies and is either

negative or positive in different countries The positive effect is large in several countries such

as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative

effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show

that the inclusion of additional variables in the original model will usually increase the AUC but

not in every country

The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with

the USUS firms being unlike the majority of other firms in this study being listed or delisted

companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese

firm samples is associated with very small differences between the medians of the non-failed and

failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable

with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed

firms is very high Prior studies based on Chinese ST -firms have also demonstrated good

predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for

additional research and modeling work regarding unlisted and delisted Chinese firms

63 Country-level data performance of the Zrsquorsquo-Score models

The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform

all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the

different models estimated for each country separately (country-level models) In this table the

ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated

40

from the country data this benchmark will beis clearly outperformed by the resulting MDA

(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and

Sweden) However these results give only weak support for H1 at the country level because the

effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and

2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs

given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed

by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation

method)

Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC

than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3

leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United

StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in

nearly every country supporting H4 This improvement is significant however only in the United

StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark

model but for which it is only 0710 for the benchmark model It This improvement is also notable

in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age

category dummies) gives higher AUCs than the benchmark model for several countries but none

of the improvements are significant The positive effect is particularly strong especially in

Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only

weakly supports H5 because these negative effects are relatively small

41

Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects

on AUCs when compared with the benchmark However the only significant effect is the negative

effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the

industry dummies However there are notable positive effects on the AUC in for example

France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak

support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in

AUC compared with the benchmark model in Colombia Iceland and the Unites States It also

leads and to notable improvements in AUC at least in Belgium Denmark Estonia France

Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are

found only in Austria and Slovenia Thus as a conclusion we can concludestate that the

classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can

remarkably increases in most countries when different effects are taken into account by

additional variables This increase is found in most European countries and in Colombia and the

United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-

modelLR model and it can only be slightly improved only slightly by additional variables

(Table 6 here)

7 Summary of the study and a suggested extension

The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model

originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is

assessed using a very large international data set We test how the original version of the Zrsquorsquo-

42

Score model performs in different countries and how re-estimations using another statistical

method and different additional variables affect the classification performance when the data

are very heterogeneous For this kind of testing seven research hypotheses on classification

performance are extractedformulated These research hypotheses are tested for all data and

separately for country data (country-level analysis) The estimation data are from 31 countries

and the results are validated for 34 countries The countries are mainly from Europe but three

non-European countries are included (China Colombia and the United StatesUS) The statuses

used in the classification are mainly bankruptcyactive but receivership firms are also considered

to have failed In the Chinese data ST (special treatment) and delisted firms are also separately

analyzed as failed firms

The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very

satisfactorily in an international context The effects of the four financial ratios on performance

are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small

contribution in re-estimation The original model performs very well in several countries such as

Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only

marginally improved the classification performance thus weakly supporting the obsolescence

hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust

across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion

holds for the re-estimation of the model using LRA because the performance results are very

similar with to those of MDA (H2) The use of additional variables in the model generally

improvesd the classification accuracy of the original model but the results for countries are

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

20

of the Zrsquorsquo-Score model is improved when the size category of the firm is explicitly taken into

account Thus we present the fourth hypothesis as follows

H4 The modelrsquos prediction accuracy of the modelis higher whenith the effect of size is

includede is higher than that of the model without this effect

H5 Age of the firm International insolvency statistics generally show that bankruptcy risk is a

function of the age of the firm Very young firms typically show a very high risk The original Zrsquorsquo-

Score model does not explicitly take age into account age However Altman (1983) noted that

the age of a firm is implicitly considered in the Retained EarningsTotal Assets ratio (X2) which

was regarded as a new ratio in the bankruptcy prediction context A relatively young firm will

probably show a low ratio because it has not had time to build up cumulative profits Thus a

young firm is to some degree discriminated against in the model and its likelihood of being

classified as bankrupt is relatively higher than that of an older firm The incidence of failure is

much higher in the early years of a firm AlthoughDespite that the age of the firm in this way is

implicitly taken into account in X2 we expect that an explicit consideration of the age will improve

the classification accuracy bydue to controlling for the age factor Therefore the fifth hypothesis

(H5) proposes that the performance of the uniform benchmark model based on the original four

financial variables of the Zrsquorsquo-Score model is increasesd when the age of the firm is explicitly taken

into account The fifth hypothesis is can be presented as follows

H5 The modelrsquos prediction accuracy of the model is higher when with the effect of firm

age is higher than that of the model without this effectincluded

21

H6 Industry of the firm The original Zrsquo-Score model was estimated only for manufacturing firms

Altman (1983) stated that it would be ideal to develop a bankruptcy prediction model utilizing a

homogenous group of bankrupt firms If we are interested in a particular industry grouping we

should gather data from bankrupt and non-bankrupt firms in these groupings Previous studies

show that financial distress analysis is influenced by the industry effect (Smith and Liou 2007)

Firms in different industries tend to report different levels of the same financial ratios which may

have an effect on the boundary between bankrupt and non-bankrupt firms This industry effect

may be present in the Zrsquo-Score model especially due to the SalesTotal Assets ratio (X5) This ratio

which showed the least significance on a univariate basis while making but it had made a very

significant contribution to the discriminant power of the multivariate model Altman (1983)

recognized the potential industry effect due to a the wide variation among industries in asset

turnover and specified the Zrsquorsquo-Score model without X5 However also the Zrsquorsquo-Score model was

also estimated using the original sample of manufacturing firms In our analysis the uniform

benchmark model based on the original four financial variables of the Zrsquorsquo-Score model is

estimated for a statistical sample representing different industries Therefore the sixth

hypothesis (H6) assumes that an explicit consideration of industry will improve the classification

accuracy of this benchmark model H6It can be expressed in the following form

H6 The modelrsquos prediction accuracy of the modelis higher whenith the effect of industry is

higher than that of the model without this effectincluded

H7 Country of origin The original Zrsquorsquo-Score model has been estimated only for USUS firms It

can be expected that the international applicability of the model to other countries is affected by

22

country-specific differences The eEconomic environment legislation culture financial markets

and accounting practices in a country may affect the financial behavior of firms and the boundary

between bankrupt and non-bankrupt firms These factors may potentially weaken the

classification performance of the model in for other countries outside other than that e one fore

country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the

seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm

into account will improve the classification accuracy of the benchmark model In our empirical

study the country effect will beis assessed by including a variable of for country risk Therefore

the seventh hypothesis is presented as follows

H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country

risk is higher than that of the model without this effectincluded

5 Empirical data and statistical methods

51 Sample of firms

The principal data of for this study are were extracted from the ORBIS databases of Bureau Van

Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the

moment of sampling contained administrative information on more than 50 million European

firms However income statement and balance sheet information was available for

approximately 8 million companies More than 99 of the companies covered in this database

are private companies from different various industries justifying the use of the Zrsquorsquo-Score model

instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all

industrial groupings and for both private and public entities (Altman 1983 124 Altman and

Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope

23

of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all

industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly

used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that

international comparability may be a problem when administrative firm-level data are

internationally pooled For administrative data Although the definition of variables is usually less

harmonized for administrative data tThis is less of a problem in the ORBIS database because of

the common international format of balance sheets For example although some discrepancies

in profitloss statements may arise because of differences in fiscal systems across countries

balance sheet variables largely adhere to international standards

AThere are a number of factors that influence the international applicability of bankruptcy

prediction models These factors include accounting legislation and practice creditor rights and

investor protection judicial efficiency corporate governance bankruptcy protection and

insolvency management and firm risk-taking These factors strongly differ between European

and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-

Score model outside Europe First it is especiallyparticularly important to include the USUS as

because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It

is include d in our study as well because it has the largest market capitalization in the world

Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it

applicable to emerging market companies Therefore we include firms from China and Colombia

which represent two very culturally and institutionally very dissimilar emerging market

countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt

24

firms ie the limit we set for European countries) from ORBIS World were not

available Thus the results are also estimated and tested for three non-European countries (the

United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-

Score modelrsquos performance The samples of firms from these countries are were extracted from

ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms

from around the world

SFor statistical sampling several requirements are set for the statistical sampling of the empirical

data First we require that the firm to be selected be an industrial (non-financial) company

Second its owners must have limited liability (so partnerships and sole proprietors are left out

of the study) Third we set a minimum requirement for the size of the firm Because the financial

ratios of very small firms are generally too unstable for a failure prediction model these firms

are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have

exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we

include in our estimation sample firms from all European countries and 3 pre-selected non-

European countries where the number of failed firms is more greater than 60 If the number of

failed firms for any European country is less than 60 the firms from this country are only included

only in the test sample For qualifying European countries the firms are randomly classified in

the estimation and test samples so that the number of firms is approximately equal in both

samples Thus our estimation data include firms from 28 European and 3 non-European

countries Fifth all failed firms that fulfill the above requirements are included in our samples

However if the number of non-failed firms in a country is very high a sample is randomly

25

selected from that country Finally the time span of fiscal years potentially available for this study

ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in

the database are from a financial period within 2007 and 2010 earlier years are also excluded

for non-failed firms for comparability All qualifying observations of non-failed firms from the

years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses

of failed firms to the last most recent financial statements available before failure The four

independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize

outliers

Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test

data by country The estimation sample includes data from 2602563 non-failed and 38215

failed firms from 28 European and 3 non-European countries The test sample is slightly larger

because it includes data from 31 European and 3 non-European countries From For the country

of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt

firms The available US data that were available consist only of listed (and delisted) firms From

China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are

included in the estimation sample although there are only 32 such firms altogether7 The Chinese

datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status

are separately analyzed separately only in for the test data8 ST firms are listed firms suffering

6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor

26

from serious financial difficulties Excluding the special US data and the two non-private Chinese

datasets 994 of the observations in the data are private firms

(Table 1 here)

52 Status of failed firms

ORBIS has five classes for potentially active firms (active default of payment receivership

dormant and branch) and seven classes for inactive firms that no longer carry out business

activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation

branch and no precision) From Among these classes only active is selected to represent non-

distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by

considering (with exceptions described below) a firm failed if its status in ORBIS is stated as

bankruptcy However because of the small number of bankrupt firms in some countries we also

consider receivership (active) firms as failed even if they are active These firms generally suffer

from serious financial distress However firms in liquidation are generally not included in the

sample of failed firms Firms in liquidation may depending on the country contain firms that

have ceased activities due to reasons other than failure (mergers discontinuing the operations

of a daughter company or of a foreign branch etc) Therefore for most countries we select only

firms that are coded as being bankrupt or under receivership However there are a number of

special cases where failed firms are coded under a different status heading These countries or

samples are the following

Country Status categories Bulgaria In liquidation Bankruptcy

27

Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the

study (for example Switzerland) If a country had only a very small number of failed firms it was

dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries

including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status

classes (including the bankruptcy category) are not completely homogenous within European

countries due to different legislations although there are obvious similarities in insolvency acts

(Philippe et al 2002) China is a special case because it includes samples with three different

criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are

two different samples (liquidation and receivership)

53 Statistical methods

In this study seven research hypotheses are drawn for statistical testing The statistical analysis

will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The

classification performance of the original model is assessed by the AUC (Area Under Curve)

measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close

connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random

28

model and 05 for a model with an average classification performance In SAS software is used

for all statistical analysess SAS software is used

The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is

tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical

method (multiple discriminant analysis or MDA) The problem is that the estimation sample

includes different numbers of failed and non-failed firms from 31 countries In the original Z-

Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from

the USUS Following the characteristics of these data we weight the firms so that the weights

for the failed and non-failed firms are equally In so doingthis way the non-proportional

sampling in from different countries does will not affect the re-estimated model The number of

firms from different countries however varies significantly leading to greater weights for larger

countries To avoid this problem the observations are also weighted so that each country has an

equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using

these weighted data and the resulting AUC is compared with that based on the original model

The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score

model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is

based on less- restrictive statistical assumptions than MDA In this estimation the dependent

variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA

does not require that independent variables be multivariate normal or that groups have equal

covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA

29

creates a score (logit) score L for every firm It is assumed that the independent variables are

linearly related to L This score or logit is used to determine the conditional probability of failure

as follows

= (eq 4)

where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the

original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing

the statistical significance of the difference between AUCs for this LR model and for the re-

estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it

is used as a benchmark for further statistical AUC comparisons because LR is applied as the

principal method in the testing of the remaining research hypotheses

The third hypothesis (H3) is associated with the performance effect of taking account of the

bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on

the following logit

(eq 5)

where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model

bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and

D1 = 1 when year = 2008 0 otherwise

D2 = 1 when year = 2009 0 otherwise

D3 = 1 when year = 2010 0 otherwise

)|1( XYp )( 441101

1

1

1XbXbbL ee

3

1

4

10

jjji

ii DcXbbL

30

The dummy variables do not directly refer to the bankruptcy year which that is not given in the

data basedatabase but rather to the last available year before bankruptcy For failed firms there

is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is

the base category If the AUC of this extended LR model statistically significantly exceeds the AUC

of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3

Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third

hypothesis above However for each hypothesis appropriate variables are used instead of the

year dummies Hypothesis H4 addresses the performance effect of taking size into account the

size and it will beis tested using by adding two additional variables measuring firm size in to the

LR -model In this LR model size is measured by the natural logarithm of total assets and its

squared form In this way the effect of logarithmic size can be reflected by in a function following

the second-order parabola Hypothesis H5 predicts that the classification performance improves

when the age of the firm is explicitly taken into account When testing this hypothesis the

category 6ndash14 years category is used as the base category and two dummy variables are

incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis

H6 looks at whether is associated with the classification performance is affected bythe effect of

the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt

is tested here using dummy variables for seven industries (D1 restaurants and hotels D2

construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and

water production D7 information technology) with all other industries acting as the base

category

31

Hypothesis H7 predicts that the explicit consideration of the country of origin will improves

classification performance This hypothesis is tested by using country risk measures instead ofnot

using dummy variables for countries but using country risk measures instead The country risk of

each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the

annual closing of accounts The rating is numerically recoded such that the best rating AAA

equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating

D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial

ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-

modelLR models with the original four financial ratios and the additional variables specified in

the hypotheses are estimated for all data In addition an LR model including all additional

variables is estimated for all data to assess the simultaneous effect of all variables Finally six of

the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not

included in this country- level testing hypothesis H7 is not tested because the additional variable

(country risk) is constant within the country

6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models

Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score

model for all data The variation in the ratios is significant as shown by the standard deviation

and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-

failed firms are close to each other indicating a symmetry of distributions However this is not

32

the case for the failed firms For the faileddistressed firms the median exceeds the mean for

these three ratios indicating negatively skewed distributions For X4 (BVETD) the means

significantly exceed the median for both failed and non-failed firms indicating a positively

skewed distribution For each of the four variables both the mean and the median are higher for

non-failed firms than for failed firms which is consistent with the expectations The difference

between the means of the non-failed and failed firms is larger in the original USUS data than in

our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is

approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of

EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may

indicate lower classification accuracy than in the original sample

(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates

(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the

large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model

Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same

statistical method or MDA The coefficients here are negative because the models are estimated

using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight

than in the original USUS data while the weights of WCTA and BVETD have proportionally

decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the

logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR

model These coefficients are directly comparable with those of the MDA model which isas

expected for the this exceptionally large sample The differences in the coefficients of the original

33

four variables among the eight LR-modelLR models (Models 1-8) are small For each model the

coefficient of BVETD is very close to zero This indicatinges that the original four variables and

the additional variables are quite independent of each other

(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-

modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate

that after 2007 (the base category) this risk of failure significantly decreased year by year The

base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due

to as a result of the global financial crisis The crisis played a significant role in the failure of key

businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The

effects were especially felt in Europe The coefficients of Model 4 for the size variables show that

the contribution of size on to the logit (risk measure) is atreaches its maximum value when

logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model

5 confirms the riskiness of young firms because the risk of failure is very high for newly founded

firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The

coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky

industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is

statistically significant (because of the large sample) but negative and very close to zero Finally

the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7

62 All data performance of the Zrsquorsquo-Score models

34

Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1

refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of

all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average

accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)

Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its

performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The

lower part of the table shows the AUCs for the countries included only in the test data The

performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)

samples It is also low and for the liquidation firms in the United Kingdom

The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5

presents the differences of these medians between the non-failed and failed firms by country

This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of

medians This correlation which is high for each financial ratio showing that the effects of the

ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large

except for EBITTA which This implies with the exceptionally high AUC that the ST firms

systematically differ from the non-ST firms although the differences are not extremely large The

differences between the medians are very large in Poland for each ratio justifying the high AUC

They are also large and in Finland and in the Czech Republic where the difference in EBITTA is

average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in

all four ratios are below the average a result thatwhich is obviously associated with a low AUC

35

In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even

negative In Iceland and Ireland the differences only in EBITTA are exceptionally small

(Table 4 here)

(Table 5 here)

Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by

MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)

supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is

at approximately the average level The re-estimation of the coefficients has led to improved

classification accuracy in a number of countries especially in Bosnia China (ST) Norway and

Greece However it has impaired the classification accuracy in the UK and China (delisted)

Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-

modelLR model) showing the effect of the estimation method For the benchmark model the

AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between

among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR

model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country

This result was expected because the coefficients of the models are directly comparable The

similar results for the models may also indicate that the independent variables conform to

multinormality This last latter result confirms what most researchers in the field of default

classification models have concluded that the accuracy levels of MDA and logistical regression

36

models are extremely similar despite any statistical violations such as normality between MDA

and logistical regression models

Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)

than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects

are not positive for all countries The effects are positive for example for Russia Estonia

Germany Ireland and Latvia but these effects are not statistically significant There are

statistically significant negative effects for China (ST) and Serbia There are notable negative

effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are

due to the exceptional annual distributions of failed firms in these countries For the countries

with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the

countries with positive effects it is only a few percent For each group non-failed firms are quite

equally distributed over years When the coefficient of D3 is very low (-0666) it strongly

decreases the risk estimates of most failed firms in the former countries but of only a few failed

firms in the latter countries This which leads to the observed effects H3 is supported by

the level of the whole sample However in some country samples (at the level of individual

levelies) H3 is not supported

Model 4 (LR-modelLR model with size variables) performs better than the benchmark model

which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads

and to significant improvements in the AUC for eg China (delisted) and Austria For China

(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC

37

also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs

in a few countries but thise decrease is not significant Model 4 is based on the four original

variables and the size effect following a second-order parabola This kind of size effect is very

small for micro firms but increases when approaching its maximum value in middle-sized firms

The countries with a positive change in AUC typically have data where in which the percent of

failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized

failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In

contrast the countries with a negative change in AUC have data where in which the percentage

of failed micro firms is exceptionally high (70-80)

Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly

the same AUC as the benchmark model (AUC = 0748) However the difference is positive and

statistically significant giving at least marginal support to H5 (age effect) For nearly all countries

the effect of the age on AUC is small For Austria however thise effect is however positive and

significant For Austria the percentage of non-failed young firms (less than 6 years) among non-

failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed

firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong

positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only

a few non-failed firms which This obviously leads to an improvement in AUC

Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC

supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its

38

AUC is notably higher than the benchmark AUC only in only a few countries such asfor example

France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg

restaurants and hotels and the information technology industryies but it has a positive effect

for on the construction and manufacturing industries For the countries with a positive effect on

AUC the percent of non-failed firms in restaurants and hotels and information technology is

high while that of failed firms is low For the risky industries (construction and manufacturing)

these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many

failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)

firms Therefore the AUC will be increasesd This situation is reversed for the countries with a

negative effect on AUC (Austria and Slovenia) The samples of these countries include a high

percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC

Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher

classification performance (AUC = 0749) than the benchmark model This result gives only very

weak support for H7 (country of origin effect) However for each country the resultant AUC is

nearly identical to that given by the benchmark model This result was expected due to the

negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7

This result implies that country risk hasdoes not have noany effect on the boundary between

bankrupt and non-bankrupt firms

Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional

variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to

39

the benchmark AUC in all test data However the effect on the AUCs largely varies and is either

negative or positive in different countries The positive effect is large in several countries such

as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative

effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show

that the inclusion of additional variables in the original model will usually increase the AUC but

not in every country

The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with

the USUS firms being unlike the majority of other firms in this study being listed or delisted

companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese

firm samples is associated with very small differences between the medians of the non-failed and

failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable

with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed

firms is very high Prior studies based on Chinese ST -firms have also demonstrated good

predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for

additional research and modeling work regarding unlisted and delisted Chinese firms

63 Country-level data performance of the Zrsquorsquo-Score models

The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform

all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the

different models estimated for each country separately (country-level models) In this table the

ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated

40

from the country data this benchmark will beis clearly outperformed by the resulting MDA

(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and

Sweden) However these results give only weak support for H1 at the country level because the

effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and

2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs

given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed

by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation

method)

Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC

than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3

leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United

StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in

nearly every country supporting H4 This improvement is significant however only in the United

StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark

model but for which it is only 0710 for the benchmark model It This improvement is also notable

in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age

category dummies) gives higher AUCs than the benchmark model for several countries but none

of the improvements are significant The positive effect is particularly strong especially in

Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only

weakly supports H5 because these negative effects are relatively small

41

Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects

on AUCs when compared with the benchmark However the only significant effect is the negative

effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the

industry dummies However there are notable positive effects on the AUC in for example

France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak

support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in

AUC compared with the benchmark model in Colombia Iceland and the Unites States It also

leads and to notable improvements in AUC at least in Belgium Denmark Estonia France

Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are

found only in Austria and Slovenia Thus as a conclusion we can concludestate that the

classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can

remarkably increases in most countries when different effects are taken into account by

additional variables This increase is found in most European countries and in Colombia and the

United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-

modelLR model and it can only be slightly improved only slightly by additional variables

(Table 6 here)

7 Summary of the study and a suggested extension

The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model

originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is

assessed using a very large international data set We test how the original version of the Zrsquorsquo-

42

Score model performs in different countries and how re-estimations using another statistical

method and different additional variables affect the classification performance when the data

are very heterogeneous For this kind of testing seven research hypotheses on classification

performance are extractedformulated These research hypotheses are tested for all data and

separately for country data (country-level analysis) The estimation data are from 31 countries

and the results are validated for 34 countries The countries are mainly from Europe but three

non-European countries are included (China Colombia and the United StatesUS) The statuses

used in the classification are mainly bankruptcyactive but receivership firms are also considered

to have failed In the Chinese data ST (special treatment) and delisted firms are also separately

analyzed as failed firms

The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very

satisfactorily in an international context The effects of the four financial ratios on performance

are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small

contribution in re-estimation The original model performs very well in several countries such as

Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only

marginally improved the classification performance thus weakly supporting the obsolescence

hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust

across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion

holds for the re-estimation of the model using LRA because the performance results are very

similar with to those of MDA (H2) The use of additional variables in the model generally

improvesd the classification accuracy of the original model but the results for countries are

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

21

H6 Industry of the firm The original Zrsquo-Score model was estimated only for manufacturing firms

Altman (1983) stated that it would be ideal to develop a bankruptcy prediction model utilizing a

homogenous group of bankrupt firms If we are interested in a particular industry grouping we

should gather data from bankrupt and non-bankrupt firms in these groupings Previous studies

show that financial distress analysis is influenced by the industry effect (Smith and Liou 2007)

Firms in different industries tend to report different levels of the same financial ratios which may

have an effect on the boundary between bankrupt and non-bankrupt firms This industry effect

may be present in the Zrsquo-Score model especially due to the SalesTotal Assets ratio (X5) This ratio

which showed the least significance on a univariate basis while making but it had made a very

significant contribution to the discriminant power of the multivariate model Altman (1983)

recognized the potential industry effect due to a the wide variation among industries in asset

turnover and specified the Zrsquorsquo-Score model without X5 However also the Zrsquorsquo-Score model was

also estimated using the original sample of manufacturing firms In our analysis the uniform

benchmark model based on the original four financial variables of the Zrsquorsquo-Score model is

estimated for a statistical sample representing different industries Therefore the sixth

hypothesis (H6) assumes that an explicit consideration of industry will improve the classification

accuracy of this benchmark model H6It can be expressed in the following form

H6 The modelrsquos prediction accuracy of the modelis higher whenith the effect of industry is

higher than that of the model without this effectincluded

H7 Country of origin The original Zrsquorsquo-Score model has been estimated only for USUS firms It

can be expected that the international applicability of the model to other countries is affected by

22

country-specific differences The eEconomic environment legislation culture financial markets

and accounting practices in a country may affect the financial behavior of firms and the boundary

between bankrupt and non-bankrupt firms These factors may potentially weaken the

classification performance of the model in for other countries outside other than that e one fore

country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the

seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm

into account will improve the classification accuracy of the benchmark model In our empirical

study the country effect will beis assessed by including a variable of for country risk Therefore

the seventh hypothesis is presented as follows

H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country

risk is higher than that of the model without this effectincluded

5 Empirical data and statistical methods

51 Sample of firms

The principal data of for this study are were extracted from the ORBIS databases of Bureau Van

Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the

moment of sampling contained administrative information on more than 50 million European

firms However income statement and balance sheet information was available for

approximately 8 million companies More than 99 of the companies covered in this database

are private companies from different various industries justifying the use of the Zrsquorsquo-Score model

instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all

industrial groupings and for both private and public entities (Altman 1983 124 Altman and

Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope

23

of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all

industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly

used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that

international comparability may be a problem when administrative firm-level data are

internationally pooled For administrative data Although the definition of variables is usually less

harmonized for administrative data tThis is less of a problem in the ORBIS database because of

the common international format of balance sheets For example although some discrepancies

in profitloss statements may arise because of differences in fiscal systems across countries

balance sheet variables largely adhere to international standards

AThere are a number of factors that influence the international applicability of bankruptcy

prediction models These factors include accounting legislation and practice creditor rights and

investor protection judicial efficiency corporate governance bankruptcy protection and

insolvency management and firm risk-taking These factors strongly differ between European

and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-

Score model outside Europe First it is especiallyparticularly important to include the USUS as

because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It

is include d in our study as well because it has the largest market capitalization in the world

Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it

applicable to emerging market companies Therefore we include firms from China and Colombia

which represent two very culturally and institutionally very dissimilar emerging market

countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt

24

firms ie the limit we set for European countries) from ORBIS World were not

available Thus the results are also estimated and tested for three non-European countries (the

United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-

Score modelrsquos performance The samples of firms from these countries are were extracted from

ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms

from around the world

SFor statistical sampling several requirements are set for the statistical sampling of the empirical

data First we require that the firm to be selected be an industrial (non-financial) company

Second its owners must have limited liability (so partnerships and sole proprietors are left out

of the study) Third we set a minimum requirement for the size of the firm Because the financial

ratios of very small firms are generally too unstable for a failure prediction model these firms

are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have

exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we

include in our estimation sample firms from all European countries and 3 pre-selected non-

European countries where the number of failed firms is more greater than 60 If the number of

failed firms for any European country is less than 60 the firms from this country are only included

only in the test sample For qualifying European countries the firms are randomly classified in

the estimation and test samples so that the number of firms is approximately equal in both

samples Thus our estimation data include firms from 28 European and 3 non-European

countries Fifth all failed firms that fulfill the above requirements are included in our samples

However if the number of non-failed firms in a country is very high a sample is randomly

25

selected from that country Finally the time span of fiscal years potentially available for this study

ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in

the database are from a financial period within 2007 and 2010 earlier years are also excluded

for non-failed firms for comparability All qualifying observations of non-failed firms from the

years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses

of failed firms to the last most recent financial statements available before failure The four

independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize

outliers

Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test

data by country The estimation sample includes data from 2602563 non-failed and 38215

failed firms from 28 European and 3 non-European countries The test sample is slightly larger

because it includes data from 31 European and 3 non-European countries From For the country

of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt

firms The available US data that were available consist only of listed (and delisted) firms From

China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are

included in the estimation sample although there are only 32 such firms altogether7 The Chinese

datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status

are separately analyzed separately only in for the test data8 ST firms are listed firms suffering

6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor

26

from serious financial difficulties Excluding the special US data and the two non-private Chinese

datasets 994 of the observations in the data are private firms

(Table 1 here)

52 Status of failed firms

ORBIS has five classes for potentially active firms (active default of payment receivership

dormant and branch) and seven classes for inactive firms that no longer carry out business

activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation

branch and no precision) From Among these classes only active is selected to represent non-

distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by

considering (with exceptions described below) a firm failed if its status in ORBIS is stated as

bankruptcy However because of the small number of bankrupt firms in some countries we also

consider receivership (active) firms as failed even if they are active These firms generally suffer

from serious financial distress However firms in liquidation are generally not included in the

sample of failed firms Firms in liquidation may depending on the country contain firms that

have ceased activities due to reasons other than failure (mergers discontinuing the operations

of a daughter company or of a foreign branch etc) Therefore for most countries we select only

firms that are coded as being bankrupt or under receivership However there are a number of

special cases where failed firms are coded under a different status heading These countries or

samples are the following

Country Status categories Bulgaria In liquidation Bankruptcy

27

Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the

study (for example Switzerland) If a country had only a very small number of failed firms it was

dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries

including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status

classes (including the bankruptcy category) are not completely homogenous within European

countries due to different legislations although there are obvious similarities in insolvency acts

(Philippe et al 2002) China is a special case because it includes samples with three different

criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are

two different samples (liquidation and receivership)

53 Statistical methods

In this study seven research hypotheses are drawn for statistical testing The statistical analysis

will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The

classification performance of the original model is assessed by the AUC (Area Under Curve)

measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close

connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random

28

model and 05 for a model with an average classification performance In SAS software is used

for all statistical analysess SAS software is used

The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is

tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical

method (multiple discriminant analysis or MDA) The problem is that the estimation sample

includes different numbers of failed and non-failed firms from 31 countries In the original Z-

Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from

the USUS Following the characteristics of these data we weight the firms so that the weights

for the failed and non-failed firms are equally In so doingthis way the non-proportional

sampling in from different countries does will not affect the re-estimated model The number of

firms from different countries however varies significantly leading to greater weights for larger

countries To avoid this problem the observations are also weighted so that each country has an

equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using

these weighted data and the resulting AUC is compared with that based on the original model

The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score

model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is

based on less- restrictive statistical assumptions than MDA In this estimation the dependent

variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA

does not require that independent variables be multivariate normal or that groups have equal

covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA

29

creates a score (logit) score L for every firm It is assumed that the independent variables are

linearly related to L This score or logit is used to determine the conditional probability of failure

as follows

= (eq 4)

where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the

original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing

the statistical significance of the difference between AUCs for this LR model and for the re-

estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it

is used as a benchmark for further statistical AUC comparisons because LR is applied as the

principal method in the testing of the remaining research hypotheses

The third hypothesis (H3) is associated with the performance effect of taking account of the

bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on

the following logit

(eq 5)

where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model

bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and

D1 = 1 when year = 2008 0 otherwise

D2 = 1 when year = 2009 0 otherwise

D3 = 1 when year = 2010 0 otherwise

)|1( XYp )( 441101

1

1

1XbXbbL ee

3

1

4

10

jjji

ii DcXbbL

30

The dummy variables do not directly refer to the bankruptcy year which that is not given in the

data basedatabase but rather to the last available year before bankruptcy For failed firms there

is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is

the base category If the AUC of this extended LR model statistically significantly exceeds the AUC

of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3

Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third

hypothesis above However for each hypothesis appropriate variables are used instead of the

year dummies Hypothesis H4 addresses the performance effect of taking size into account the

size and it will beis tested using by adding two additional variables measuring firm size in to the

LR -model In this LR model size is measured by the natural logarithm of total assets and its

squared form In this way the effect of logarithmic size can be reflected by in a function following

the second-order parabola Hypothesis H5 predicts that the classification performance improves

when the age of the firm is explicitly taken into account When testing this hypothesis the

category 6ndash14 years category is used as the base category and two dummy variables are

incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis

H6 looks at whether is associated with the classification performance is affected bythe effect of

the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt

is tested here using dummy variables for seven industries (D1 restaurants and hotels D2

construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and

water production D7 information technology) with all other industries acting as the base

category

31

Hypothesis H7 predicts that the explicit consideration of the country of origin will improves

classification performance This hypothesis is tested by using country risk measures instead ofnot

using dummy variables for countries but using country risk measures instead The country risk of

each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the

annual closing of accounts The rating is numerically recoded such that the best rating AAA

equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating

D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial

ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-

modelLR models with the original four financial ratios and the additional variables specified in

the hypotheses are estimated for all data In addition an LR model including all additional

variables is estimated for all data to assess the simultaneous effect of all variables Finally six of

the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not

included in this country- level testing hypothesis H7 is not tested because the additional variable

(country risk) is constant within the country

6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models

Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score

model for all data The variation in the ratios is significant as shown by the standard deviation

and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-

failed firms are close to each other indicating a symmetry of distributions However this is not

32

the case for the failed firms For the faileddistressed firms the median exceeds the mean for

these three ratios indicating negatively skewed distributions For X4 (BVETD) the means

significantly exceed the median for both failed and non-failed firms indicating a positively

skewed distribution For each of the four variables both the mean and the median are higher for

non-failed firms than for failed firms which is consistent with the expectations The difference

between the means of the non-failed and failed firms is larger in the original USUS data than in

our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is

approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of

EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may

indicate lower classification accuracy than in the original sample

(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates

(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the

large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model

Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same

statistical method or MDA The coefficients here are negative because the models are estimated

using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight

than in the original USUS data while the weights of WCTA and BVETD have proportionally

decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the

logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR

model These coefficients are directly comparable with those of the MDA model which isas

expected for the this exceptionally large sample The differences in the coefficients of the original

33

four variables among the eight LR-modelLR models (Models 1-8) are small For each model the

coefficient of BVETD is very close to zero This indicatinges that the original four variables and

the additional variables are quite independent of each other

(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-

modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate

that after 2007 (the base category) this risk of failure significantly decreased year by year The

base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due

to as a result of the global financial crisis The crisis played a significant role in the failure of key

businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The

effects were especially felt in Europe The coefficients of Model 4 for the size variables show that

the contribution of size on to the logit (risk measure) is atreaches its maximum value when

logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model

5 confirms the riskiness of young firms because the risk of failure is very high for newly founded

firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The

coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky

industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is

statistically significant (because of the large sample) but negative and very close to zero Finally

the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7

62 All data performance of the Zrsquorsquo-Score models

34

Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1

refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of

all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average

accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)

Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its

performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The

lower part of the table shows the AUCs for the countries included only in the test data The

performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)

samples It is also low and for the liquidation firms in the United Kingdom

The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5

presents the differences of these medians between the non-failed and failed firms by country

This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of

medians This correlation which is high for each financial ratio showing that the effects of the

ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large

except for EBITTA which This implies with the exceptionally high AUC that the ST firms

systematically differ from the non-ST firms although the differences are not extremely large The

differences between the medians are very large in Poland for each ratio justifying the high AUC

They are also large and in Finland and in the Czech Republic where the difference in EBITTA is

average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in

all four ratios are below the average a result thatwhich is obviously associated with a low AUC

35

In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even

negative In Iceland and Ireland the differences only in EBITTA are exceptionally small

(Table 4 here)

(Table 5 here)

Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by

MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)

supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is

at approximately the average level The re-estimation of the coefficients has led to improved

classification accuracy in a number of countries especially in Bosnia China (ST) Norway and

Greece However it has impaired the classification accuracy in the UK and China (delisted)

Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-

modelLR model) showing the effect of the estimation method For the benchmark model the

AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between

among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR

model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country

This result was expected because the coefficients of the models are directly comparable The

similar results for the models may also indicate that the independent variables conform to

multinormality This last latter result confirms what most researchers in the field of default

classification models have concluded that the accuracy levels of MDA and logistical regression

36

models are extremely similar despite any statistical violations such as normality between MDA

and logistical regression models

Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)

than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects

are not positive for all countries The effects are positive for example for Russia Estonia

Germany Ireland and Latvia but these effects are not statistically significant There are

statistically significant negative effects for China (ST) and Serbia There are notable negative

effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are

due to the exceptional annual distributions of failed firms in these countries For the countries

with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the

countries with positive effects it is only a few percent For each group non-failed firms are quite

equally distributed over years When the coefficient of D3 is very low (-0666) it strongly

decreases the risk estimates of most failed firms in the former countries but of only a few failed

firms in the latter countries This which leads to the observed effects H3 is supported by

the level of the whole sample However in some country samples (at the level of individual

levelies) H3 is not supported

Model 4 (LR-modelLR model with size variables) performs better than the benchmark model

which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads

and to significant improvements in the AUC for eg China (delisted) and Austria For China

(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC

37

also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs

in a few countries but thise decrease is not significant Model 4 is based on the four original

variables and the size effect following a second-order parabola This kind of size effect is very

small for micro firms but increases when approaching its maximum value in middle-sized firms

The countries with a positive change in AUC typically have data where in which the percent of

failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized

failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In

contrast the countries with a negative change in AUC have data where in which the percentage

of failed micro firms is exceptionally high (70-80)

Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly

the same AUC as the benchmark model (AUC = 0748) However the difference is positive and

statistically significant giving at least marginal support to H5 (age effect) For nearly all countries

the effect of the age on AUC is small For Austria however thise effect is however positive and

significant For Austria the percentage of non-failed young firms (less than 6 years) among non-

failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed

firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong

positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only

a few non-failed firms which This obviously leads to an improvement in AUC

Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC

supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its

38

AUC is notably higher than the benchmark AUC only in only a few countries such asfor example

France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg

restaurants and hotels and the information technology industryies but it has a positive effect

for on the construction and manufacturing industries For the countries with a positive effect on

AUC the percent of non-failed firms in restaurants and hotels and information technology is

high while that of failed firms is low For the risky industries (construction and manufacturing)

these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many

failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)

firms Therefore the AUC will be increasesd This situation is reversed for the countries with a

negative effect on AUC (Austria and Slovenia) The samples of these countries include a high

percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC

Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher

classification performance (AUC = 0749) than the benchmark model This result gives only very

weak support for H7 (country of origin effect) However for each country the resultant AUC is

nearly identical to that given by the benchmark model This result was expected due to the

negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7

This result implies that country risk hasdoes not have noany effect on the boundary between

bankrupt and non-bankrupt firms

Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional

variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to

39

the benchmark AUC in all test data However the effect on the AUCs largely varies and is either

negative or positive in different countries The positive effect is large in several countries such

as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative

effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show

that the inclusion of additional variables in the original model will usually increase the AUC but

not in every country

The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with

the USUS firms being unlike the majority of other firms in this study being listed or delisted

companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese

firm samples is associated with very small differences between the medians of the non-failed and

failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable

with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed

firms is very high Prior studies based on Chinese ST -firms have also demonstrated good

predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for

additional research and modeling work regarding unlisted and delisted Chinese firms

63 Country-level data performance of the Zrsquorsquo-Score models

The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform

all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the

different models estimated for each country separately (country-level models) In this table the

ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated

40

from the country data this benchmark will beis clearly outperformed by the resulting MDA

(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and

Sweden) However these results give only weak support for H1 at the country level because the

effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and

2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs

given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed

by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation

method)

Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC

than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3

leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United

StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in

nearly every country supporting H4 This improvement is significant however only in the United

StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark

model but for which it is only 0710 for the benchmark model It This improvement is also notable

in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age

category dummies) gives higher AUCs than the benchmark model for several countries but none

of the improvements are significant The positive effect is particularly strong especially in

Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only

weakly supports H5 because these negative effects are relatively small

41

Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects

on AUCs when compared with the benchmark However the only significant effect is the negative

effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the

industry dummies However there are notable positive effects on the AUC in for example

France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak

support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in

AUC compared with the benchmark model in Colombia Iceland and the Unites States It also

leads and to notable improvements in AUC at least in Belgium Denmark Estonia France

Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are

found only in Austria and Slovenia Thus as a conclusion we can concludestate that the

classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can

remarkably increases in most countries when different effects are taken into account by

additional variables This increase is found in most European countries and in Colombia and the

United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-

modelLR model and it can only be slightly improved only slightly by additional variables

(Table 6 here)

7 Summary of the study and a suggested extension

The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model

originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is

assessed using a very large international data set We test how the original version of the Zrsquorsquo-

42

Score model performs in different countries and how re-estimations using another statistical

method and different additional variables affect the classification performance when the data

are very heterogeneous For this kind of testing seven research hypotheses on classification

performance are extractedformulated These research hypotheses are tested for all data and

separately for country data (country-level analysis) The estimation data are from 31 countries

and the results are validated for 34 countries The countries are mainly from Europe but three

non-European countries are included (China Colombia and the United StatesUS) The statuses

used in the classification are mainly bankruptcyactive but receivership firms are also considered

to have failed In the Chinese data ST (special treatment) and delisted firms are also separately

analyzed as failed firms

The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very

satisfactorily in an international context The effects of the four financial ratios on performance

are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small

contribution in re-estimation The original model performs very well in several countries such as

Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only

marginally improved the classification performance thus weakly supporting the obsolescence

hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust

across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion

holds for the re-estimation of the model using LRA because the performance results are very

similar with to those of MDA (H2) The use of additional variables in the model generally

improvesd the classification accuracy of the original model but the results for countries are

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

22

country-specific differences The eEconomic environment legislation culture financial markets

and accounting practices in a country may affect the financial behavior of firms and the boundary

between bankrupt and non-bankrupt firms These factors may potentially weaken the

classification performance of the model in for other countries outside other than that e one fore

country in which the model wasis originally estimated (Ooghe and Balcaen 2007) Therefore the

seventh hypothesis (H7) assumes that explicitly taking account of the country of origin of a firm

into account will improve the classification accuracy of the benchmark model In our empirical

study the country effect will beis assessed by including a variable of for country risk Therefore

the seventh hypothesis is presented as follows

H7 The modelrsquos prediction accuracy of the modelis higher whenith the effect of country

risk is higher than that of the model without this effectincluded

5 Empirical data and statistical methods

51 Sample of firms

The principal data of for this study are were extracted from the ORBIS databases of Bureau Van

Dijk (BvD) The main data are from ORBIS Europe which is a commercial database that at the

moment of sampling contained administrative information on more than 50 million European

firms However income statement and balance sheet information was available for

approximately 8 million companies More than 99 of the companies covered in this database

are private companies from different various industries justifying the use of the Zrsquorsquo-Score model

instead of the original Z-Score model The Zrsquorsquo-Score model was originally made robust across all

industrial groupings and for both private and public entities (Altman 1983 124 Altman and

Hotchkiss 2006 267 Altman 2014 13) In this studyBecause we do not want to limit the scope

23

of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all

industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly

used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that

international comparability may be a problem when administrative firm-level data are

internationally pooled For administrative data Although the definition of variables is usually less

harmonized for administrative data tThis is less of a problem in the ORBIS database because of

the common international format of balance sheets For example although some discrepancies

in profitloss statements may arise because of differences in fiscal systems across countries

balance sheet variables largely adhere to international standards

AThere are a number of factors that influence the international applicability of bankruptcy

prediction models These factors include accounting legislation and practice creditor rights and

investor protection judicial efficiency corporate governance bankruptcy protection and

insolvency management and firm risk-taking These factors strongly differ between European

and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-

Score model outside Europe First it is especiallyparticularly important to include the USUS as

because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It

is include d in our study as well because it has the largest market capitalization in the world

Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it

applicable to emerging market companies Therefore we include firms from China and Colombia

which represent two very culturally and institutionally very dissimilar emerging market

countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt

24

firms ie the limit we set for European countries) from ORBIS World were not

available Thus the results are also estimated and tested for three non-European countries (the

United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-

Score modelrsquos performance The samples of firms from these countries are were extracted from

ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms

from around the world

SFor statistical sampling several requirements are set for the statistical sampling of the empirical

data First we require that the firm to be selected be an industrial (non-financial) company

Second its owners must have limited liability (so partnerships and sole proprietors are left out

of the study) Third we set a minimum requirement for the size of the firm Because the financial

ratios of very small firms are generally too unstable for a failure prediction model these firms

are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have

exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we

include in our estimation sample firms from all European countries and 3 pre-selected non-

European countries where the number of failed firms is more greater than 60 If the number of

failed firms for any European country is less than 60 the firms from this country are only included

only in the test sample For qualifying European countries the firms are randomly classified in

the estimation and test samples so that the number of firms is approximately equal in both

samples Thus our estimation data include firms from 28 European and 3 non-European

countries Fifth all failed firms that fulfill the above requirements are included in our samples

However if the number of non-failed firms in a country is very high a sample is randomly

25

selected from that country Finally the time span of fiscal years potentially available for this study

ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in

the database are from a financial period within 2007 and 2010 earlier years are also excluded

for non-failed firms for comparability All qualifying observations of non-failed firms from the

years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses

of failed firms to the last most recent financial statements available before failure The four

independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize

outliers

Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test

data by country The estimation sample includes data from 2602563 non-failed and 38215

failed firms from 28 European and 3 non-European countries The test sample is slightly larger

because it includes data from 31 European and 3 non-European countries From For the country

of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt

firms The available US data that were available consist only of listed (and delisted) firms From

China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are

included in the estimation sample although there are only 32 such firms altogether7 The Chinese

datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status

are separately analyzed separately only in for the test data8 ST firms are listed firms suffering

6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor

26

from serious financial difficulties Excluding the special US data and the two non-private Chinese

datasets 994 of the observations in the data are private firms

(Table 1 here)

52 Status of failed firms

ORBIS has five classes for potentially active firms (active default of payment receivership

dormant and branch) and seven classes for inactive firms that no longer carry out business

activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation

branch and no precision) From Among these classes only active is selected to represent non-

distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by

considering (with exceptions described below) a firm failed if its status in ORBIS is stated as

bankruptcy However because of the small number of bankrupt firms in some countries we also

consider receivership (active) firms as failed even if they are active These firms generally suffer

from serious financial distress However firms in liquidation are generally not included in the

sample of failed firms Firms in liquidation may depending on the country contain firms that

have ceased activities due to reasons other than failure (mergers discontinuing the operations

of a daughter company or of a foreign branch etc) Therefore for most countries we select only

firms that are coded as being bankrupt or under receivership However there are a number of

special cases where failed firms are coded under a different status heading These countries or

samples are the following

Country Status categories Bulgaria In liquidation Bankruptcy

27

Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the

study (for example Switzerland) If a country had only a very small number of failed firms it was

dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries

including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status

classes (including the bankruptcy category) are not completely homogenous within European

countries due to different legislations although there are obvious similarities in insolvency acts

(Philippe et al 2002) China is a special case because it includes samples with three different

criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are

two different samples (liquidation and receivership)

53 Statistical methods

In this study seven research hypotheses are drawn for statistical testing The statistical analysis

will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The

classification performance of the original model is assessed by the AUC (Area Under Curve)

measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close

connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random

28

model and 05 for a model with an average classification performance In SAS software is used

for all statistical analysess SAS software is used

The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is

tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical

method (multiple discriminant analysis or MDA) The problem is that the estimation sample

includes different numbers of failed and non-failed firms from 31 countries In the original Z-

Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from

the USUS Following the characteristics of these data we weight the firms so that the weights

for the failed and non-failed firms are equally In so doingthis way the non-proportional

sampling in from different countries does will not affect the re-estimated model The number of

firms from different countries however varies significantly leading to greater weights for larger

countries To avoid this problem the observations are also weighted so that each country has an

equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using

these weighted data and the resulting AUC is compared with that based on the original model

The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score

model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is

based on less- restrictive statistical assumptions than MDA In this estimation the dependent

variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA

does not require that independent variables be multivariate normal or that groups have equal

covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA

29

creates a score (logit) score L for every firm It is assumed that the independent variables are

linearly related to L This score or logit is used to determine the conditional probability of failure

as follows

= (eq 4)

where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the

original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing

the statistical significance of the difference between AUCs for this LR model and for the re-

estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it

is used as a benchmark for further statistical AUC comparisons because LR is applied as the

principal method in the testing of the remaining research hypotheses

The third hypothesis (H3) is associated with the performance effect of taking account of the

bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on

the following logit

(eq 5)

where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model

bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and

D1 = 1 when year = 2008 0 otherwise

D2 = 1 when year = 2009 0 otherwise

D3 = 1 when year = 2010 0 otherwise

)|1( XYp )( 441101

1

1

1XbXbbL ee

3

1

4

10

jjji

ii DcXbbL

30

The dummy variables do not directly refer to the bankruptcy year which that is not given in the

data basedatabase but rather to the last available year before bankruptcy For failed firms there

is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is

the base category If the AUC of this extended LR model statistically significantly exceeds the AUC

of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3

Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third

hypothesis above However for each hypothesis appropriate variables are used instead of the

year dummies Hypothesis H4 addresses the performance effect of taking size into account the

size and it will beis tested using by adding two additional variables measuring firm size in to the

LR -model In this LR model size is measured by the natural logarithm of total assets and its

squared form In this way the effect of logarithmic size can be reflected by in a function following

the second-order parabola Hypothesis H5 predicts that the classification performance improves

when the age of the firm is explicitly taken into account When testing this hypothesis the

category 6ndash14 years category is used as the base category and two dummy variables are

incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis

H6 looks at whether is associated with the classification performance is affected bythe effect of

the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt

is tested here using dummy variables for seven industries (D1 restaurants and hotels D2

construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and

water production D7 information technology) with all other industries acting as the base

category

31

Hypothesis H7 predicts that the explicit consideration of the country of origin will improves

classification performance This hypothesis is tested by using country risk measures instead ofnot

using dummy variables for countries but using country risk measures instead The country risk of

each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the

annual closing of accounts The rating is numerically recoded such that the best rating AAA

equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating

D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial

ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-

modelLR models with the original four financial ratios and the additional variables specified in

the hypotheses are estimated for all data In addition an LR model including all additional

variables is estimated for all data to assess the simultaneous effect of all variables Finally six of

the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not

included in this country- level testing hypothesis H7 is not tested because the additional variable

(country risk) is constant within the country

6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models

Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score

model for all data The variation in the ratios is significant as shown by the standard deviation

and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-

failed firms are close to each other indicating a symmetry of distributions However this is not

32

the case for the failed firms For the faileddistressed firms the median exceeds the mean for

these three ratios indicating negatively skewed distributions For X4 (BVETD) the means

significantly exceed the median for both failed and non-failed firms indicating a positively

skewed distribution For each of the four variables both the mean and the median are higher for

non-failed firms than for failed firms which is consistent with the expectations The difference

between the means of the non-failed and failed firms is larger in the original USUS data than in

our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is

approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of

EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may

indicate lower classification accuracy than in the original sample

(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates

(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the

large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model

Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same

statistical method or MDA The coefficients here are negative because the models are estimated

using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight

than in the original USUS data while the weights of WCTA and BVETD have proportionally

decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the

logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR

model These coefficients are directly comparable with those of the MDA model which isas

expected for the this exceptionally large sample The differences in the coefficients of the original

33

four variables among the eight LR-modelLR models (Models 1-8) are small For each model the

coefficient of BVETD is very close to zero This indicatinges that the original four variables and

the additional variables are quite independent of each other

(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-

modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate

that after 2007 (the base category) this risk of failure significantly decreased year by year The

base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due

to as a result of the global financial crisis The crisis played a significant role in the failure of key

businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The

effects were especially felt in Europe The coefficients of Model 4 for the size variables show that

the contribution of size on to the logit (risk measure) is atreaches its maximum value when

logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model

5 confirms the riskiness of young firms because the risk of failure is very high for newly founded

firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The

coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky

industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is

statistically significant (because of the large sample) but negative and very close to zero Finally

the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7

62 All data performance of the Zrsquorsquo-Score models

34

Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1

refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of

all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average

accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)

Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its

performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The

lower part of the table shows the AUCs for the countries included only in the test data The

performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)

samples It is also low and for the liquidation firms in the United Kingdom

The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5

presents the differences of these medians between the non-failed and failed firms by country

This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of

medians This correlation which is high for each financial ratio showing that the effects of the

ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large

except for EBITTA which This implies with the exceptionally high AUC that the ST firms

systematically differ from the non-ST firms although the differences are not extremely large The

differences between the medians are very large in Poland for each ratio justifying the high AUC

They are also large and in Finland and in the Czech Republic where the difference in EBITTA is

average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in

all four ratios are below the average a result thatwhich is obviously associated with a low AUC

35

In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even

negative In Iceland and Ireland the differences only in EBITTA are exceptionally small

(Table 4 here)

(Table 5 here)

Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by

MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)

supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is

at approximately the average level The re-estimation of the coefficients has led to improved

classification accuracy in a number of countries especially in Bosnia China (ST) Norway and

Greece However it has impaired the classification accuracy in the UK and China (delisted)

Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-

modelLR model) showing the effect of the estimation method For the benchmark model the

AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between

among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR

model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country

This result was expected because the coefficients of the models are directly comparable The

similar results for the models may also indicate that the independent variables conform to

multinormality This last latter result confirms what most researchers in the field of default

classification models have concluded that the accuracy levels of MDA and logistical regression

36

models are extremely similar despite any statistical violations such as normality between MDA

and logistical regression models

Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)

than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects

are not positive for all countries The effects are positive for example for Russia Estonia

Germany Ireland and Latvia but these effects are not statistically significant There are

statistically significant negative effects for China (ST) and Serbia There are notable negative

effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are

due to the exceptional annual distributions of failed firms in these countries For the countries

with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the

countries with positive effects it is only a few percent For each group non-failed firms are quite

equally distributed over years When the coefficient of D3 is very low (-0666) it strongly

decreases the risk estimates of most failed firms in the former countries but of only a few failed

firms in the latter countries This which leads to the observed effects H3 is supported by

the level of the whole sample However in some country samples (at the level of individual

levelies) H3 is not supported

Model 4 (LR-modelLR model with size variables) performs better than the benchmark model

which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads

and to significant improvements in the AUC for eg China (delisted) and Austria For China

(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC

37

also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs

in a few countries but thise decrease is not significant Model 4 is based on the four original

variables and the size effect following a second-order parabola This kind of size effect is very

small for micro firms but increases when approaching its maximum value in middle-sized firms

The countries with a positive change in AUC typically have data where in which the percent of

failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized

failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In

contrast the countries with a negative change in AUC have data where in which the percentage

of failed micro firms is exceptionally high (70-80)

Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly

the same AUC as the benchmark model (AUC = 0748) However the difference is positive and

statistically significant giving at least marginal support to H5 (age effect) For nearly all countries

the effect of the age on AUC is small For Austria however thise effect is however positive and

significant For Austria the percentage of non-failed young firms (less than 6 years) among non-

failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed

firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong

positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only

a few non-failed firms which This obviously leads to an improvement in AUC

Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC

supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its

38

AUC is notably higher than the benchmark AUC only in only a few countries such asfor example

France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg

restaurants and hotels and the information technology industryies but it has a positive effect

for on the construction and manufacturing industries For the countries with a positive effect on

AUC the percent of non-failed firms in restaurants and hotels and information technology is

high while that of failed firms is low For the risky industries (construction and manufacturing)

these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many

failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)

firms Therefore the AUC will be increasesd This situation is reversed for the countries with a

negative effect on AUC (Austria and Slovenia) The samples of these countries include a high

percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC

Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher

classification performance (AUC = 0749) than the benchmark model This result gives only very

weak support for H7 (country of origin effect) However for each country the resultant AUC is

nearly identical to that given by the benchmark model This result was expected due to the

negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7

This result implies that country risk hasdoes not have noany effect on the boundary between

bankrupt and non-bankrupt firms

Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional

variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to

39

the benchmark AUC in all test data However the effect on the AUCs largely varies and is either

negative or positive in different countries The positive effect is large in several countries such

as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative

effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show

that the inclusion of additional variables in the original model will usually increase the AUC but

not in every country

The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with

the USUS firms being unlike the majority of other firms in this study being listed or delisted

companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese

firm samples is associated with very small differences between the medians of the non-failed and

failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable

with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed

firms is very high Prior studies based on Chinese ST -firms have also demonstrated good

predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for

additional research and modeling work regarding unlisted and delisted Chinese firms

63 Country-level data performance of the Zrsquorsquo-Score models

The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform

all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the

different models estimated for each country separately (country-level models) In this table the

ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated

40

from the country data this benchmark will beis clearly outperformed by the resulting MDA

(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and

Sweden) However these results give only weak support for H1 at the country level because the

effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and

2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs

given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed

by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation

method)

Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC

than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3

leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United

StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in

nearly every country supporting H4 This improvement is significant however only in the United

StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark

model but for which it is only 0710 for the benchmark model It This improvement is also notable

in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age

category dummies) gives higher AUCs than the benchmark model for several countries but none

of the improvements are significant The positive effect is particularly strong especially in

Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only

weakly supports H5 because these negative effects are relatively small

41

Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects

on AUCs when compared with the benchmark However the only significant effect is the negative

effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the

industry dummies However there are notable positive effects on the AUC in for example

France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak

support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in

AUC compared with the benchmark model in Colombia Iceland and the Unites States It also

leads and to notable improvements in AUC at least in Belgium Denmark Estonia France

Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are

found only in Austria and Slovenia Thus as a conclusion we can concludestate that the

classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can

remarkably increases in most countries when different effects are taken into account by

additional variables This increase is found in most European countries and in Colombia and the

United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-

modelLR model and it can only be slightly improved only slightly by additional variables

(Table 6 here)

7 Summary of the study and a suggested extension

The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model

originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is

assessed using a very large international data set We test how the original version of the Zrsquorsquo-

42

Score model performs in different countries and how re-estimations using another statistical

method and different additional variables affect the classification performance when the data

are very heterogeneous For this kind of testing seven research hypotheses on classification

performance are extractedformulated These research hypotheses are tested for all data and

separately for country data (country-level analysis) The estimation data are from 31 countries

and the results are validated for 34 countries The countries are mainly from Europe but three

non-European countries are included (China Colombia and the United StatesUS) The statuses

used in the classification are mainly bankruptcyactive but receivership firms are also considered

to have failed In the Chinese data ST (special treatment) and delisted firms are also separately

analyzed as failed firms

The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very

satisfactorily in an international context The effects of the four financial ratios on performance

are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small

contribution in re-estimation The original model performs very well in several countries such as

Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only

marginally improved the classification performance thus weakly supporting the obsolescence

hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust

across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion

holds for the re-estimation of the model using LRA because the performance results are very

similar with to those of MDA (H2) The use of additional variables in the model generally

improvesd the classification accuracy of the original model but the results for countries are

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

23

of the Zrsquorsquo-Score model in this study Therefore we retain both private and public firms from all

industrial groupings The ORBIS formats have been derived from the worldrsquos most commonly

used formats for the presentation of business accounts (Ribeiro et al 2010) It is clear that

international comparability may be a problem when administrative firm-level data are

internationally pooled For administrative data Although the definition of variables is usually less

harmonized for administrative data tThis is less of a problem in the ORBIS database because of

the common international format of balance sheets For example although some discrepancies

in profitloss statements may arise because of differences in fiscal systems across countries

balance sheet variables largely adhere to international standards

AThere are a number of factors that influence the international applicability of bankruptcy

prediction models These factors include accounting legislation and practice creditor rights and

investor protection judicial efficiency corporate governance bankruptcy protection and

insolvency management and firm risk-taking These factors strongly differ between European

and non-European countries Therefore we also wantaim to test the performance of the Zrsquorsquo-

Score model outside Europe First it is especiallyparticularly important to include the USUS as

because it is the country of origin for the Zrsquorsquo-Score model the US is especially important and It

is include d in our study as well because it has the largest market capitalization in the world

Second a central motivation in developing the modified Zrsquorsquo-Score model was to make it

applicable to emerging market companies Therefore we include firms from China and Colombia

which represent two very culturally and institutionally very dissimilar emerging market

countries For other non-European countries sufficient bankruptcy data (more than 60 bankrupt

24

firms ie the limit we set for European countries) from ORBIS World were not

available Thus the results are also estimated and tested for three non-European countries (the

United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-

Score modelrsquos performance The samples of firms from these countries are were extracted from

ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms

from around the world

SFor statistical sampling several requirements are set for the statistical sampling of the empirical

data First we require that the firm to be selected be an industrial (non-financial) company

Second its owners must have limited liability (so partnerships and sole proprietors are left out

of the study) Third we set a minimum requirement for the size of the firm Because the financial

ratios of very small firms are generally too unstable for a failure prediction model these firms

are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have

exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we

include in our estimation sample firms from all European countries and 3 pre-selected non-

European countries where the number of failed firms is more greater than 60 If the number of

failed firms for any European country is less than 60 the firms from this country are only included

only in the test sample For qualifying European countries the firms are randomly classified in

the estimation and test samples so that the number of firms is approximately equal in both

samples Thus our estimation data include firms from 28 European and 3 non-European

countries Fifth all failed firms that fulfill the above requirements are included in our samples

However if the number of non-failed firms in a country is very high a sample is randomly

25

selected from that country Finally the time span of fiscal years potentially available for this study

ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in

the database are from a financial period within 2007 and 2010 earlier years are also excluded

for non-failed firms for comparability All qualifying observations of non-failed firms from the

years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses

of failed firms to the last most recent financial statements available before failure The four

independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize

outliers

Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test

data by country The estimation sample includes data from 2602563 non-failed and 38215

failed firms from 28 European and 3 non-European countries The test sample is slightly larger

because it includes data from 31 European and 3 non-European countries From For the country

of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt

firms The available US data that were available consist only of listed (and delisted) firms From

China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are

included in the estimation sample although there are only 32 such firms altogether7 The Chinese

datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status

are separately analyzed separately only in for the test data8 ST firms are listed firms suffering

6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor

26

from serious financial difficulties Excluding the special US data and the two non-private Chinese

datasets 994 of the observations in the data are private firms

(Table 1 here)

52 Status of failed firms

ORBIS has five classes for potentially active firms (active default of payment receivership

dormant and branch) and seven classes for inactive firms that no longer carry out business

activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation

branch and no precision) From Among these classes only active is selected to represent non-

distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by

considering (with exceptions described below) a firm failed if its status in ORBIS is stated as

bankruptcy However because of the small number of bankrupt firms in some countries we also

consider receivership (active) firms as failed even if they are active These firms generally suffer

from serious financial distress However firms in liquidation are generally not included in the

sample of failed firms Firms in liquidation may depending on the country contain firms that

have ceased activities due to reasons other than failure (mergers discontinuing the operations

of a daughter company or of a foreign branch etc) Therefore for most countries we select only

firms that are coded as being bankrupt or under receivership However there are a number of

special cases where failed firms are coded under a different status heading These countries or

samples are the following

Country Status categories Bulgaria In liquidation Bankruptcy

27

Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the

study (for example Switzerland) If a country had only a very small number of failed firms it was

dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries

including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status

classes (including the bankruptcy category) are not completely homogenous within European

countries due to different legislations although there are obvious similarities in insolvency acts

(Philippe et al 2002) China is a special case because it includes samples with three different

criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are

two different samples (liquidation and receivership)

53 Statistical methods

In this study seven research hypotheses are drawn for statistical testing The statistical analysis

will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The

classification performance of the original model is assessed by the AUC (Area Under Curve)

measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close

connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random

28

model and 05 for a model with an average classification performance In SAS software is used

for all statistical analysess SAS software is used

The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is

tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical

method (multiple discriminant analysis or MDA) The problem is that the estimation sample

includes different numbers of failed and non-failed firms from 31 countries In the original Z-

Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from

the USUS Following the characteristics of these data we weight the firms so that the weights

for the failed and non-failed firms are equally In so doingthis way the non-proportional

sampling in from different countries does will not affect the re-estimated model The number of

firms from different countries however varies significantly leading to greater weights for larger

countries To avoid this problem the observations are also weighted so that each country has an

equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using

these weighted data and the resulting AUC is compared with that based on the original model

The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score

model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is

based on less- restrictive statistical assumptions than MDA In this estimation the dependent

variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA

does not require that independent variables be multivariate normal or that groups have equal

covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA

29

creates a score (logit) score L for every firm It is assumed that the independent variables are

linearly related to L This score or logit is used to determine the conditional probability of failure

as follows

= (eq 4)

where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the

original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing

the statistical significance of the difference between AUCs for this LR model and for the re-

estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it

is used as a benchmark for further statistical AUC comparisons because LR is applied as the

principal method in the testing of the remaining research hypotheses

The third hypothesis (H3) is associated with the performance effect of taking account of the

bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on

the following logit

(eq 5)

where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model

bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and

D1 = 1 when year = 2008 0 otherwise

D2 = 1 when year = 2009 0 otherwise

D3 = 1 when year = 2010 0 otherwise

)|1( XYp )( 441101

1

1

1XbXbbL ee

3

1

4

10

jjji

ii DcXbbL

30

The dummy variables do not directly refer to the bankruptcy year which that is not given in the

data basedatabase but rather to the last available year before bankruptcy For failed firms there

is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is

the base category If the AUC of this extended LR model statistically significantly exceeds the AUC

of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3

Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third

hypothesis above However for each hypothesis appropriate variables are used instead of the

year dummies Hypothesis H4 addresses the performance effect of taking size into account the

size and it will beis tested using by adding two additional variables measuring firm size in to the

LR -model In this LR model size is measured by the natural logarithm of total assets and its

squared form In this way the effect of logarithmic size can be reflected by in a function following

the second-order parabola Hypothesis H5 predicts that the classification performance improves

when the age of the firm is explicitly taken into account When testing this hypothesis the

category 6ndash14 years category is used as the base category and two dummy variables are

incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis

H6 looks at whether is associated with the classification performance is affected bythe effect of

the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt

is tested here using dummy variables for seven industries (D1 restaurants and hotels D2

construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and

water production D7 information technology) with all other industries acting as the base

category

31

Hypothesis H7 predicts that the explicit consideration of the country of origin will improves

classification performance This hypothesis is tested by using country risk measures instead ofnot

using dummy variables for countries but using country risk measures instead The country risk of

each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the

annual closing of accounts The rating is numerically recoded such that the best rating AAA

equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating

D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial

ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-

modelLR models with the original four financial ratios and the additional variables specified in

the hypotheses are estimated for all data In addition an LR model including all additional

variables is estimated for all data to assess the simultaneous effect of all variables Finally six of

the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not

included in this country- level testing hypothesis H7 is not tested because the additional variable

(country risk) is constant within the country

6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models

Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score

model for all data The variation in the ratios is significant as shown by the standard deviation

and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-

failed firms are close to each other indicating a symmetry of distributions However this is not

32

the case for the failed firms For the faileddistressed firms the median exceeds the mean for

these three ratios indicating negatively skewed distributions For X4 (BVETD) the means

significantly exceed the median for both failed and non-failed firms indicating a positively

skewed distribution For each of the four variables both the mean and the median are higher for

non-failed firms than for failed firms which is consistent with the expectations The difference

between the means of the non-failed and failed firms is larger in the original USUS data than in

our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is

approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of

EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may

indicate lower classification accuracy than in the original sample

(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates

(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the

large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model

Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same

statistical method or MDA The coefficients here are negative because the models are estimated

using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight

than in the original USUS data while the weights of WCTA and BVETD have proportionally

decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the

logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR

model These coefficients are directly comparable with those of the MDA model which isas

expected for the this exceptionally large sample The differences in the coefficients of the original

33

four variables among the eight LR-modelLR models (Models 1-8) are small For each model the

coefficient of BVETD is very close to zero This indicatinges that the original four variables and

the additional variables are quite independent of each other

(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-

modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate

that after 2007 (the base category) this risk of failure significantly decreased year by year The

base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due

to as a result of the global financial crisis The crisis played a significant role in the failure of key

businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The

effects were especially felt in Europe The coefficients of Model 4 for the size variables show that

the contribution of size on to the logit (risk measure) is atreaches its maximum value when

logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model

5 confirms the riskiness of young firms because the risk of failure is very high for newly founded

firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The

coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky

industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is

statistically significant (because of the large sample) but negative and very close to zero Finally

the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7

62 All data performance of the Zrsquorsquo-Score models

34

Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1

refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of

all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average

accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)

Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its

performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The

lower part of the table shows the AUCs for the countries included only in the test data The

performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)

samples It is also low and for the liquidation firms in the United Kingdom

The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5

presents the differences of these medians between the non-failed and failed firms by country

This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of

medians This correlation which is high for each financial ratio showing that the effects of the

ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large

except for EBITTA which This implies with the exceptionally high AUC that the ST firms

systematically differ from the non-ST firms although the differences are not extremely large The

differences between the medians are very large in Poland for each ratio justifying the high AUC

They are also large and in Finland and in the Czech Republic where the difference in EBITTA is

average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in

all four ratios are below the average a result thatwhich is obviously associated with a low AUC

35

In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even

negative In Iceland and Ireland the differences only in EBITTA are exceptionally small

(Table 4 here)

(Table 5 here)

Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by

MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)

supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is

at approximately the average level The re-estimation of the coefficients has led to improved

classification accuracy in a number of countries especially in Bosnia China (ST) Norway and

Greece However it has impaired the classification accuracy in the UK and China (delisted)

Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-

modelLR model) showing the effect of the estimation method For the benchmark model the

AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between

among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR

model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country

This result was expected because the coefficients of the models are directly comparable The

similar results for the models may also indicate that the independent variables conform to

multinormality This last latter result confirms what most researchers in the field of default

classification models have concluded that the accuracy levels of MDA and logistical regression

36

models are extremely similar despite any statistical violations such as normality between MDA

and logistical regression models

Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)

than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects

are not positive for all countries The effects are positive for example for Russia Estonia

Germany Ireland and Latvia but these effects are not statistically significant There are

statistically significant negative effects for China (ST) and Serbia There are notable negative

effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are

due to the exceptional annual distributions of failed firms in these countries For the countries

with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the

countries with positive effects it is only a few percent For each group non-failed firms are quite

equally distributed over years When the coefficient of D3 is very low (-0666) it strongly

decreases the risk estimates of most failed firms in the former countries but of only a few failed

firms in the latter countries This which leads to the observed effects H3 is supported by

the level of the whole sample However in some country samples (at the level of individual

levelies) H3 is not supported

Model 4 (LR-modelLR model with size variables) performs better than the benchmark model

which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads

and to significant improvements in the AUC for eg China (delisted) and Austria For China

(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC

37

also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs

in a few countries but thise decrease is not significant Model 4 is based on the four original

variables and the size effect following a second-order parabola This kind of size effect is very

small for micro firms but increases when approaching its maximum value in middle-sized firms

The countries with a positive change in AUC typically have data where in which the percent of

failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized

failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In

contrast the countries with a negative change in AUC have data where in which the percentage

of failed micro firms is exceptionally high (70-80)

Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly

the same AUC as the benchmark model (AUC = 0748) However the difference is positive and

statistically significant giving at least marginal support to H5 (age effect) For nearly all countries

the effect of the age on AUC is small For Austria however thise effect is however positive and

significant For Austria the percentage of non-failed young firms (less than 6 years) among non-

failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed

firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong

positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only

a few non-failed firms which This obviously leads to an improvement in AUC

Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC

supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its

38

AUC is notably higher than the benchmark AUC only in only a few countries such asfor example

France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg

restaurants and hotels and the information technology industryies but it has a positive effect

for on the construction and manufacturing industries For the countries with a positive effect on

AUC the percent of non-failed firms in restaurants and hotels and information technology is

high while that of failed firms is low For the risky industries (construction and manufacturing)

these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many

failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)

firms Therefore the AUC will be increasesd This situation is reversed for the countries with a

negative effect on AUC (Austria and Slovenia) The samples of these countries include a high

percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC

Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher

classification performance (AUC = 0749) than the benchmark model This result gives only very

weak support for H7 (country of origin effect) However for each country the resultant AUC is

nearly identical to that given by the benchmark model This result was expected due to the

negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7

This result implies that country risk hasdoes not have noany effect on the boundary between

bankrupt and non-bankrupt firms

Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional

variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to

39

the benchmark AUC in all test data However the effect on the AUCs largely varies and is either

negative or positive in different countries The positive effect is large in several countries such

as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative

effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show

that the inclusion of additional variables in the original model will usually increase the AUC but

not in every country

The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with

the USUS firms being unlike the majority of other firms in this study being listed or delisted

companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese

firm samples is associated with very small differences between the medians of the non-failed and

failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable

with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed

firms is very high Prior studies based on Chinese ST -firms have also demonstrated good

predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for

additional research and modeling work regarding unlisted and delisted Chinese firms

63 Country-level data performance of the Zrsquorsquo-Score models

The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform

all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the

different models estimated for each country separately (country-level models) In this table the

ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated

40

from the country data this benchmark will beis clearly outperformed by the resulting MDA

(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and

Sweden) However these results give only weak support for H1 at the country level because the

effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and

2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs

given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed

by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation

method)

Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC

than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3

leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United

StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in

nearly every country supporting H4 This improvement is significant however only in the United

StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark

model but for which it is only 0710 for the benchmark model It This improvement is also notable

in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age

category dummies) gives higher AUCs than the benchmark model for several countries but none

of the improvements are significant The positive effect is particularly strong especially in

Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only

weakly supports H5 because these negative effects are relatively small

41

Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects

on AUCs when compared with the benchmark However the only significant effect is the negative

effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the

industry dummies However there are notable positive effects on the AUC in for example

France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak

support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in

AUC compared with the benchmark model in Colombia Iceland and the Unites States It also

leads and to notable improvements in AUC at least in Belgium Denmark Estonia France

Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are

found only in Austria and Slovenia Thus as a conclusion we can concludestate that the

classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can

remarkably increases in most countries when different effects are taken into account by

additional variables This increase is found in most European countries and in Colombia and the

United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-

modelLR model and it can only be slightly improved only slightly by additional variables

(Table 6 here)

7 Summary of the study and a suggested extension

The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model

originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is

assessed using a very large international data set We test how the original version of the Zrsquorsquo-

42

Score model performs in different countries and how re-estimations using another statistical

method and different additional variables affect the classification performance when the data

are very heterogeneous For this kind of testing seven research hypotheses on classification

performance are extractedformulated These research hypotheses are tested for all data and

separately for country data (country-level analysis) The estimation data are from 31 countries

and the results are validated for 34 countries The countries are mainly from Europe but three

non-European countries are included (China Colombia and the United StatesUS) The statuses

used in the classification are mainly bankruptcyactive but receivership firms are also considered

to have failed In the Chinese data ST (special treatment) and delisted firms are also separately

analyzed as failed firms

The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very

satisfactorily in an international context The effects of the four financial ratios on performance

are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small

contribution in re-estimation The original model performs very well in several countries such as

Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only

marginally improved the classification performance thus weakly supporting the obsolescence

hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust

across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion

holds for the re-estimation of the model using LRA because the performance results are very

similar with to those of MDA (H2) The use of additional variables in the model generally

improvesd the classification accuracy of the original model but the results for countries are

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

24

firms ie the limit we set for European countries) from ORBIS World were not

available Thus the results are also estimated and tested for three non-European countries (the

United StatesUS China and Colombia) to gain a more global view of the performance of the Zrsquorsquo-

Score modelrsquos performance The samples of firms from these countries are were extracted from

ORBIS World which containsing middle-sized (total assets over 15 million EUR) and larger firms

from around the world

SFor statistical sampling several requirements are set for the statistical sampling of the empirical

data First we require that the firm to be selected be an industrial (non-financial) company

Second its owners must have limited liability (so partnerships and sole proprietors are left out

of the study) Third we set a minimum requirement for the size of the firm Because the financial

ratios of very small firms are generally too unstable for a failure prediction model these firms

are excluded (see Balcaen and Ooghe 2006) We require that the a firmrsquos total assets must have

exceeded 100 thousand EUR at least once in the available time series for a firm Fourth we

include in our estimation sample firms from all European countries and 3 pre-selected non-

European countries where the number of failed firms is more greater than 60 If the number of

failed firms for any European country is less than 60 the firms from this country are only included

only in the test sample For qualifying European countries the firms are randomly classified in

the estimation and test samples so that the number of firms is approximately equal in both

samples Thus our estimation data include firms from 28 European and 3 non-European

countries Fifth all failed firms that fulfill the above requirements are included in our samples

However if the number of non-failed firms in a country is very high a sample is randomly

25

selected from that country Finally the time span of fiscal years potentially available for this study

ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in

the database are from a financial period within 2007 and 2010 earlier years are also excluded

for non-failed firms for comparability All qualifying observations of non-failed firms from the

years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses

of failed firms to the last most recent financial statements available before failure The four

independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize

outliers

Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test

data by country The estimation sample includes data from 2602563 non-failed and 38215

failed firms from 28 European and 3 non-European countries The test sample is slightly larger

because it includes data from 31 European and 3 non-European countries From For the country

of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt

firms The available US data that were available consist only of listed (and delisted) firms From

China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are

included in the estimation sample although there are only 32 such firms altogether7 The Chinese

datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status

are separately analyzed separately only in for the test data8 ST firms are listed firms suffering

6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor

26

from serious financial difficulties Excluding the special US data and the two non-private Chinese

datasets 994 of the observations in the data are private firms

(Table 1 here)

52 Status of failed firms

ORBIS has five classes for potentially active firms (active default of payment receivership

dormant and branch) and seven classes for inactive firms that no longer carry out business

activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation

branch and no precision) From Among these classes only active is selected to represent non-

distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by

considering (with exceptions described below) a firm failed if its status in ORBIS is stated as

bankruptcy However because of the small number of bankrupt firms in some countries we also

consider receivership (active) firms as failed even if they are active These firms generally suffer

from serious financial distress However firms in liquidation are generally not included in the

sample of failed firms Firms in liquidation may depending on the country contain firms that

have ceased activities due to reasons other than failure (mergers discontinuing the operations

of a daughter company or of a foreign branch etc) Therefore for most countries we select only

firms that are coded as being bankrupt or under receivership However there are a number of

special cases where failed firms are coded under a different status heading These countries or

samples are the following

Country Status categories Bulgaria In liquidation Bankruptcy

27

Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the

study (for example Switzerland) If a country had only a very small number of failed firms it was

dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries

including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status

classes (including the bankruptcy category) are not completely homogenous within European

countries due to different legislations although there are obvious similarities in insolvency acts

(Philippe et al 2002) China is a special case because it includes samples with three different

criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are

two different samples (liquidation and receivership)

53 Statistical methods

In this study seven research hypotheses are drawn for statistical testing The statistical analysis

will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The

classification performance of the original model is assessed by the AUC (Area Under Curve)

measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close

connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random

28

model and 05 for a model with an average classification performance In SAS software is used

for all statistical analysess SAS software is used

The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is

tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical

method (multiple discriminant analysis or MDA) The problem is that the estimation sample

includes different numbers of failed and non-failed firms from 31 countries In the original Z-

Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from

the USUS Following the characteristics of these data we weight the firms so that the weights

for the failed and non-failed firms are equally In so doingthis way the non-proportional

sampling in from different countries does will not affect the re-estimated model The number of

firms from different countries however varies significantly leading to greater weights for larger

countries To avoid this problem the observations are also weighted so that each country has an

equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using

these weighted data and the resulting AUC is compared with that based on the original model

The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score

model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is

based on less- restrictive statistical assumptions than MDA In this estimation the dependent

variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA

does not require that independent variables be multivariate normal or that groups have equal

covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA

29

creates a score (logit) score L for every firm It is assumed that the independent variables are

linearly related to L This score or logit is used to determine the conditional probability of failure

as follows

= (eq 4)

where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the

original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing

the statistical significance of the difference between AUCs for this LR model and for the re-

estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it

is used as a benchmark for further statistical AUC comparisons because LR is applied as the

principal method in the testing of the remaining research hypotheses

The third hypothesis (H3) is associated with the performance effect of taking account of the

bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on

the following logit

(eq 5)

where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model

bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and

D1 = 1 when year = 2008 0 otherwise

D2 = 1 when year = 2009 0 otherwise

D3 = 1 when year = 2010 0 otherwise

)|1( XYp )( 441101

1

1

1XbXbbL ee

3

1

4

10

jjji

ii DcXbbL

30

The dummy variables do not directly refer to the bankruptcy year which that is not given in the

data basedatabase but rather to the last available year before bankruptcy For failed firms there

is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is

the base category If the AUC of this extended LR model statistically significantly exceeds the AUC

of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3

Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third

hypothesis above However for each hypothesis appropriate variables are used instead of the

year dummies Hypothesis H4 addresses the performance effect of taking size into account the

size and it will beis tested using by adding two additional variables measuring firm size in to the

LR -model In this LR model size is measured by the natural logarithm of total assets and its

squared form In this way the effect of logarithmic size can be reflected by in a function following

the second-order parabola Hypothesis H5 predicts that the classification performance improves

when the age of the firm is explicitly taken into account When testing this hypothesis the

category 6ndash14 years category is used as the base category and two dummy variables are

incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis

H6 looks at whether is associated with the classification performance is affected bythe effect of

the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt

is tested here using dummy variables for seven industries (D1 restaurants and hotels D2

construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and

water production D7 information technology) with all other industries acting as the base

category

31

Hypothesis H7 predicts that the explicit consideration of the country of origin will improves

classification performance This hypothesis is tested by using country risk measures instead ofnot

using dummy variables for countries but using country risk measures instead The country risk of

each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the

annual closing of accounts The rating is numerically recoded such that the best rating AAA

equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating

D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial

ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-

modelLR models with the original four financial ratios and the additional variables specified in

the hypotheses are estimated for all data In addition an LR model including all additional

variables is estimated for all data to assess the simultaneous effect of all variables Finally six of

the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not

included in this country- level testing hypothesis H7 is not tested because the additional variable

(country risk) is constant within the country

6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models

Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score

model for all data The variation in the ratios is significant as shown by the standard deviation

and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-

failed firms are close to each other indicating a symmetry of distributions However this is not

32

the case for the failed firms For the faileddistressed firms the median exceeds the mean for

these three ratios indicating negatively skewed distributions For X4 (BVETD) the means

significantly exceed the median for both failed and non-failed firms indicating a positively

skewed distribution For each of the four variables both the mean and the median are higher for

non-failed firms than for failed firms which is consistent with the expectations The difference

between the means of the non-failed and failed firms is larger in the original USUS data than in

our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is

approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of

EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may

indicate lower classification accuracy than in the original sample

(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates

(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the

large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model

Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same

statistical method or MDA The coefficients here are negative because the models are estimated

using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight

than in the original USUS data while the weights of WCTA and BVETD have proportionally

decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the

logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR

model These coefficients are directly comparable with those of the MDA model which isas

expected for the this exceptionally large sample The differences in the coefficients of the original

33

four variables among the eight LR-modelLR models (Models 1-8) are small For each model the

coefficient of BVETD is very close to zero This indicatinges that the original four variables and

the additional variables are quite independent of each other

(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-

modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate

that after 2007 (the base category) this risk of failure significantly decreased year by year The

base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due

to as a result of the global financial crisis The crisis played a significant role in the failure of key

businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The

effects were especially felt in Europe The coefficients of Model 4 for the size variables show that

the contribution of size on to the logit (risk measure) is atreaches its maximum value when

logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model

5 confirms the riskiness of young firms because the risk of failure is very high for newly founded

firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The

coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky

industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is

statistically significant (because of the large sample) but negative and very close to zero Finally

the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7

62 All data performance of the Zrsquorsquo-Score models

34

Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1

refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of

all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average

accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)

Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its

performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The

lower part of the table shows the AUCs for the countries included only in the test data The

performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)

samples It is also low and for the liquidation firms in the United Kingdom

The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5

presents the differences of these medians between the non-failed and failed firms by country

This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of

medians This correlation which is high for each financial ratio showing that the effects of the

ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large

except for EBITTA which This implies with the exceptionally high AUC that the ST firms

systematically differ from the non-ST firms although the differences are not extremely large The

differences between the medians are very large in Poland for each ratio justifying the high AUC

They are also large and in Finland and in the Czech Republic where the difference in EBITTA is

average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in

all four ratios are below the average a result thatwhich is obviously associated with a low AUC

35

In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even

negative In Iceland and Ireland the differences only in EBITTA are exceptionally small

(Table 4 here)

(Table 5 here)

Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by

MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)

supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is

at approximately the average level The re-estimation of the coefficients has led to improved

classification accuracy in a number of countries especially in Bosnia China (ST) Norway and

Greece However it has impaired the classification accuracy in the UK and China (delisted)

Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-

modelLR model) showing the effect of the estimation method For the benchmark model the

AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between

among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR

model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country

This result was expected because the coefficients of the models are directly comparable The

similar results for the models may also indicate that the independent variables conform to

multinormality This last latter result confirms what most researchers in the field of default

classification models have concluded that the accuracy levels of MDA and logistical regression

36

models are extremely similar despite any statistical violations such as normality between MDA

and logistical regression models

Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)

than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects

are not positive for all countries The effects are positive for example for Russia Estonia

Germany Ireland and Latvia but these effects are not statistically significant There are

statistically significant negative effects for China (ST) and Serbia There are notable negative

effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are

due to the exceptional annual distributions of failed firms in these countries For the countries

with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the

countries with positive effects it is only a few percent For each group non-failed firms are quite

equally distributed over years When the coefficient of D3 is very low (-0666) it strongly

decreases the risk estimates of most failed firms in the former countries but of only a few failed

firms in the latter countries This which leads to the observed effects H3 is supported by

the level of the whole sample However in some country samples (at the level of individual

levelies) H3 is not supported

Model 4 (LR-modelLR model with size variables) performs better than the benchmark model

which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads

and to significant improvements in the AUC for eg China (delisted) and Austria For China

(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC

37

also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs

in a few countries but thise decrease is not significant Model 4 is based on the four original

variables and the size effect following a second-order parabola This kind of size effect is very

small for micro firms but increases when approaching its maximum value in middle-sized firms

The countries with a positive change in AUC typically have data where in which the percent of

failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized

failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In

contrast the countries with a negative change in AUC have data where in which the percentage

of failed micro firms is exceptionally high (70-80)

Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly

the same AUC as the benchmark model (AUC = 0748) However the difference is positive and

statistically significant giving at least marginal support to H5 (age effect) For nearly all countries

the effect of the age on AUC is small For Austria however thise effect is however positive and

significant For Austria the percentage of non-failed young firms (less than 6 years) among non-

failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed

firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong

positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only

a few non-failed firms which This obviously leads to an improvement in AUC

Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC

supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its

38

AUC is notably higher than the benchmark AUC only in only a few countries such asfor example

France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg

restaurants and hotels and the information technology industryies but it has a positive effect

for on the construction and manufacturing industries For the countries with a positive effect on

AUC the percent of non-failed firms in restaurants and hotels and information technology is

high while that of failed firms is low For the risky industries (construction and manufacturing)

these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many

failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)

firms Therefore the AUC will be increasesd This situation is reversed for the countries with a

negative effect on AUC (Austria and Slovenia) The samples of these countries include a high

percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC

Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher

classification performance (AUC = 0749) than the benchmark model This result gives only very

weak support for H7 (country of origin effect) However for each country the resultant AUC is

nearly identical to that given by the benchmark model This result was expected due to the

negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7

This result implies that country risk hasdoes not have noany effect on the boundary between

bankrupt and non-bankrupt firms

Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional

variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to

39

the benchmark AUC in all test data However the effect on the AUCs largely varies and is either

negative or positive in different countries The positive effect is large in several countries such

as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative

effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show

that the inclusion of additional variables in the original model will usually increase the AUC but

not in every country

The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with

the USUS firms being unlike the majority of other firms in this study being listed or delisted

companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese

firm samples is associated with very small differences between the medians of the non-failed and

failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable

with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed

firms is very high Prior studies based on Chinese ST -firms have also demonstrated good

predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for

additional research and modeling work regarding unlisted and delisted Chinese firms

63 Country-level data performance of the Zrsquorsquo-Score models

The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform

all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the

different models estimated for each country separately (country-level models) In this table the

ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated

40

from the country data this benchmark will beis clearly outperformed by the resulting MDA

(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and

Sweden) However these results give only weak support for H1 at the country level because the

effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and

2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs

given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed

by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation

method)

Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC

than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3

leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United

StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in

nearly every country supporting H4 This improvement is significant however only in the United

StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark

model but for which it is only 0710 for the benchmark model It This improvement is also notable

in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age

category dummies) gives higher AUCs than the benchmark model for several countries but none

of the improvements are significant The positive effect is particularly strong especially in

Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only

weakly supports H5 because these negative effects are relatively small

41

Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects

on AUCs when compared with the benchmark However the only significant effect is the negative

effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the

industry dummies However there are notable positive effects on the AUC in for example

France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak

support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in

AUC compared with the benchmark model in Colombia Iceland and the Unites States It also

leads and to notable improvements in AUC at least in Belgium Denmark Estonia France

Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are

found only in Austria and Slovenia Thus as a conclusion we can concludestate that the

classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can

remarkably increases in most countries when different effects are taken into account by

additional variables This increase is found in most European countries and in Colombia and the

United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-

modelLR model and it can only be slightly improved only slightly by additional variables

(Table 6 here)

7 Summary of the study and a suggested extension

The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model

originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is

assessed using a very large international data set We test how the original version of the Zrsquorsquo-

42

Score model performs in different countries and how re-estimations using another statistical

method and different additional variables affect the classification performance when the data

are very heterogeneous For this kind of testing seven research hypotheses on classification

performance are extractedformulated These research hypotheses are tested for all data and

separately for country data (country-level analysis) The estimation data are from 31 countries

and the results are validated for 34 countries The countries are mainly from Europe but three

non-European countries are included (China Colombia and the United StatesUS) The statuses

used in the classification are mainly bankruptcyactive but receivership firms are also considered

to have failed In the Chinese data ST (special treatment) and delisted firms are also separately

analyzed as failed firms

The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very

satisfactorily in an international context The effects of the four financial ratios on performance

are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small

contribution in re-estimation The original model performs very well in several countries such as

Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only

marginally improved the classification performance thus weakly supporting the obsolescence

hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust

across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion

holds for the re-estimation of the model using LRA because the performance results are very

similar with to those of MDA (H2) The use of additional variables in the model generally

improvesd the classification accuracy of the original model but the results for countries are

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

25

selected from that country Finally the time span of fiscal years potentially available for this study

ranges from 2002 to 2010 Because the last most recent financial statements for failed firms in

the database are from a financial period within 2007 and 2010 earlier years are also excluded

for non-failed firms for comparability All qualifying observations of non-failed firms from the

years 2007 to 2010 are included in the data sets As for failed firms Wwe restrict the analyses

of failed firms to the last most recent financial statements available before failure The four

independent variables of the Zrsquorsquo-Score model were winsorized at 1 and 99 to minimize

outliers

Table 1 shows the resulting number of non-failed and failed firms in the estimation data and test

data by country The estimation sample includes data from 2602563 non-failed and 38215

failed firms from 28 European and 3 non-European countries The test sample is slightly larger

because it includes data from 31 European and 3 non-European countries From For the country

of origin of the Zrsquorsquo-Score model the US the estimation sample only includes only 56 bankrupt

firms The available US data that were available consist only of listed (and delisted) firms From

China there are three sub-samples Only 32 pPublic firms with special treatment (ST)6 status are

included in the estimation sample although there are only 32 such firms altogether7 The Chinese

datasets of predominantly private firms (CN) and of public firms with delisted (DL) failure status

are separately analyzed separately only in for the test data8 ST firms are listed firms suffering

6 See Zhang Altman and Yen et al (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies These are firms put on probation by the stock exchange for poor operating performance andor negative equity 7This is done because the results about predictability were also good for such a small sample 8These firms are included only in the test data because the predictability of failure was exceptionally poor

26

from serious financial difficulties Excluding the special US data and the two non-private Chinese

datasets 994 of the observations in the data are private firms

(Table 1 here)

52 Status of failed firms

ORBIS has five classes for potentially active firms (active default of payment receivership

dormant and branch) and seven classes for inactive firms that no longer carry out business

activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation

branch and no precision) From Among these classes only active is selected to represent non-

distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by

considering (with exceptions described below) a firm failed if its status in ORBIS is stated as

bankruptcy However because of the small number of bankrupt firms in some countries we also

consider receivership (active) firms as failed even if they are active These firms generally suffer

from serious financial distress However firms in liquidation are generally not included in the

sample of failed firms Firms in liquidation may depending on the country contain firms that

have ceased activities due to reasons other than failure (mergers discontinuing the operations

of a daughter company or of a foreign branch etc) Therefore for most countries we select only

firms that are coded as being bankrupt or under receivership However there are a number of

special cases where failed firms are coded under a different status heading These countries or

samples are the following

Country Status categories Bulgaria In liquidation Bankruptcy

27

Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the

study (for example Switzerland) If a country had only a very small number of failed firms it was

dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries

including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status

classes (including the bankruptcy category) are not completely homogenous within European

countries due to different legislations although there are obvious similarities in insolvency acts

(Philippe et al 2002) China is a special case because it includes samples with three different

criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are

two different samples (liquidation and receivership)

53 Statistical methods

In this study seven research hypotheses are drawn for statistical testing The statistical analysis

will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The

classification performance of the original model is assessed by the AUC (Area Under Curve)

measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close

connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random

28

model and 05 for a model with an average classification performance In SAS software is used

for all statistical analysess SAS software is used

The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is

tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical

method (multiple discriminant analysis or MDA) The problem is that the estimation sample

includes different numbers of failed and non-failed firms from 31 countries In the original Z-

Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from

the USUS Following the characteristics of these data we weight the firms so that the weights

for the failed and non-failed firms are equally In so doingthis way the non-proportional

sampling in from different countries does will not affect the re-estimated model The number of

firms from different countries however varies significantly leading to greater weights for larger

countries To avoid this problem the observations are also weighted so that each country has an

equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using

these weighted data and the resulting AUC is compared with that based on the original model

The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score

model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is

based on less- restrictive statistical assumptions than MDA In this estimation the dependent

variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA

does not require that independent variables be multivariate normal or that groups have equal

covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA

29

creates a score (logit) score L for every firm It is assumed that the independent variables are

linearly related to L This score or logit is used to determine the conditional probability of failure

as follows

= (eq 4)

where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the

original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing

the statistical significance of the difference between AUCs for this LR model and for the re-

estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it

is used as a benchmark for further statistical AUC comparisons because LR is applied as the

principal method in the testing of the remaining research hypotheses

The third hypothesis (H3) is associated with the performance effect of taking account of the

bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on

the following logit

(eq 5)

where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model

bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and

D1 = 1 when year = 2008 0 otherwise

D2 = 1 when year = 2009 0 otherwise

D3 = 1 when year = 2010 0 otherwise

)|1( XYp )( 441101

1

1

1XbXbbL ee

3

1

4

10

jjji

ii DcXbbL

30

The dummy variables do not directly refer to the bankruptcy year which that is not given in the

data basedatabase but rather to the last available year before bankruptcy For failed firms there

is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is

the base category If the AUC of this extended LR model statistically significantly exceeds the AUC

of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3

Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third

hypothesis above However for each hypothesis appropriate variables are used instead of the

year dummies Hypothesis H4 addresses the performance effect of taking size into account the

size and it will beis tested using by adding two additional variables measuring firm size in to the

LR -model In this LR model size is measured by the natural logarithm of total assets and its

squared form In this way the effect of logarithmic size can be reflected by in a function following

the second-order parabola Hypothesis H5 predicts that the classification performance improves

when the age of the firm is explicitly taken into account When testing this hypothesis the

category 6ndash14 years category is used as the base category and two dummy variables are

incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis

H6 looks at whether is associated with the classification performance is affected bythe effect of

the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt

is tested here using dummy variables for seven industries (D1 restaurants and hotels D2

construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and

water production D7 information technology) with all other industries acting as the base

category

31

Hypothesis H7 predicts that the explicit consideration of the country of origin will improves

classification performance This hypothesis is tested by using country risk measures instead ofnot

using dummy variables for countries but using country risk measures instead The country risk of

each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the

annual closing of accounts The rating is numerically recoded such that the best rating AAA

equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating

D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial

ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-

modelLR models with the original four financial ratios and the additional variables specified in

the hypotheses are estimated for all data In addition an LR model including all additional

variables is estimated for all data to assess the simultaneous effect of all variables Finally six of

the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not

included in this country- level testing hypothesis H7 is not tested because the additional variable

(country risk) is constant within the country

6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models

Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score

model for all data The variation in the ratios is significant as shown by the standard deviation

and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-

failed firms are close to each other indicating a symmetry of distributions However this is not

32

the case for the failed firms For the faileddistressed firms the median exceeds the mean for

these three ratios indicating negatively skewed distributions For X4 (BVETD) the means

significantly exceed the median for both failed and non-failed firms indicating a positively

skewed distribution For each of the four variables both the mean and the median are higher for

non-failed firms than for failed firms which is consistent with the expectations The difference

between the means of the non-failed and failed firms is larger in the original USUS data than in

our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is

approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of

EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may

indicate lower classification accuracy than in the original sample

(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates

(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the

large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model

Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same

statistical method or MDA The coefficients here are negative because the models are estimated

using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight

than in the original USUS data while the weights of WCTA and BVETD have proportionally

decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the

logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR

model These coefficients are directly comparable with those of the MDA model which isas

expected for the this exceptionally large sample The differences in the coefficients of the original

33

four variables among the eight LR-modelLR models (Models 1-8) are small For each model the

coefficient of BVETD is very close to zero This indicatinges that the original four variables and

the additional variables are quite independent of each other

(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-

modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate

that after 2007 (the base category) this risk of failure significantly decreased year by year The

base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due

to as a result of the global financial crisis The crisis played a significant role in the failure of key

businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The

effects were especially felt in Europe The coefficients of Model 4 for the size variables show that

the contribution of size on to the logit (risk measure) is atreaches its maximum value when

logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model

5 confirms the riskiness of young firms because the risk of failure is very high for newly founded

firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The

coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky

industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is

statistically significant (because of the large sample) but negative and very close to zero Finally

the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7

62 All data performance of the Zrsquorsquo-Score models

34

Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1

refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of

all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average

accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)

Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its

performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The

lower part of the table shows the AUCs for the countries included only in the test data The

performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)

samples It is also low and for the liquidation firms in the United Kingdom

The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5

presents the differences of these medians between the non-failed and failed firms by country

This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of

medians This correlation which is high for each financial ratio showing that the effects of the

ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large

except for EBITTA which This implies with the exceptionally high AUC that the ST firms

systematically differ from the non-ST firms although the differences are not extremely large The

differences between the medians are very large in Poland for each ratio justifying the high AUC

They are also large and in Finland and in the Czech Republic where the difference in EBITTA is

average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in

all four ratios are below the average a result thatwhich is obviously associated with a low AUC

35

In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even

negative In Iceland and Ireland the differences only in EBITTA are exceptionally small

(Table 4 here)

(Table 5 here)

Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by

MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)

supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is

at approximately the average level The re-estimation of the coefficients has led to improved

classification accuracy in a number of countries especially in Bosnia China (ST) Norway and

Greece However it has impaired the classification accuracy in the UK and China (delisted)

Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-

modelLR model) showing the effect of the estimation method For the benchmark model the

AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between

among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR

model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country

This result was expected because the coefficients of the models are directly comparable The

similar results for the models may also indicate that the independent variables conform to

multinormality This last latter result confirms what most researchers in the field of default

classification models have concluded that the accuracy levels of MDA and logistical regression

36

models are extremely similar despite any statistical violations such as normality between MDA

and logistical regression models

Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)

than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects

are not positive for all countries The effects are positive for example for Russia Estonia

Germany Ireland and Latvia but these effects are not statistically significant There are

statistically significant negative effects for China (ST) and Serbia There are notable negative

effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are

due to the exceptional annual distributions of failed firms in these countries For the countries

with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the

countries with positive effects it is only a few percent For each group non-failed firms are quite

equally distributed over years When the coefficient of D3 is very low (-0666) it strongly

decreases the risk estimates of most failed firms in the former countries but of only a few failed

firms in the latter countries This which leads to the observed effects H3 is supported by

the level of the whole sample However in some country samples (at the level of individual

levelies) H3 is not supported

Model 4 (LR-modelLR model with size variables) performs better than the benchmark model

which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads

and to significant improvements in the AUC for eg China (delisted) and Austria For China

(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC

37

also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs

in a few countries but thise decrease is not significant Model 4 is based on the four original

variables and the size effect following a second-order parabola This kind of size effect is very

small for micro firms but increases when approaching its maximum value in middle-sized firms

The countries with a positive change in AUC typically have data where in which the percent of

failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized

failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In

contrast the countries with a negative change in AUC have data where in which the percentage

of failed micro firms is exceptionally high (70-80)

Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly

the same AUC as the benchmark model (AUC = 0748) However the difference is positive and

statistically significant giving at least marginal support to H5 (age effect) For nearly all countries

the effect of the age on AUC is small For Austria however thise effect is however positive and

significant For Austria the percentage of non-failed young firms (less than 6 years) among non-

failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed

firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong

positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only

a few non-failed firms which This obviously leads to an improvement in AUC

Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC

supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its

38

AUC is notably higher than the benchmark AUC only in only a few countries such asfor example

France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg

restaurants and hotels and the information technology industryies but it has a positive effect

for on the construction and manufacturing industries For the countries with a positive effect on

AUC the percent of non-failed firms in restaurants and hotels and information technology is

high while that of failed firms is low For the risky industries (construction and manufacturing)

these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many

failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)

firms Therefore the AUC will be increasesd This situation is reversed for the countries with a

negative effect on AUC (Austria and Slovenia) The samples of these countries include a high

percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC

Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher

classification performance (AUC = 0749) than the benchmark model This result gives only very

weak support for H7 (country of origin effect) However for each country the resultant AUC is

nearly identical to that given by the benchmark model This result was expected due to the

negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7

This result implies that country risk hasdoes not have noany effect on the boundary between

bankrupt and non-bankrupt firms

Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional

variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to

39

the benchmark AUC in all test data However the effect on the AUCs largely varies and is either

negative or positive in different countries The positive effect is large in several countries such

as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative

effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show

that the inclusion of additional variables in the original model will usually increase the AUC but

not in every country

The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with

the USUS firms being unlike the majority of other firms in this study being listed or delisted

companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese

firm samples is associated with very small differences between the medians of the non-failed and

failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable

with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed

firms is very high Prior studies based on Chinese ST -firms have also demonstrated good

predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for

additional research and modeling work regarding unlisted and delisted Chinese firms

63 Country-level data performance of the Zrsquorsquo-Score models

The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform

all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the

different models estimated for each country separately (country-level models) In this table the

ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated

40

from the country data this benchmark will beis clearly outperformed by the resulting MDA

(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and

Sweden) However these results give only weak support for H1 at the country level because the

effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and

2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs

given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed

by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation

method)

Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC

than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3

leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United

StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in

nearly every country supporting H4 This improvement is significant however only in the United

StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark

model but for which it is only 0710 for the benchmark model It This improvement is also notable

in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age

category dummies) gives higher AUCs than the benchmark model for several countries but none

of the improvements are significant The positive effect is particularly strong especially in

Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only

weakly supports H5 because these negative effects are relatively small

41

Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects

on AUCs when compared with the benchmark However the only significant effect is the negative

effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the

industry dummies However there are notable positive effects on the AUC in for example

France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak

support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in

AUC compared with the benchmark model in Colombia Iceland and the Unites States It also

leads and to notable improvements in AUC at least in Belgium Denmark Estonia France

Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are

found only in Austria and Slovenia Thus as a conclusion we can concludestate that the

classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can

remarkably increases in most countries when different effects are taken into account by

additional variables This increase is found in most European countries and in Colombia and the

United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-

modelLR model and it can only be slightly improved only slightly by additional variables

(Table 6 here)

7 Summary of the study and a suggested extension

The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model

originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is

assessed using a very large international data set We test how the original version of the Zrsquorsquo-

42

Score model performs in different countries and how re-estimations using another statistical

method and different additional variables affect the classification performance when the data

are very heterogeneous For this kind of testing seven research hypotheses on classification

performance are extractedformulated These research hypotheses are tested for all data and

separately for country data (country-level analysis) The estimation data are from 31 countries

and the results are validated for 34 countries The countries are mainly from Europe but three

non-European countries are included (China Colombia and the United StatesUS) The statuses

used in the classification are mainly bankruptcyactive but receivership firms are also considered

to have failed In the Chinese data ST (special treatment) and delisted firms are also separately

analyzed as failed firms

The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very

satisfactorily in an international context The effects of the four financial ratios on performance

are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small

contribution in re-estimation The original model performs very well in several countries such as

Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only

marginally improved the classification performance thus weakly supporting the obsolescence

hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust

across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion

holds for the re-estimation of the model using LRA because the performance results are very

similar with to those of MDA (H2) The use of additional variables in the model generally

improvesd the classification accuracy of the original model but the results for countries are

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

26

from serious financial difficulties Excluding the special US data and the two non-private Chinese

datasets 994 of the observations in the data are private firms

(Table 1 here)

52 Status of failed firms

ORBIS has five classes for potentially active firms (active default of payment receivership

dormant and branch) and seven classes for inactive firms that no longer carry out business

activities (bankruptcy dissolved dissolved-merger dissolved-demerger in liquidation

branch and no precision) From Among these classes only active is selected to represent non-

distressed firms In selecting the failed firms we try to avoid ambiguity as much as possible by

considering (with exceptions described below) a firm failed if its status in ORBIS is stated as

bankruptcy However because of the small number of bankrupt firms in some countries we also

consider receivership (active) firms as failed even if they are active These firms generally suffer

from serious financial distress However firms in liquidation are generally not included in the

sample of failed firms Firms in liquidation may depending on the country contain firms that

have ceased activities due to reasons other than failure (mergers discontinuing the operations

of a daughter company or of a foreign branch etc) Therefore for most countries we select only

firms that are coded as being bankrupt or under receivership However there are a number of

special cases where failed firms are coded under a different status heading These countries or

samples are the following

Country Status categories Bulgaria In liquidation Bankruptcy

27

Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the

study (for example Switzerland) If a country had only a very small number of failed firms it was

dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries

including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status

classes (including the bankruptcy category) are not completely homogenous within European

countries due to different legislations although there are obvious similarities in insolvency acts

(Philippe et al 2002) China is a special case because it includes samples with three different

criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are

two different samples (liquidation and receivership)

53 Statistical methods

In this study seven research hypotheses are drawn for statistical testing The statistical analysis

will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The

classification performance of the original model is assessed by the AUC (Area Under Curve)

measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close

connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random

28

model and 05 for a model with an average classification performance In SAS software is used

for all statistical analysess SAS software is used

The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is

tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical

method (multiple discriminant analysis or MDA) The problem is that the estimation sample

includes different numbers of failed and non-failed firms from 31 countries In the original Z-

Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from

the USUS Following the characteristics of these data we weight the firms so that the weights

for the failed and non-failed firms are equally In so doingthis way the non-proportional

sampling in from different countries does will not affect the re-estimated model The number of

firms from different countries however varies significantly leading to greater weights for larger

countries To avoid this problem the observations are also weighted so that each country has an

equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using

these weighted data and the resulting AUC is compared with that based on the original model

The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score

model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is

based on less- restrictive statistical assumptions than MDA In this estimation the dependent

variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA

does not require that independent variables be multivariate normal or that groups have equal

covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA

29

creates a score (logit) score L for every firm It is assumed that the independent variables are

linearly related to L This score or logit is used to determine the conditional probability of failure

as follows

= (eq 4)

where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the

original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing

the statistical significance of the difference between AUCs for this LR model and for the re-

estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it

is used as a benchmark for further statistical AUC comparisons because LR is applied as the

principal method in the testing of the remaining research hypotheses

The third hypothesis (H3) is associated with the performance effect of taking account of the

bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on

the following logit

(eq 5)

where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model

bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and

D1 = 1 when year = 2008 0 otherwise

D2 = 1 when year = 2009 0 otherwise

D3 = 1 when year = 2010 0 otherwise

)|1( XYp )( 441101

1

1

1XbXbbL ee

3

1

4

10

jjji

ii DcXbbL

30

The dummy variables do not directly refer to the bankruptcy year which that is not given in the

data basedatabase but rather to the last available year before bankruptcy For failed firms there

is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is

the base category If the AUC of this extended LR model statistically significantly exceeds the AUC

of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3

Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third

hypothesis above However for each hypothesis appropriate variables are used instead of the

year dummies Hypothesis H4 addresses the performance effect of taking size into account the

size and it will beis tested using by adding two additional variables measuring firm size in to the

LR -model In this LR model size is measured by the natural logarithm of total assets and its

squared form In this way the effect of logarithmic size can be reflected by in a function following

the second-order parabola Hypothesis H5 predicts that the classification performance improves

when the age of the firm is explicitly taken into account When testing this hypothesis the

category 6ndash14 years category is used as the base category and two dummy variables are

incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis

H6 looks at whether is associated with the classification performance is affected bythe effect of

the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt

is tested here using dummy variables for seven industries (D1 restaurants and hotels D2

construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and

water production D7 information technology) with all other industries acting as the base

category

31

Hypothesis H7 predicts that the explicit consideration of the country of origin will improves

classification performance This hypothesis is tested by using country risk measures instead ofnot

using dummy variables for countries but using country risk measures instead The country risk of

each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the

annual closing of accounts The rating is numerically recoded such that the best rating AAA

equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating

D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial

ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-

modelLR models with the original four financial ratios and the additional variables specified in

the hypotheses are estimated for all data In addition an LR model including all additional

variables is estimated for all data to assess the simultaneous effect of all variables Finally six of

the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not

included in this country- level testing hypothesis H7 is not tested because the additional variable

(country risk) is constant within the country

6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models

Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score

model for all data The variation in the ratios is significant as shown by the standard deviation

and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-

failed firms are close to each other indicating a symmetry of distributions However this is not

32

the case for the failed firms For the faileddistressed firms the median exceeds the mean for

these three ratios indicating negatively skewed distributions For X4 (BVETD) the means

significantly exceed the median for both failed and non-failed firms indicating a positively

skewed distribution For each of the four variables both the mean and the median are higher for

non-failed firms than for failed firms which is consistent with the expectations The difference

between the means of the non-failed and failed firms is larger in the original USUS data than in

our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is

approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of

EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may

indicate lower classification accuracy than in the original sample

(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates

(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the

large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model

Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same

statistical method or MDA The coefficients here are negative because the models are estimated

using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight

than in the original USUS data while the weights of WCTA and BVETD have proportionally

decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the

logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR

model These coefficients are directly comparable with those of the MDA model which isas

expected for the this exceptionally large sample The differences in the coefficients of the original

33

four variables among the eight LR-modelLR models (Models 1-8) are small For each model the

coefficient of BVETD is very close to zero This indicatinges that the original four variables and

the additional variables are quite independent of each other

(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-

modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate

that after 2007 (the base category) this risk of failure significantly decreased year by year The

base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due

to as a result of the global financial crisis The crisis played a significant role in the failure of key

businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The

effects were especially felt in Europe The coefficients of Model 4 for the size variables show that

the contribution of size on to the logit (risk measure) is atreaches its maximum value when

logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model

5 confirms the riskiness of young firms because the risk of failure is very high for newly founded

firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The

coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky

industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is

statistically significant (because of the large sample) but negative and very close to zero Finally

the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7

62 All data performance of the Zrsquorsquo-Score models

34

Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1

refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of

all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average

accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)

Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its

performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The

lower part of the table shows the AUCs for the countries included only in the test data The

performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)

samples It is also low and for the liquidation firms in the United Kingdom

The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5

presents the differences of these medians between the non-failed and failed firms by country

This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of

medians This correlation which is high for each financial ratio showing that the effects of the

ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large

except for EBITTA which This implies with the exceptionally high AUC that the ST firms

systematically differ from the non-ST firms although the differences are not extremely large The

differences between the medians are very large in Poland for each ratio justifying the high AUC

They are also large and in Finland and in the Czech Republic where the difference in EBITTA is

average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in

all four ratios are below the average a result thatwhich is obviously associated with a low AUC

35

In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even

negative In Iceland and Ireland the differences only in EBITTA are exceptionally small

(Table 4 here)

(Table 5 here)

Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by

MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)

supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is

at approximately the average level The re-estimation of the coefficients has led to improved

classification accuracy in a number of countries especially in Bosnia China (ST) Norway and

Greece However it has impaired the classification accuracy in the UK and China (delisted)

Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-

modelLR model) showing the effect of the estimation method For the benchmark model the

AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between

among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR

model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country

This result was expected because the coefficients of the models are directly comparable The

similar results for the models may also indicate that the independent variables conform to

multinormality This last latter result confirms what most researchers in the field of default

classification models have concluded that the accuracy levels of MDA and logistical regression

36

models are extremely similar despite any statistical violations such as normality between MDA

and logistical regression models

Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)

than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects

are not positive for all countries The effects are positive for example for Russia Estonia

Germany Ireland and Latvia but these effects are not statistically significant There are

statistically significant negative effects for China (ST) and Serbia There are notable negative

effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are

due to the exceptional annual distributions of failed firms in these countries For the countries

with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the

countries with positive effects it is only a few percent For each group non-failed firms are quite

equally distributed over years When the coefficient of D3 is very low (-0666) it strongly

decreases the risk estimates of most failed firms in the former countries but of only a few failed

firms in the latter countries This which leads to the observed effects H3 is supported by

the level of the whole sample However in some country samples (at the level of individual

levelies) H3 is not supported

Model 4 (LR-modelLR model with size variables) performs better than the benchmark model

which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads

and to significant improvements in the AUC for eg China (delisted) and Austria For China

(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC

37

also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs

in a few countries but thise decrease is not significant Model 4 is based on the four original

variables and the size effect following a second-order parabola This kind of size effect is very

small for micro firms but increases when approaching its maximum value in middle-sized firms

The countries with a positive change in AUC typically have data where in which the percent of

failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized

failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In

contrast the countries with a negative change in AUC have data where in which the percentage

of failed micro firms is exceptionally high (70-80)

Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly

the same AUC as the benchmark model (AUC = 0748) However the difference is positive and

statistically significant giving at least marginal support to H5 (age effect) For nearly all countries

the effect of the age on AUC is small For Austria however thise effect is however positive and

significant For Austria the percentage of non-failed young firms (less than 6 years) among non-

failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed

firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong

positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only

a few non-failed firms which This obviously leads to an improvement in AUC

Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC

supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its

38

AUC is notably higher than the benchmark AUC only in only a few countries such asfor example

France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg

restaurants and hotels and the information technology industryies but it has a positive effect

for on the construction and manufacturing industries For the countries with a positive effect on

AUC the percent of non-failed firms in restaurants and hotels and information technology is

high while that of failed firms is low For the risky industries (construction and manufacturing)

these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many

failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)

firms Therefore the AUC will be increasesd This situation is reversed for the countries with a

negative effect on AUC (Austria and Slovenia) The samples of these countries include a high

percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC

Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher

classification performance (AUC = 0749) than the benchmark model This result gives only very

weak support for H7 (country of origin effect) However for each country the resultant AUC is

nearly identical to that given by the benchmark model This result was expected due to the

negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7

This result implies that country risk hasdoes not have noany effect on the boundary between

bankrupt and non-bankrupt firms

Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional

variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to

39

the benchmark AUC in all test data However the effect on the AUCs largely varies and is either

negative or positive in different countries The positive effect is large in several countries such

as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative

effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show

that the inclusion of additional variables in the original model will usually increase the AUC but

not in every country

The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with

the USUS firms being unlike the majority of other firms in this study being listed or delisted

companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese

firm samples is associated with very small differences between the medians of the non-failed and

failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable

with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed

firms is very high Prior studies based on Chinese ST -firms have also demonstrated good

predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for

additional research and modeling work regarding unlisted and delisted Chinese firms

63 Country-level data performance of the Zrsquorsquo-Score models

The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform

all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the

different models estimated for each country separately (country-level models) In this table the

ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated

40

from the country data this benchmark will beis clearly outperformed by the resulting MDA

(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and

Sweden) However these results give only weak support for H1 at the country level because the

effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and

2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs

given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed

by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation

method)

Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC

than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3

leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United

StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in

nearly every country supporting H4 This improvement is significant however only in the United

StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark

model but for which it is only 0710 for the benchmark model It This improvement is also notable

in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age

category dummies) gives higher AUCs than the benchmark model for several countries but none

of the improvements are significant The positive effect is particularly strong especially in

Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only

weakly supports H5 because these negative effects are relatively small

41

Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects

on AUCs when compared with the benchmark However the only significant effect is the negative

effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the

industry dummies However there are notable positive effects on the AUC in for example

France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak

support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in

AUC compared with the benchmark model in Colombia Iceland and the Unites States It also

leads and to notable improvements in AUC at least in Belgium Denmark Estonia France

Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are

found only in Austria and Slovenia Thus as a conclusion we can concludestate that the

classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can

remarkably increases in most countries when different effects are taken into account by

additional variables This increase is found in most European countries and in Colombia and the

United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-

modelLR model and it can only be slightly improved only slightly by additional variables

(Table 6 here)

7 Summary of the study and a suggested extension

The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model

originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is

assessed using a very large international data set We test how the original version of the Zrsquorsquo-

42

Score model performs in different countries and how re-estimations using another statistical

method and different additional variables affect the classification performance when the data

are very heterogeneous For this kind of testing seven research hypotheses on classification

performance are extractedformulated These research hypotheses are tested for all data and

separately for country data (country-level analysis) The estimation data are from 31 countries

and the results are validated for 34 countries The countries are mainly from Europe but three

non-European countries are included (China Colombia and the United StatesUS) The statuses

used in the classification are mainly bankruptcyactive but receivership firms are also considered

to have failed In the Chinese data ST (special treatment) and delisted firms are also separately

analyzed as failed firms

The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very

satisfactorily in an international context The effects of the four financial ratios on performance

are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small

contribution in re-estimation The original model performs very well in several countries such as

Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only

marginally improved the classification performance thus weakly supporting the obsolescence

hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust

across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion

holds for the re-estimation of the model using LRA because the performance results are very

similar with to those of MDA (H2) The use of additional variables in the model generally

improvesd the classification accuracy of the original model but the results for countries are

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

27

Denmark Inactive (no precision) Greece Active (receivership) In liquidation Bankruptcy Ireland In liquidation Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership) In liquidation Bankruptcy Ukraine In liquidation Bankruptcy UK liquidation set In liquidation China ST Active (special treatment) China delisted DL Active (delisted) If no such category for of failed firms could be identified that country was excluded from the

study (for example Switzerland) If a country had only a very small number of failed firms it was

dropped from the study (Luxembourg Liechtenstein Montenegro typically small countries

including Luxembourg Liechtenstein and Montenegro) It should also be noted that the status

classes (including the bankruptcy category) are not completely homogenous within European

countries due to different legislations although there are obvious similarities in insolvency acts

(Philippe et al 2002) China is a special case because it includes samples with three different

criteria of failure (bankruptcy special treatment and delisted) Additionally for the UK there are

two different samples (liquidation and receivership)

53 Statistical methods

In this study seven research hypotheses are drawn for statistical testing The statistical analysis

will begins with calculating the original Zrsquorsquo-Score for the firms in the data as in equation (3) The

classification performance of the original model is assessed by the AUC (Area Under Curve)

measure extracted from the ROC (Receiver Operating Characteristic) curve AUC has a close

connection with the Accuracy Ratio (AR) because AR = 2 AUC ndash 1 AR equals 0 for a random

28

model and 05 for a model with an average classification performance In SAS software is used

for all statistical analysess SAS software is used

The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is

tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical

method (multiple discriminant analysis or MDA) The problem is that the estimation sample

includes different numbers of failed and non-failed firms from 31 countries In the original Z-

Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from

the USUS Following the characteristics of these data we weight the firms so that the weights

for the failed and non-failed firms are equally In so doingthis way the non-proportional

sampling in from different countries does will not affect the re-estimated model The number of

firms from different countries however varies significantly leading to greater weights for larger

countries To avoid this problem the observations are also weighted so that each country has an

equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using

these weighted data and the resulting AUC is compared with that based on the original model

The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score

model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is

based on less- restrictive statistical assumptions than MDA In this estimation the dependent

variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA

does not require that independent variables be multivariate normal or that groups have equal

covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA

29

creates a score (logit) score L for every firm It is assumed that the independent variables are

linearly related to L This score or logit is used to determine the conditional probability of failure

as follows

= (eq 4)

where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the

original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing

the statistical significance of the difference between AUCs for this LR model and for the re-

estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it

is used as a benchmark for further statistical AUC comparisons because LR is applied as the

principal method in the testing of the remaining research hypotheses

The third hypothesis (H3) is associated with the performance effect of taking account of the

bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on

the following logit

(eq 5)

where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model

bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and

D1 = 1 when year = 2008 0 otherwise

D2 = 1 when year = 2009 0 otherwise

D3 = 1 when year = 2010 0 otherwise

)|1( XYp )( 441101

1

1

1XbXbbL ee

3

1

4

10

jjji

ii DcXbbL

30

The dummy variables do not directly refer to the bankruptcy year which that is not given in the

data basedatabase but rather to the last available year before bankruptcy For failed firms there

is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is

the base category If the AUC of this extended LR model statistically significantly exceeds the AUC

of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3

Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third

hypothesis above However for each hypothesis appropriate variables are used instead of the

year dummies Hypothesis H4 addresses the performance effect of taking size into account the

size and it will beis tested using by adding two additional variables measuring firm size in to the

LR -model In this LR model size is measured by the natural logarithm of total assets and its

squared form In this way the effect of logarithmic size can be reflected by in a function following

the second-order parabola Hypothesis H5 predicts that the classification performance improves

when the age of the firm is explicitly taken into account When testing this hypothesis the

category 6ndash14 years category is used as the base category and two dummy variables are

incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis

H6 looks at whether is associated with the classification performance is affected bythe effect of

the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt

is tested here using dummy variables for seven industries (D1 restaurants and hotels D2

construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and

water production D7 information technology) with all other industries acting as the base

category

31

Hypothesis H7 predicts that the explicit consideration of the country of origin will improves

classification performance This hypothesis is tested by using country risk measures instead ofnot

using dummy variables for countries but using country risk measures instead The country risk of

each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the

annual closing of accounts The rating is numerically recoded such that the best rating AAA

equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating

D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial

ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-

modelLR models with the original four financial ratios and the additional variables specified in

the hypotheses are estimated for all data In addition an LR model including all additional

variables is estimated for all data to assess the simultaneous effect of all variables Finally six of

the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not

included in this country- level testing hypothesis H7 is not tested because the additional variable

(country risk) is constant within the country

6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models

Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score

model for all data The variation in the ratios is significant as shown by the standard deviation

and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-

failed firms are close to each other indicating a symmetry of distributions However this is not

32

the case for the failed firms For the faileddistressed firms the median exceeds the mean for

these three ratios indicating negatively skewed distributions For X4 (BVETD) the means

significantly exceed the median for both failed and non-failed firms indicating a positively

skewed distribution For each of the four variables both the mean and the median are higher for

non-failed firms than for failed firms which is consistent with the expectations The difference

between the means of the non-failed and failed firms is larger in the original USUS data than in

our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is

approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of

EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may

indicate lower classification accuracy than in the original sample

(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates

(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the

large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model

Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same

statistical method or MDA The coefficients here are negative because the models are estimated

using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight

than in the original USUS data while the weights of WCTA and BVETD have proportionally

decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the

logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR

model These coefficients are directly comparable with those of the MDA model which isas

expected for the this exceptionally large sample The differences in the coefficients of the original

33

four variables among the eight LR-modelLR models (Models 1-8) are small For each model the

coefficient of BVETD is very close to zero This indicatinges that the original four variables and

the additional variables are quite independent of each other

(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-

modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate

that after 2007 (the base category) this risk of failure significantly decreased year by year The

base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due

to as a result of the global financial crisis The crisis played a significant role in the failure of key

businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The

effects were especially felt in Europe The coefficients of Model 4 for the size variables show that

the contribution of size on to the logit (risk measure) is atreaches its maximum value when

logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model

5 confirms the riskiness of young firms because the risk of failure is very high for newly founded

firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The

coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky

industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is

statistically significant (because of the large sample) but negative and very close to zero Finally

the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7

62 All data performance of the Zrsquorsquo-Score models

34

Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1

refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of

all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average

accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)

Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its

performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The

lower part of the table shows the AUCs for the countries included only in the test data The

performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)

samples It is also low and for the liquidation firms in the United Kingdom

The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5

presents the differences of these medians between the non-failed and failed firms by country

This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of

medians This correlation which is high for each financial ratio showing that the effects of the

ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large

except for EBITTA which This implies with the exceptionally high AUC that the ST firms

systematically differ from the non-ST firms although the differences are not extremely large The

differences between the medians are very large in Poland for each ratio justifying the high AUC

They are also large and in Finland and in the Czech Republic where the difference in EBITTA is

average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in

all four ratios are below the average a result thatwhich is obviously associated with a low AUC

35

In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even

negative In Iceland and Ireland the differences only in EBITTA are exceptionally small

(Table 4 here)

(Table 5 here)

Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by

MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)

supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is

at approximately the average level The re-estimation of the coefficients has led to improved

classification accuracy in a number of countries especially in Bosnia China (ST) Norway and

Greece However it has impaired the classification accuracy in the UK and China (delisted)

Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-

modelLR model) showing the effect of the estimation method For the benchmark model the

AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between

among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR

model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country

This result was expected because the coefficients of the models are directly comparable The

similar results for the models may also indicate that the independent variables conform to

multinormality This last latter result confirms what most researchers in the field of default

classification models have concluded that the accuracy levels of MDA and logistical regression

36

models are extremely similar despite any statistical violations such as normality between MDA

and logistical regression models

Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)

than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects

are not positive for all countries The effects are positive for example for Russia Estonia

Germany Ireland and Latvia but these effects are not statistically significant There are

statistically significant negative effects for China (ST) and Serbia There are notable negative

effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are

due to the exceptional annual distributions of failed firms in these countries For the countries

with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the

countries with positive effects it is only a few percent For each group non-failed firms are quite

equally distributed over years When the coefficient of D3 is very low (-0666) it strongly

decreases the risk estimates of most failed firms in the former countries but of only a few failed

firms in the latter countries This which leads to the observed effects H3 is supported by

the level of the whole sample However in some country samples (at the level of individual

levelies) H3 is not supported

Model 4 (LR-modelLR model with size variables) performs better than the benchmark model

which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads

and to significant improvements in the AUC for eg China (delisted) and Austria For China

(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC

37

also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs

in a few countries but thise decrease is not significant Model 4 is based on the four original

variables and the size effect following a second-order parabola This kind of size effect is very

small for micro firms but increases when approaching its maximum value in middle-sized firms

The countries with a positive change in AUC typically have data where in which the percent of

failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized

failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In

contrast the countries with a negative change in AUC have data where in which the percentage

of failed micro firms is exceptionally high (70-80)

Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly

the same AUC as the benchmark model (AUC = 0748) However the difference is positive and

statistically significant giving at least marginal support to H5 (age effect) For nearly all countries

the effect of the age on AUC is small For Austria however thise effect is however positive and

significant For Austria the percentage of non-failed young firms (less than 6 years) among non-

failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed

firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong

positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only

a few non-failed firms which This obviously leads to an improvement in AUC

Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC

supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its

38

AUC is notably higher than the benchmark AUC only in only a few countries such asfor example

France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg

restaurants and hotels and the information technology industryies but it has a positive effect

for on the construction and manufacturing industries For the countries with a positive effect on

AUC the percent of non-failed firms in restaurants and hotels and information technology is

high while that of failed firms is low For the risky industries (construction and manufacturing)

these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many

failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)

firms Therefore the AUC will be increasesd This situation is reversed for the countries with a

negative effect on AUC (Austria and Slovenia) The samples of these countries include a high

percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC

Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher

classification performance (AUC = 0749) than the benchmark model This result gives only very

weak support for H7 (country of origin effect) However for each country the resultant AUC is

nearly identical to that given by the benchmark model This result was expected due to the

negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7

This result implies that country risk hasdoes not have noany effect on the boundary between

bankrupt and non-bankrupt firms

Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional

variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to

39

the benchmark AUC in all test data However the effect on the AUCs largely varies and is either

negative or positive in different countries The positive effect is large in several countries such

as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative

effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show

that the inclusion of additional variables in the original model will usually increase the AUC but

not in every country

The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with

the USUS firms being unlike the majority of other firms in this study being listed or delisted

companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese

firm samples is associated with very small differences between the medians of the non-failed and

failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable

with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed

firms is very high Prior studies based on Chinese ST -firms have also demonstrated good

predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for

additional research and modeling work regarding unlisted and delisted Chinese firms

63 Country-level data performance of the Zrsquorsquo-Score models

The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform

all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the

different models estimated for each country separately (country-level models) In this table the

ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated

40

from the country data this benchmark will beis clearly outperformed by the resulting MDA

(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and

Sweden) However these results give only weak support for H1 at the country level because the

effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and

2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs

given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed

by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation

method)

Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC

than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3

leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United

StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in

nearly every country supporting H4 This improvement is significant however only in the United

StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark

model but for which it is only 0710 for the benchmark model It This improvement is also notable

in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age

category dummies) gives higher AUCs than the benchmark model for several countries but none

of the improvements are significant The positive effect is particularly strong especially in

Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only

weakly supports H5 because these negative effects are relatively small

41

Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects

on AUCs when compared with the benchmark However the only significant effect is the negative

effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the

industry dummies However there are notable positive effects on the AUC in for example

France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak

support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in

AUC compared with the benchmark model in Colombia Iceland and the Unites States It also

leads and to notable improvements in AUC at least in Belgium Denmark Estonia France

Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are

found only in Austria and Slovenia Thus as a conclusion we can concludestate that the

classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can

remarkably increases in most countries when different effects are taken into account by

additional variables This increase is found in most European countries and in Colombia and the

United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-

modelLR model and it can only be slightly improved only slightly by additional variables

(Table 6 here)

7 Summary of the study and a suggested extension

The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model

originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is

assessed using a very large international data set We test how the original version of the Zrsquorsquo-

42

Score model performs in different countries and how re-estimations using another statistical

method and different additional variables affect the classification performance when the data

are very heterogeneous For this kind of testing seven research hypotheses on classification

performance are extractedformulated These research hypotheses are tested for all data and

separately for country data (country-level analysis) The estimation data are from 31 countries

and the results are validated for 34 countries The countries are mainly from Europe but three

non-European countries are included (China Colombia and the United StatesUS) The statuses

used in the classification are mainly bankruptcyactive but receivership firms are also considered

to have failed In the Chinese data ST (special treatment) and delisted firms are also separately

analyzed as failed firms

The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very

satisfactorily in an international context The effects of the four financial ratios on performance

are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small

contribution in re-estimation The original model performs very well in several countries such as

Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only

marginally improved the classification performance thus weakly supporting the obsolescence

hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust

across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion

holds for the re-estimation of the model using LRA because the performance results are very

similar with to those of MDA (H2) The use of additional variables in the model generally

improvesd the classification accuracy of the original model but the results for countries are

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

28

model and 05 for a model with an average classification performance In SAS software is used

for all statistical analysess SAS software is used

The first hypothesis (H1) assumes that the coefficients of the original model are obsolete H1 is

tested by re-estimating the coefficients of the Zrsquorsquo-Score model using the original statistical

method (multiple discriminant analysis or MDA) The problem is that the estimation sample

includes different numbers of failed and non-failed firms from 31 countries In the original Z-

Scorersquorsquo sample (1983) equal numbers of bankrupt and non-bankrupt firms were selected from

the USUS Following the characteristics of these data we weight the firms so that the weights

for the failed and non-failed firms are equally In so doingthis way the non-proportional

sampling in from different countries does will not affect the re-estimated model The number of

firms from different countries however varies significantly leading to greater weights for larger

countries To avoid this problem the observations are also weighted so that each country has an

equal weight in the analysis Then the coefficients of the Zrsquorsquo-Score model are re-estimated using

these weighted data and the resulting AUC is compared with that based on the original model

The second hypothesis (H2) expects the classification performance of the re-estimated Zrsquorsquo-Score

model to improve when it is re-estimated by using the logistic regression analysis (LRA) which is

based on less- restrictive statistical assumptions than MDA In this estimation the dependent

variable Y = 0 when for the non-failed firms is non-failed and Y = 1 when it isfor failed firms LRA

does not require that independent variables be multivariate normal or that groups have equal

covariance matrices which are basic assumptions in MDA (Hosmer and Lemeshow 1989) LRA

29

creates a score (logit) score L for every firm It is assumed that the independent variables are

linearly related to L This score or logit is used to determine the conditional probability of failure

as follows

= (eq 4)

where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the

original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing

the statistical significance of the difference between AUCs for this LR model and for the re-

estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it

is used as a benchmark for further statistical AUC comparisons because LR is applied as the

principal method in the testing of the remaining research hypotheses

The third hypothesis (H3) is associated with the performance effect of taking account of the

bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on

the following logit

(eq 5)

where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model

bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and

D1 = 1 when year = 2008 0 otherwise

D2 = 1 when year = 2009 0 otherwise

D3 = 1 when year = 2010 0 otherwise

)|1( XYp )( 441101

1

1

1XbXbbL ee

3

1

4

10

jjji

ii DcXbbL

30

The dummy variables do not directly refer to the bankruptcy year which that is not given in the

data basedatabase but rather to the last available year before bankruptcy For failed firms there

is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is

the base category If the AUC of this extended LR model statistically significantly exceeds the AUC

of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3

Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third

hypothesis above However for each hypothesis appropriate variables are used instead of the

year dummies Hypothesis H4 addresses the performance effect of taking size into account the

size and it will beis tested using by adding two additional variables measuring firm size in to the

LR -model In this LR model size is measured by the natural logarithm of total assets and its

squared form In this way the effect of logarithmic size can be reflected by in a function following

the second-order parabola Hypothesis H5 predicts that the classification performance improves

when the age of the firm is explicitly taken into account When testing this hypothesis the

category 6ndash14 years category is used as the base category and two dummy variables are

incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis

H6 looks at whether is associated with the classification performance is affected bythe effect of

the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt

is tested here using dummy variables for seven industries (D1 restaurants and hotels D2

construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and

water production D7 information technology) with all other industries acting as the base

category

31

Hypothesis H7 predicts that the explicit consideration of the country of origin will improves

classification performance This hypothesis is tested by using country risk measures instead ofnot

using dummy variables for countries but using country risk measures instead The country risk of

each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the

annual closing of accounts The rating is numerically recoded such that the best rating AAA

equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating

D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial

ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-

modelLR models with the original four financial ratios and the additional variables specified in

the hypotheses are estimated for all data In addition an LR model including all additional

variables is estimated for all data to assess the simultaneous effect of all variables Finally six of

the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not

included in this country- level testing hypothesis H7 is not tested because the additional variable

(country risk) is constant within the country

6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models

Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score

model for all data The variation in the ratios is significant as shown by the standard deviation

and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-

failed firms are close to each other indicating a symmetry of distributions However this is not

32

the case for the failed firms For the faileddistressed firms the median exceeds the mean for

these three ratios indicating negatively skewed distributions For X4 (BVETD) the means

significantly exceed the median for both failed and non-failed firms indicating a positively

skewed distribution For each of the four variables both the mean and the median are higher for

non-failed firms than for failed firms which is consistent with the expectations The difference

between the means of the non-failed and failed firms is larger in the original USUS data than in

our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is

approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of

EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may

indicate lower classification accuracy than in the original sample

(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates

(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the

large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model

Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same

statistical method or MDA The coefficients here are negative because the models are estimated

using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight

than in the original USUS data while the weights of WCTA and BVETD have proportionally

decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the

logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR

model These coefficients are directly comparable with those of the MDA model which isas

expected for the this exceptionally large sample The differences in the coefficients of the original

33

four variables among the eight LR-modelLR models (Models 1-8) are small For each model the

coefficient of BVETD is very close to zero This indicatinges that the original four variables and

the additional variables are quite independent of each other

(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-

modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate

that after 2007 (the base category) this risk of failure significantly decreased year by year The

base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due

to as a result of the global financial crisis The crisis played a significant role in the failure of key

businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The

effects were especially felt in Europe The coefficients of Model 4 for the size variables show that

the contribution of size on to the logit (risk measure) is atreaches its maximum value when

logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model

5 confirms the riskiness of young firms because the risk of failure is very high for newly founded

firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The

coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky

industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is

statistically significant (because of the large sample) but negative and very close to zero Finally

the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7

62 All data performance of the Zrsquorsquo-Score models

34

Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1

refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of

all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average

accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)

Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its

performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The

lower part of the table shows the AUCs for the countries included only in the test data The

performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)

samples It is also low and for the liquidation firms in the United Kingdom

The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5

presents the differences of these medians between the non-failed and failed firms by country

This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of

medians This correlation which is high for each financial ratio showing that the effects of the

ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large

except for EBITTA which This implies with the exceptionally high AUC that the ST firms

systematically differ from the non-ST firms although the differences are not extremely large The

differences between the medians are very large in Poland for each ratio justifying the high AUC

They are also large and in Finland and in the Czech Republic where the difference in EBITTA is

average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in

all four ratios are below the average a result thatwhich is obviously associated with a low AUC

35

In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even

negative In Iceland and Ireland the differences only in EBITTA are exceptionally small

(Table 4 here)

(Table 5 here)

Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by

MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)

supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is

at approximately the average level The re-estimation of the coefficients has led to improved

classification accuracy in a number of countries especially in Bosnia China (ST) Norway and

Greece However it has impaired the classification accuracy in the UK and China (delisted)

Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-

modelLR model) showing the effect of the estimation method For the benchmark model the

AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between

among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR

model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country

This result was expected because the coefficients of the models are directly comparable The

similar results for the models may also indicate that the independent variables conform to

multinormality This last latter result confirms what most researchers in the field of default

classification models have concluded that the accuracy levels of MDA and logistical regression

36

models are extremely similar despite any statistical violations such as normality between MDA

and logistical regression models

Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)

than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects

are not positive for all countries The effects are positive for example for Russia Estonia

Germany Ireland and Latvia but these effects are not statistically significant There are

statistically significant negative effects for China (ST) and Serbia There are notable negative

effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are

due to the exceptional annual distributions of failed firms in these countries For the countries

with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the

countries with positive effects it is only a few percent For each group non-failed firms are quite

equally distributed over years When the coefficient of D3 is very low (-0666) it strongly

decreases the risk estimates of most failed firms in the former countries but of only a few failed

firms in the latter countries This which leads to the observed effects H3 is supported by

the level of the whole sample However in some country samples (at the level of individual

levelies) H3 is not supported

Model 4 (LR-modelLR model with size variables) performs better than the benchmark model

which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads

and to significant improvements in the AUC for eg China (delisted) and Austria For China

(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC

37

also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs

in a few countries but thise decrease is not significant Model 4 is based on the four original

variables and the size effect following a second-order parabola This kind of size effect is very

small for micro firms but increases when approaching its maximum value in middle-sized firms

The countries with a positive change in AUC typically have data where in which the percent of

failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized

failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In

contrast the countries with a negative change in AUC have data where in which the percentage

of failed micro firms is exceptionally high (70-80)

Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly

the same AUC as the benchmark model (AUC = 0748) However the difference is positive and

statistically significant giving at least marginal support to H5 (age effect) For nearly all countries

the effect of the age on AUC is small For Austria however thise effect is however positive and

significant For Austria the percentage of non-failed young firms (less than 6 years) among non-

failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed

firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong

positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only

a few non-failed firms which This obviously leads to an improvement in AUC

Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC

supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its

38

AUC is notably higher than the benchmark AUC only in only a few countries such asfor example

France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg

restaurants and hotels and the information technology industryies but it has a positive effect

for on the construction and manufacturing industries For the countries with a positive effect on

AUC the percent of non-failed firms in restaurants and hotels and information technology is

high while that of failed firms is low For the risky industries (construction and manufacturing)

these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many

failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)

firms Therefore the AUC will be increasesd This situation is reversed for the countries with a

negative effect on AUC (Austria and Slovenia) The samples of these countries include a high

percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC

Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher

classification performance (AUC = 0749) than the benchmark model This result gives only very

weak support for H7 (country of origin effect) However for each country the resultant AUC is

nearly identical to that given by the benchmark model This result was expected due to the

negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7

This result implies that country risk hasdoes not have noany effect on the boundary between

bankrupt and non-bankrupt firms

Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional

variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to

39

the benchmark AUC in all test data However the effect on the AUCs largely varies and is either

negative or positive in different countries The positive effect is large in several countries such

as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative

effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show

that the inclusion of additional variables in the original model will usually increase the AUC but

not in every country

The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with

the USUS firms being unlike the majority of other firms in this study being listed or delisted

companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese

firm samples is associated with very small differences between the medians of the non-failed and

failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable

with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed

firms is very high Prior studies based on Chinese ST -firms have also demonstrated good

predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for

additional research and modeling work regarding unlisted and delisted Chinese firms

63 Country-level data performance of the Zrsquorsquo-Score models

The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform

all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the

different models estimated for each country separately (country-level models) In this table the

ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated

40

from the country data this benchmark will beis clearly outperformed by the resulting MDA

(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and

Sweden) However these results give only weak support for H1 at the country level because the

effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and

2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs

given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed

by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation

method)

Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC

than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3

leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United

StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in

nearly every country supporting H4 This improvement is significant however only in the United

StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark

model but for which it is only 0710 for the benchmark model It This improvement is also notable

in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age

category dummies) gives higher AUCs than the benchmark model for several countries but none

of the improvements are significant The positive effect is particularly strong especially in

Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only

weakly supports H5 because these negative effects are relatively small

41

Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects

on AUCs when compared with the benchmark However the only significant effect is the negative

effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the

industry dummies However there are notable positive effects on the AUC in for example

France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak

support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in

AUC compared with the benchmark model in Colombia Iceland and the Unites States It also

leads and to notable improvements in AUC at least in Belgium Denmark Estonia France

Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are

found only in Austria and Slovenia Thus as a conclusion we can concludestate that the

classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can

remarkably increases in most countries when different effects are taken into account by

additional variables This increase is found in most European countries and in Colombia and the

United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-

modelLR model and it can only be slightly improved only slightly by additional variables

(Table 6 here)

7 Summary of the study and a suggested extension

The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model

originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is

assessed using a very large international data set We test how the original version of the Zrsquorsquo-

42

Score model performs in different countries and how re-estimations using another statistical

method and different additional variables affect the classification performance when the data

are very heterogeneous For this kind of testing seven research hypotheses on classification

performance are extractedformulated These research hypotheses are tested for all data and

separately for country data (country-level analysis) The estimation data are from 31 countries

and the results are validated for 34 countries The countries are mainly from Europe but three

non-European countries are included (China Colombia and the United StatesUS) The statuses

used in the classification are mainly bankruptcyactive but receivership firms are also considered

to have failed In the Chinese data ST (special treatment) and delisted firms are also separately

analyzed as failed firms

The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very

satisfactorily in an international context The effects of the four financial ratios on performance

are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small

contribution in re-estimation The original model performs very well in several countries such as

Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only

marginally improved the classification performance thus weakly supporting the obsolescence

hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust

across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion

holds for the re-estimation of the model using LRA because the performance results are very

similar with to those of MDA (H2) The use of additional variables in the model generally

improvesd the classification accuracy of the original model but the results for countries are

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

29

creates a score (logit) score L for every firm It is assumed that the independent variables are

linearly related to L This score or logit is used to determine the conditional probability of failure

as follows

= (eq 4)

where bi (i =0hellip 4) are the coefficients and Xi (i =1hellip 4) are the four independent variables of the

original Zrsquorsquo-Model The effect of thise method on classification performance is assessed by testing

the statistical significance of the difference between AUCs for this LR model and for the re-

estimated MDA model The resulting model is here called the Zrsquorsquo-Score LR-ModelLR model and it

is used as a benchmark for further statistical AUC comparisons because LR is applied as the

principal method in the testing of the remaining research hypotheses

The third hypothesis (H3) is associated with the performance effect of taking account of the

bankruptcy year in the estimation This hypothesis is tested by estimating an LR model based on

the following logit

(eq 5)

where b0 is a constant Xi (i =1hellip 4) are the four independent variables of the original Zrsquorsquo-Model

bi (i =1hellip 4) are their coefficients cj (j =1hellip 3) are coefficients of the dummy variables and

D1 = 1 when year = 2008 0 otherwise

D2 = 1 when year = 2009 0 otherwise

D3 = 1 when year = 2010 0 otherwise

)|1( XYp )( 441101

1

1

1XbXbbL ee

3

1

4

10

jjji

ii DcXbbL

30

The dummy variables do not directly refer to the bankruptcy year which that is not given in the

data basedatabase but rather to the last available year before bankruptcy For failed firms there

is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is

the base category If the AUC of this extended LR model statistically significantly exceeds the AUC

of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3

Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third

hypothesis above However for each hypothesis appropriate variables are used instead of the

year dummies Hypothesis H4 addresses the performance effect of taking size into account the

size and it will beis tested using by adding two additional variables measuring firm size in to the

LR -model In this LR model size is measured by the natural logarithm of total assets and its

squared form In this way the effect of logarithmic size can be reflected by in a function following

the second-order parabola Hypothesis H5 predicts that the classification performance improves

when the age of the firm is explicitly taken into account When testing this hypothesis the

category 6ndash14 years category is used as the base category and two dummy variables are

incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis

H6 looks at whether is associated with the classification performance is affected bythe effect of

the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt

is tested here using dummy variables for seven industries (D1 restaurants and hotels D2

construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and

water production D7 information technology) with all other industries acting as the base

category

31

Hypothesis H7 predicts that the explicit consideration of the country of origin will improves

classification performance This hypothesis is tested by using country risk measures instead ofnot

using dummy variables for countries but using country risk measures instead The country risk of

each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the

annual closing of accounts The rating is numerically recoded such that the best rating AAA

equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating

D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial

ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-

modelLR models with the original four financial ratios and the additional variables specified in

the hypotheses are estimated for all data In addition an LR model including all additional

variables is estimated for all data to assess the simultaneous effect of all variables Finally six of

the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not

included in this country- level testing hypothesis H7 is not tested because the additional variable

(country risk) is constant within the country

6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models

Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score

model for all data The variation in the ratios is significant as shown by the standard deviation

and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-

failed firms are close to each other indicating a symmetry of distributions However this is not

32

the case for the failed firms For the faileddistressed firms the median exceeds the mean for

these three ratios indicating negatively skewed distributions For X4 (BVETD) the means

significantly exceed the median for both failed and non-failed firms indicating a positively

skewed distribution For each of the four variables both the mean and the median are higher for

non-failed firms than for failed firms which is consistent with the expectations The difference

between the means of the non-failed and failed firms is larger in the original USUS data than in

our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is

approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of

EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may

indicate lower classification accuracy than in the original sample

(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates

(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the

large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model

Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same

statistical method or MDA The coefficients here are negative because the models are estimated

using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight

than in the original USUS data while the weights of WCTA and BVETD have proportionally

decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the

logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR

model These coefficients are directly comparable with those of the MDA model which isas

expected for the this exceptionally large sample The differences in the coefficients of the original

33

four variables among the eight LR-modelLR models (Models 1-8) are small For each model the

coefficient of BVETD is very close to zero This indicatinges that the original four variables and

the additional variables are quite independent of each other

(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-

modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate

that after 2007 (the base category) this risk of failure significantly decreased year by year The

base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due

to as a result of the global financial crisis The crisis played a significant role in the failure of key

businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The

effects were especially felt in Europe The coefficients of Model 4 for the size variables show that

the contribution of size on to the logit (risk measure) is atreaches its maximum value when

logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model

5 confirms the riskiness of young firms because the risk of failure is very high for newly founded

firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The

coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky

industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is

statistically significant (because of the large sample) but negative and very close to zero Finally

the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7

62 All data performance of the Zrsquorsquo-Score models

34

Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1

refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of

all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average

accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)

Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its

performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The

lower part of the table shows the AUCs for the countries included only in the test data The

performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)

samples It is also low and for the liquidation firms in the United Kingdom

The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5

presents the differences of these medians between the non-failed and failed firms by country

This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of

medians This correlation which is high for each financial ratio showing that the effects of the

ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large

except for EBITTA which This implies with the exceptionally high AUC that the ST firms

systematically differ from the non-ST firms although the differences are not extremely large The

differences between the medians are very large in Poland for each ratio justifying the high AUC

They are also large and in Finland and in the Czech Republic where the difference in EBITTA is

average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in

all four ratios are below the average a result thatwhich is obviously associated with a low AUC

35

In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even

negative In Iceland and Ireland the differences only in EBITTA are exceptionally small

(Table 4 here)

(Table 5 here)

Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by

MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)

supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is

at approximately the average level The re-estimation of the coefficients has led to improved

classification accuracy in a number of countries especially in Bosnia China (ST) Norway and

Greece However it has impaired the classification accuracy in the UK and China (delisted)

Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-

modelLR model) showing the effect of the estimation method For the benchmark model the

AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between

among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR

model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country

This result was expected because the coefficients of the models are directly comparable The

similar results for the models may also indicate that the independent variables conform to

multinormality This last latter result confirms what most researchers in the field of default

classification models have concluded that the accuracy levels of MDA and logistical regression

36

models are extremely similar despite any statistical violations such as normality between MDA

and logistical regression models

Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)

than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects

are not positive for all countries The effects are positive for example for Russia Estonia

Germany Ireland and Latvia but these effects are not statistically significant There are

statistically significant negative effects for China (ST) and Serbia There are notable negative

effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are

due to the exceptional annual distributions of failed firms in these countries For the countries

with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the

countries with positive effects it is only a few percent For each group non-failed firms are quite

equally distributed over years When the coefficient of D3 is very low (-0666) it strongly

decreases the risk estimates of most failed firms in the former countries but of only a few failed

firms in the latter countries This which leads to the observed effects H3 is supported by

the level of the whole sample However in some country samples (at the level of individual

levelies) H3 is not supported

Model 4 (LR-modelLR model with size variables) performs better than the benchmark model

which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads

and to significant improvements in the AUC for eg China (delisted) and Austria For China

(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC

37

also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs

in a few countries but thise decrease is not significant Model 4 is based on the four original

variables and the size effect following a second-order parabola This kind of size effect is very

small for micro firms but increases when approaching its maximum value in middle-sized firms

The countries with a positive change in AUC typically have data where in which the percent of

failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized

failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In

contrast the countries with a negative change in AUC have data where in which the percentage

of failed micro firms is exceptionally high (70-80)

Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly

the same AUC as the benchmark model (AUC = 0748) However the difference is positive and

statistically significant giving at least marginal support to H5 (age effect) For nearly all countries

the effect of the age on AUC is small For Austria however thise effect is however positive and

significant For Austria the percentage of non-failed young firms (less than 6 years) among non-

failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed

firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong

positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only

a few non-failed firms which This obviously leads to an improvement in AUC

Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC

supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its

38

AUC is notably higher than the benchmark AUC only in only a few countries such asfor example

France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg

restaurants and hotels and the information technology industryies but it has a positive effect

for on the construction and manufacturing industries For the countries with a positive effect on

AUC the percent of non-failed firms in restaurants and hotels and information technology is

high while that of failed firms is low For the risky industries (construction and manufacturing)

these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many

failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)

firms Therefore the AUC will be increasesd This situation is reversed for the countries with a

negative effect on AUC (Austria and Slovenia) The samples of these countries include a high

percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC

Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher

classification performance (AUC = 0749) than the benchmark model This result gives only very

weak support for H7 (country of origin effect) However for each country the resultant AUC is

nearly identical to that given by the benchmark model This result was expected due to the

negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7

This result implies that country risk hasdoes not have noany effect on the boundary between

bankrupt and non-bankrupt firms

Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional

variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to

39

the benchmark AUC in all test data However the effect on the AUCs largely varies and is either

negative or positive in different countries The positive effect is large in several countries such

as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative

effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show

that the inclusion of additional variables in the original model will usually increase the AUC but

not in every country

The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with

the USUS firms being unlike the majority of other firms in this study being listed or delisted

companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese

firm samples is associated with very small differences between the medians of the non-failed and

failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable

with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed

firms is very high Prior studies based on Chinese ST -firms have also demonstrated good

predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for

additional research and modeling work regarding unlisted and delisted Chinese firms

63 Country-level data performance of the Zrsquorsquo-Score models

The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform

all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the

different models estimated for each country separately (country-level models) In this table the

ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated

40

from the country data this benchmark will beis clearly outperformed by the resulting MDA

(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and

Sweden) However these results give only weak support for H1 at the country level because the

effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and

2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs

given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed

by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation

method)

Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC

than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3

leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United

StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in

nearly every country supporting H4 This improvement is significant however only in the United

StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark

model but for which it is only 0710 for the benchmark model It This improvement is also notable

in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age

category dummies) gives higher AUCs than the benchmark model for several countries but none

of the improvements are significant The positive effect is particularly strong especially in

Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only

weakly supports H5 because these negative effects are relatively small

41

Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects

on AUCs when compared with the benchmark However the only significant effect is the negative

effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the

industry dummies However there are notable positive effects on the AUC in for example

France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak

support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in

AUC compared with the benchmark model in Colombia Iceland and the Unites States It also

leads and to notable improvements in AUC at least in Belgium Denmark Estonia France

Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are

found only in Austria and Slovenia Thus as a conclusion we can concludestate that the

classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can

remarkably increases in most countries when different effects are taken into account by

additional variables This increase is found in most European countries and in Colombia and the

United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-

modelLR model and it can only be slightly improved only slightly by additional variables

(Table 6 here)

7 Summary of the study and a suggested extension

The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model

originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is

assessed using a very large international data set We test how the original version of the Zrsquorsquo-

42

Score model performs in different countries and how re-estimations using another statistical

method and different additional variables affect the classification performance when the data

are very heterogeneous For this kind of testing seven research hypotheses on classification

performance are extractedformulated These research hypotheses are tested for all data and

separately for country data (country-level analysis) The estimation data are from 31 countries

and the results are validated for 34 countries The countries are mainly from Europe but three

non-European countries are included (China Colombia and the United StatesUS) The statuses

used in the classification are mainly bankruptcyactive but receivership firms are also considered

to have failed In the Chinese data ST (special treatment) and delisted firms are also separately

analyzed as failed firms

The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very

satisfactorily in an international context The effects of the four financial ratios on performance

are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small

contribution in re-estimation The original model performs very well in several countries such as

Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only

marginally improved the classification performance thus weakly supporting the obsolescence

hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust

across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion

holds for the re-estimation of the model using LRA because the performance results are very

similar with to those of MDA (H2) The use of additional variables in the model generally

improvesd the classification accuracy of the original model but the results for countries are

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

30

The dummy variables do not directly refer to the bankruptcy year which that is not given in the

data basedatabase but rather to the last available year before bankruptcy For failed firms there

is an approximately 1ndash2-year lead time to failure from this year In this model the year 2007 is

the base category If the AUC of this extended LR model statistically significantly exceeds the AUC

of the Zrsquorsquo-Score LR-modelLR model (benchmark) the evidence supports hypothesis H3

Research hypotheses H4ndashH7 are statistically tested using the same approach as with the third

hypothesis above However for each hypothesis appropriate variables are used instead of the

year dummies Hypothesis H4 addresses the performance effect of taking size into account the

size and it will beis tested using by adding two additional variables measuring firm size in to the

LR -model In this LR model size is measured by the natural logarithm of total assets and its

squared form In this way the effect of logarithmic size can be reflected by in a function following

the second-order parabola Hypothesis H5 predicts that the classification performance improves

when the age of the firm is explicitly taken into account When testing this hypothesis the

category 6ndash14 years category is used as the base category and two dummy variables are

incorporated in the LR-modelLR model (D1 less than 6 years D2 15 years or more) Hypothesis

H6 looks at whether is associated with the classification performance is affected bythe effect of

the explicit consideration of industry on eaffectsthe classification performance The hypothesisIt

is tested here using dummy variables for seven industries (D1 restaurants and hotels D2

construction D3 wholesale and retailing D4 agriculture D5 manufacturing D6 energy and

water production D7 information technology) with all other industries acting as the base

category

31

Hypothesis H7 predicts that the explicit consideration of the country of origin will improves

classification performance This hypothesis is tested by using country risk measures instead ofnot

using dummy variables for countries but using country risk measures instead The country risk of

each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the

annual closing of accounts The rating is numerically recoded such that the best rating AAA

equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating

D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial

ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-

modelLR models with the original four financial ratios and the additional variables specified in

the hypotheses are estimated for all data In addition an LR model including all additional

variables is estimated for all data to assess the simultaneous effect of all variables Finally six of

the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not

included in this country- level testing hypothesis H7 is not tested because the additional variable

(country risk) is constant within the country

6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models

Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score

model for all data The variation in the ratios is significant as shown by the standard deviation

and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-

failed firms are close to each other indicating a symmetry of distributions However this is not

32

the case for the failed firms For the faileddistressed firms the median exceeds the mean for

these three ratios indicating negatively skewed distributions For X4 (BVETD) the means

significantly exceed the median for both failed and non-failed firms indicating a positively

skewed distribution For each of the four variables both the mean and the median are higher for

non-failed firms than for failed firms which is consistent with the expectations The difference

between the means of the non-failed and failed firms is larger in the original USUS data than in

our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is

approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of

EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may

indicate lower classification accuracy than in the original sample

(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates

(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the

large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model

Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same

statistical method or MDA The coefficients here are negative because the models are estimated

using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight

than in the original USUS data while the weights of WCTA and BVETD have proportionally

decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the

logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR

model These coefficients are directly comparable with those of the MDA model which isas

expected for the this exceptionally large sample The differences in the coefficients of the original

33

four variables among the eight LR-modelLR models (Models 1-8) are small For each model the

coefficient of BVETD is very close to zero This indicatinges that the original four variables and

the additional variables are quite independent of each other

(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-

modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate

that after 2007 (the base category) this risk of failure significantly decreased year by year The

base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due

to as a result of the global financial crisis The crisis played a significant role in the failure of key

businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The

effects were especially felt in Europe The coefficients of Model 4 for the size variables show that

the contribution of size on to the logit (risk measure) is atreaches its maximum value when

logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model

5 confirms the riskiness of young firms because the risk of failure is very high for newly founded

firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The

coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky

industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is

statistically significant (because of the large sample) but negative and very close to zero Finally

the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7

62 All data performance of the Zrsquorsquo-Score models

34

Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1

refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of

all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average

accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)

Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its

performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The

lower part of the table shows the AUCs for the countries included only in the test data The

performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)

samples It is also low and for the liquidation firms in the United Kingdom

The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5

presents the differences of these medians between the non-failed and failed firms by country

This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of

medians This correlation which is high for each financial ratio showing that the effects of the

ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large

except for EBITTA which This implies with the exceptionally high AUC that the ST firms

systematically differ from the non-ST firms although the differences are not extremely large The

differences between the medians are very large in Poland for each ratio justifying the high AUC

They are also large and in Finland and in the Czech Republic where the difference in EBITTA is

average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in

all four ratios are below the average a result thatwhich is obviously associated with a low AUC

35

In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even

negative In Iceland and Ireland the differences only in EBITTA are exceptionally small

(Table 4 here)

(Table 5 here)

Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by

MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)

supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is

at approximately the average level The re-estimation of the coefficients has led to improved

classification accuracy in a number of countries especially in Bosnia China (ST) Norway and

Greece However it has impaired the classification accuracy in the UK and China (delisted)

Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-

modelLR model) showing the effect of the estimation method For the benchmark model the

AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between

among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR

model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country

This result was expected because the coefficients of the models are directly comparable The

similar results for the models may also indicate that the independent variables conform to

multinormality This last latter result confirms what most researchers in the field of default

classification models have concluded that the accuracy levels of MDA and logistical regression

36

models are extremely similar despite any statistical violations such as normality between MDA

and logistical regression models

Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)

than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects

are not positive for all countries The effects are positive for example for Russia Estonia

Germany Ireland and Latvia but these effects are not statistically significant There are

statistically significant negative effects for China (ST) and Serbia There are notable negative

effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are

due to the exceptional annual distributions of failed firms in these countries For the countries

with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the

countries with positive effects it is only a few percent For each group non-failed firms are quite

equally distributed over years When the coefficient of D3 is very low (-0666) it strongly

decreases the risk estimates of most failed firms in the former countries but of only a few failed

firms in the latter countries This which leads to the observed effects H3 is supported by

the level of the whole sample However in some country samples (at the level of individual

levelies) H3 is not supported

Model 4 (LR-modelLR model with size variables) performs better than the benchmark model

which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads

and to significant improvements in the AUC for eg China (delisted) and Austria For China

(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC

37

also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs

in a few countries but thise decrease is not significant Model 4 is based on the four original

variables and the size effect following a second-order parabola This kind of size effect is very

small for micro firms but increases when approaching its maximum value in middle-sized firms

The countries with a positive change in AUC typically have data where in which the percent of

failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized

failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In

contrast the countries with a negative change in AUC have data where in which the percentage

of failed micro firms is exceptionally high (70-80)

Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly

the same AUC as the benchmark model (AUC = 0748) However the difference is positive and

statistically significant giving at least marginal support to H5 (age effect) For nearly all countries

the effect of the age on AUC is small For Austria however thise effect is however positive and

significant For Austria the percentage of non-failed young firms (less than 6 years) among non-

failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed

firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong

positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only

a few non-failed firms which This obviously leads to an improvement in AUC

Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC

supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its

38

AUC is notably higher than the benchmark AUC only in only a few countries such asfor example

France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg

restaurants and hotels and the information technology industryies but it has a positive effect

for on the construction and manufacturing industries For the countries with a positive effect on

AUC the percent of non-failed firms in restaurants and hotels and information technology is

high while that of failed firms is low For the risky industries (construction and manufacturing)

these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many

failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)

firms Therefore the AUC will be increasesd This situation is reversed for the countries with a

negative effect on AUC (Austria and Slovenia) The samples of these countries include a high

percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC

Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher

classification performance (AUC = 0749) than the benchmark model This result gives only very

weak support for H7 (country of origin effect) However for each country the resultant AUC is

nearly identical to that given by the benchmark model This result was expected due to the

negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7

This result implies that country risk hasdoes not have noany effect on the boundary between

bankrupt and non-bankrupt firms

Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional

variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to

39

the benchmark AUC in all test data However the effect on the AUCs largely varies and is either

negative or positive in different countries The positive effect is large in several countries such

as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative

effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show

that the inclusion of additional variables in the original model will usually increase the AUC but

not in every country

The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with

the USUS firms being unlike the majority of other firms in this study being listed or delisted

companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese

firm samples is associated with very small differences between the medians of the non-failed and

failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable

with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed

firms is very high Prior studies based on Chinese ST -firms have also demonstrated good

predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for

additional research and modeling work regarding unlisted and delisted Chinese firms

63 Country-level data performance of the Zrsquorsquo-Score models

The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform

all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the

different models estimated for each country separately (country-level models) In this table the

ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated

40

from the country data this benchmark will beis clearly outperformed by the resulting MDA

(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and

Sweden) However these results give only weak support for H1 at the country level because the

effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and

2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs

given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed

by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation

method)

Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC

than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3

leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United

StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in

nearly every country supporting H4 This improvement is significant however only in the United

StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark

model but for which it is only 0710 for the benchmark model It This improvement is also notable

in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age

category dummies) gives higher AUCs than the benchmark model for several countries but none

of the improvements are significant The positive effect is particularly strong especially in

Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only

weakly supports H5 because these negative effects are relatively small

41

Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects

on AUCs when compared with the benchmark However the only significant effect is the negative

effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the

industry dummies However there are notable positive effects on the AUC in for example

France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak

support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in

AUC compared with the benchmark model in Colombia Iceland and the Unites States It also

leads and to notable improvements in AUC at least in Belgium Denmark Estonia France

Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are

found only in Austria and Slovenia Thus as a conclusion we can concludestate that the

classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can

remarkably increases in most countries when different effects are taken into account by

additional variables This increase is found in most European countries and in Colombia and the

United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-

modelLR model and it can only be slightly improved only slightly by additional variables

(Table 6 here)

7 Summary of the study and a suggested extension

The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model

originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is

assessed using a very large international data set We test how the original version of the Zrsquorsquo-

42

Score model performs in different countries and how re-estimations using another statistical

method and different additional variables affect the classification performance when the data

are very heterogeneous For this kind of testing seven research hypotheses on classification

performance are extractedformulated These research hypotheses are tested for all data and

separately for country data (country-level analysis) The estimation data are from 31 countries

and the results are validated for 34 countries The countries are mainly from Europe but three

non-European countries are included (China Colombia and the United StatesUS) The statuses

used in the classification are mainly bankruptcyactive but receivership firms are also considered

to have failed In the Chinese data ST (special treatment) and delisted firms are also separately

analyzed as failed firms

The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very

satisfactorily in an international context The effects of the four financial ratios on performance

are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small

contribution in re-estimation The original model performs very well in several countries such as

Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only

marginally improved the classification performance thus weakly supporting the obsolescence

hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust

across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion

holds for the re-estimation of the model using LRA because the performance results are very

similar with to those of MDA (H2) The use of additional variables in the model generally

improvesd the classification accuracy of the original model but the results for countries are

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

31

Hypothesis H7 predicts that the explicit consideration of the country of origin will improves

classification performance This hypothesis is tested by using country risk measures instead ofnot

using dummy variables for countries but using country risk measures instead The country risk of

each country is measured by Standard amp Poorrsquos Country Risk Rating per six months after the

annual closing of accounts The rating is numerically recoded such that the best rating AAA

equals= 1 the second- best rating AA+ equals = 2 and so onet cetera Finally the lowest rating

D equals = 22 Thus H7 is tested by estimating an LR-modelLR model based on the four financial

ratios of the original Zrsquorsquo-Score model and a 22-step variable referring to country risk The five LR-

modelLR models with the original four financial ratios and the additional variables specified in

the hypotheses are estimated for all data In addition an LR model including all additional

variables is estimated for all data to assess the simultaneous effect of all variables Finally six of

the seven hypotheses are tested for the data of each country separately Hypothesis H7 Iis not

included in this country- level testing hypothesis H7 is not tested because the additional variable

(country risk) is constant within the country

6 Empirical results 61 All data coefficients of the Zrsquorsquo-Score models

Table 2 presents descriptive statistics of the four independent variables (X1-X4) of the Zrsquorsquo-Score

model for all data The variation in the ratios is significant as shown by the standard deviation

and the quartiles For X1 (WCTA) X2 (RETA) and X3 (EBITTA) the median and the mean for non-

failed firms are close to each other indicating a symmetry of distributions However this is not

32

the case for the failed firms For the faileddistressed firms the median exceeds the mean for

these three ratios indicating negatively skewed distributions For X4 (BVETD) the means

significantly exceed the median for both failed and non-failed firms indicating a positively

skewed distribution For each of the four variables both the mean and the median are higher for

non-failed firms than for failed firms which is consistent with the expectations The difference

between the means of the non-failed and failed firms is larger in the original USUS data than in

our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is

approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of

EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may

indicate lower classification accuracy than in the original sample

(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates

(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the

large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model

Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same

statistical method or MDA The coefficients here are negative because the models are estimated

using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight

than in the original USUS data while the weights of WCTA and BVETD have proportionally

decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the

logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR

model These coefficients are directly comparable with those of the MDA model which isas

expected for the this exceptionally large sample The differences in the coefficients of the original

33

four variables among the eight LR-modelLR models (Models 1-8) are small For each model the

coefficient of BVETD is very close to zero This indicatinges that the original four variables and

the additional variables are quite independent of each other

(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-

modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate

that after 2007 (the base category) this risk of failure significantly decreased year by year The

base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due

to as a result of the global financial crisis The crisis played a significant role in the failure of key

businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The

effects were especially felt in Europe The coefficients of Model 4 for the size variables show that

the contribution of size on to the logit (risk measure) is atreaches its maximum value when

logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model

5 confirms the riskiness of young firms because the risk of failure is very high for newly founded

firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The

coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky

industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is

statistically significant (because of the large sample) but negative and very close to zero Finally

the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7

62 All data performance of the Zrsquorsquo-Score models

34

Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1

refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of

all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average

accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)

Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its

performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The

lower part of the table shows the AUCs for the countries included only in the test data The

performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)

samples It is also low and for the liquidation firms in the United Kingdom

The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5

presents the differences of these medians between the non-failed and failed firms by country

This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of

medians This correlation which is high for each financial ratio showing that the effects of the

ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large

except for EBITTA which This implies with the exceptionally high AUC that the ST firms

systematically differ from the non-ST firms although the differences are not extremely large The

differences between the medians are very large in Poland for each ratio justifying the high AUC

They are also large and in Finland and in the Czech Republic where the difference in EBITTA is

average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in

all four ratios are below the average a result thatwhich is obviously associated with a low AUC

35

In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even

negative In Iceland and Ireland the differences only in EBITTA are exceptionally small

(Table 4 here)

(Table 5 here)

Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by

MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)

supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is

at approximately the average level The re-estimation of the coefficients has led to improved

classification accuracy in a number of countries especially in Bosnia China (ST) Norway and

Greece However it has impaired the classification accuracy in the UK and China (delisted)

Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-

modelLR model) showing the effect of the estimation method For the benchmark model the

AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between

among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR

model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country

This result was expected because the coefficients of the models are directly comparable The

similar results for the models may also indicate that the independent variables conform to

multinormality This last latter result confirms what most researchers in the field of default

classification models have concluded that the accuracy levels of MDA and logistical regression

36

models are extremely similar despite any statistical violations such as normality between MDA

and logistical regression models

Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)

than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects

are not positive for all countries The effects are positive for example for Russia Estonia

Germany Ireland and Latvia but these effects are not statistically significant There are

statistically significant negative effects for China (ST) and Serbia There are notable negative

effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are

due to the exceptional annual distributions of failed firms in these countries For the countries

with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the

countries with positive effects it is only a few percent For each group non-failed firms are quite

equally distributed over years When the coefficient of D3 is very low (-0666) it strongly

decreases the risk estimates of most failed firms in the former countries but of only a few failed

firms in the latter countries This which leads to the observed effects H3 is supported by

the level of the whole sample However in some country samples (at the level of individual

levelies) H3 is not supported

Model 4 (LR-modelLR model with size variables) performs better than the benchmark model

which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads

and to significant improvements in the AUC for eg China (delisted) and Austria For China

(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC

37

also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs

in a few countries but thise decrease is not significant Model 4 is based on the four original

variables and the size effect following a second-order parabola This kind of size effect is very

small for micro firms but increases when approaching its maximum value in middle-sized firms

The countries with a positive change in AUC typically have data where in which the percent of

failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized

failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In

contrast the countries with a negative change in AUC have data where in which the percentage

of failed micro firms is exceptionally high (70-80)

Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly

the same AUC as the benchmark model (AUC = 0748) However the difference is positive and

statistically significant giving at least marginal support to H5 (age effect) For nearly all countries

the effect of the age on AUC is small For Austria however thise effect is however positive and

significant For Austria the percentage of non-failed young firms (less than 6 years) among non-

failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed

firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong

positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only

a few non-failed firms which This obviously leads to an improvement in AUC

Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC

supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its

38

AUC is notably higher than the benchmark AUC only in only a few countries such asfor example

France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg

restaurants and hotels and the information technology industryies but it has a positive effect

for on the construction and manufacturing industries For the countries with a positive effect on

AUC the percent of non-failed firms in restaurants and hotels and information technology is

high while that of failed firms is low For the risky industries (construction and manufacturing)

these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many

failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)

firms Therefore the AUC will be increasesd This situation is reversed for the countries with a

negative effect on AUC (Austria and Slovenia) The samples of these countries include a high

percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC

Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher

classification performance (AUC = 0749) than the benchmark model This result gives only very

weak support for H7 (country of origin effect) However for each country the resultant AUC is

nearly identical to that given by the benchmark model This result was expected due to the

negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7

This result implies that country risk hasdoes not have noany effect on the boundary between

bankrupt and non-bankrupt firms

Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional

variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to

39

the benchmark AUC in all test data However the effect on the AUCs largely varies and is either

negative or positive in different countries The positive effect is large in several countries such

as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative

effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show

that the inclusion of additional variables in the original model will usually increase the AUC but

not in every country

The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with

the USUS firms being unlike the majority of other firms in this study being listed or delisted

companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese

firm samples is associated with very small differences between the medians of the non-failed and

failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable

with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed

firms is very high Prior studies based on Chinese ST -firms have also demonstrated good

predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for

additional research and modeling work regarding unlisted and delisted Chinese firms

63 Country-level data performance of the Zrsquorsquo-Score models

The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform

all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the

different models estimated for each country separately (country-level models) In this table the

ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated

40

from the country data this benchmark will beis clearly outperformed by the resulting MDA

(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and

Sweden) However these results give only weak support for H1 at the country level because the

effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and

2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs

given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed

by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation

method)

Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC

than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3

leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United

StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in

nearly every country supporting H4 This improvement is significant however only in the United

StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark

model but for which it is only 0710 for the benchmark model It This improvement is also notable

in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age

category dummies) gives higher AUCs than the benchmark model for several countries but none

of the improvements are significant The positive effect is particularly strong especially in

Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only

weakly supports H5 because these negative effects are relatively small

41

Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects

on AUCs when compared with the benchmark However the only significant effect is the negative

effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the

industry dummies However there are notable positive effects on the AUC in for example

France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak

support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in

AUC compared with the benchmark model in Colombia Iceland and the Unites States It also

leads and to notable improvements in AUC at least in Belgium Denmark Estonia France

Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are

found only in Austria and Slovenia Thus as a conclusion we can concludestate that the

classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can

remarkably increases in most countries when different effects are taken into account by

additional variables This increase is found in most European countries and in Colombia and the

United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-

modelLR model and it can only be slightly improved only slightly by additional variables

(Table 6 here)

7 Summary of the study and a suggested extension

The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model

originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is

assessed using a very large international data set We test how the original version of the Zrsquorsquo-

42

Score model performs in different countries and how re-estimations using another statistical

method and different additional variables affect the classification performance when the data

are very heterogeneous For this kind of testing seven research hypotheses on classification

performance are extractedformulated These research hypotheses are tested for all data and

separately for country data (country-level analysis) The estimation data are from 31 countries

and the results are validated for 34 countries The countries are mainly from Europe but three

non-European countries are included (China Colombia and the United StatesUS) The statuses

used in the classification are mainly bankruptcyactive but receivership firms are also considered

to have failed In the Chinese data ST (special treatment) and delisted firms are also separately

analyzed as failed firms

The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very

satisfactorily in an international context The effects of the four financial ratios on performance

are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small

contribution in re-estimation The original model performs very well in several countries such as

Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only

marginally improved the classification performance thus weakly supporting the obsolescence

hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust

across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion

holds for the re-estimation of the model using LRA because the performance results are very

similar with to those of MDA (H2) The use of additional variables in the model generally

improvesd the classification accuracy of the original model but the results for countries are

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

32

the case for the failed firms For the faileddistressed firms the median exceeds the mean for

these three ratios indicating negatively skewed distributions For X4 (BVETD) the means

significantly exceed the median for both failed and non-failed firms indicating a positively

skewed distribution For each of the four variables both the mean and the median are higher for

non-failed firms than for failed firms which is consistent with the expectations The difference

between the means of the non-failed and failed firms is larger in the original USUS data than in

our all data for Retained EarningsTotal Assets (RETA) and EBITTotal Assets (EBITTA) but is

approximately the same size for Working CapitalTotal Assets (WCTA) and Book Value of

EquityTotal Liabilities (BVETD) BVETD (Altman 1983) These characteristics of the data may

indicate lower classification accuracy than in the original sample

(Table 2 here) Table 3 presents the coefficients of the different models estimated for all data All LRA estimates

(Model 2 to Model 9) are statistically significant at 0001 due to their contributions and to the

large sample size The first column presents the coefficients of the original Zrsquorsquo-Score model

Column The ldquoModel 1rdquo column shows the coefficients when they are re-estimated by the same

statistical method or MDA The coefficients here are negative because the models are estimated

using Y = 1 for the failed firms In our all data EBITTA has a significantly higher relative weight

than in the original USUS data while the weights of WCTA and BVETD have proportionally

decreased The re-estimated coefficient of BVETD is very small indicating a minor effect on the

logit Column The ldquoModel 2rdquo column presents the coefficients for the Zrsquorsquo-Score LR-modelLR

model These coefficients are directly comparable with those of the MDA model which isas

expected for the this exceptionally large sample The differences in the coefficients of the original

33

four variables among the eight LR-modelLR models (Models 1-8) are small For each model the

coefficient of BVETD is very close to zero This indicatinges that the original four variables and

the additional variables are quite independent of each other

(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-

modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate

that after 2007 (the base category) this risk of failure significantly decreased year by year The

base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due

to as a result of the global financial crisis The crisis played a significant role in the failure of key

businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The

effects were especially felt in Europe The coefficients of Model 4 for the size variables show that

the contribution of size on to the logit (risk measure) is atreaches its maximum value when

logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model

5 confirms the riskiness of young firms because the risk of failure is very high for newly founded

firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The

coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky

industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is

statistically significant (because of the large sample) but negative and very close to zero Finally

the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7

62 All data performance of the Zrsquorsquo-Score models

34

Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1

refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of

all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average

accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)

Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its

performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The

lower part of the table shows the AUCs for the countries included only in the test data The

performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)

samples It is also low and for the liquidation firms in the United Kingdom

The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5

presents the differences of these medians between the non-failed and failed firms by country

This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of

medians This correlation which is high for each financial ratio showing that the effects of the

ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large

except for EBITTA which This implies with the exceptionally high AUC that the ST firms

systematically differ from the non-ST firms although the differences are not extremely large The

differences between the medians are very large in Poland for each ratio justifying the high AUC

They are also large and in Finland and in the Czech Republic where the difference in EBITTA is

average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in

all four ratios are below the average a result thatwhich is obviously associated with a low AUC

35

In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even

negative In Iceland and Ireland the differences only in EBITTA are exceptionally small

(Table 4 here)

(Table 5 here)

Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by

MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)

supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is

at approximately the average level The re-estimation of the coefficients has led to improved

classification accuracy in a number of countries especially in Bosnia China (ST) Norway and

Greece However it has impaired the classification accuracy in the UK and China (delisted)

Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-

modelLR model) showing the effect of the estimation method For the benchmark model the

AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between

among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR

model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country

This result was expected because the coefficients of the models are directly comparable The

similar results for the models may also indicate that the independent variables conform to

multinormality This last latter result confirms what most researchers in the field of default

classification models have concluded that the accuracy levels of MDA and logistical regression

36

models are extremely similar despite any statistical violations such as normality between MDA

and logistical regression models

Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)

than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects

are not positive for all countries The effects are positive for example for Russia Estonia

Germany Ireland and Latvia but these effects are not statistically significant There are

statistically significant negative effects for China (ST) and Serbia There are notable negative

effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are

due to the exceptional annual distributions of failed firms in these countries For the countries

with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the

countries with positive effects it is only a few percent For each group non-failed firms are quite

equally distributed over years When the coefficient of D3 is very low (-0666) it strongly

decreases the risk estimates of most failed firms in the former countries but of only a few failed

firms in the latter countries This which leads to the observed effects H3 is supported by

the level of the whole sample However in some country samples (at the level of individual

levelies) H3 is not supported

Model 4 (LR-modelLR model with size variables) performs better than the benchmark model

which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads

and to significant improvements in the AUC for eg China (delisted) and Austria For China

(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC

37

also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs

in a few countries but thise decrease is not significant Model 4 is based on the four original

variables and the size effect following a second-order parabola This kind of size effect is very

small for micro firms but increases when approaching its maximum value in middle-sized firms

The countries with a positive change in AUC typically have data where in which the percent of

failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized

failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In

contrast the countries with a negative change in AUC have data where in which the percentage

of failed micro firms is exceptionally high (70-80)

Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly

the same AUC as the benchmark model (AUC = 0748) However the difference is positive and

statistically significant giving at least marginal support to H5 (age effect) For nearly all countries

the effect of the age on AUC is small For Austria however thise effect is however positive and

significant For Austria the percentage of non-failed young firms (less than 6 years) among non-

failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed

firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong

positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only

a few non-failed firms which This obviously leads to an improvement in AUC

Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC

supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its

38

AUC is notably higher than the benchmark AUC only in only a few countries such asfor example

France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg

restaurants and hotels and the information technology industryies but it has a positive effect

for on the construction and manufacturing industries For the countries with a positive effect on

AUC the percent of non-failed firms in restaurants and hotels and information technology is

high while that of failed firms is low For the risky industries (construction and manufacturing)

these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many

failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)

firms Therefore the AUC will be increasesd This situation is reversed for the countries with a

negative effect on AUC (Austria and Slovenia) The samples of these countries include a high

percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC

Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher

classification performance (AUC = 0749) than the benchmark model This result gives only very

weak support for H7 (country of origin effect) However for each country the resultant AUC is

nearly identical to that given by the benchmark model This result was expected due to the

negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7

This result implies that country risk hasdoes not have noany effect on the boundary between

bankrupt and non-bankrupt firms

Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional

variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to

39

the benchmark AUC in all test data However the effect on the AUCs largely varies and is either

negative or positive in different countries The positive effect is large in several countries such

as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative

effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show

that the inclusion of additional variables in the original model will usually increase the AUC but

not in every country

The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with

the USUS firms being unlike the majority of other firms in this study being listed or delisted

companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese

firm samples is associated with very small differences between the medians of the non-failed and

failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable

with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed

firms is very high Prior studies based on Chinese ST -firms have also demonstrated good

predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for

additional research and modeling work regarding unlisted and delisted Chinese firms

63 Country-level data performance of the Zrsquorsquo-Score models

The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform

all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the

different models estimated for each country separately (country-level models) In this table the

ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated

40

from the country data this benchmark will beis clearly outperformed by the resulting MDA

(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and

Sweden) However these results give only weak support for H1 at the country level because the

effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and

2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs

given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed

by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation

method)

Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC

than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3

leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United

StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in

nearly every country supporting H4 This improvement is significant however only in the United

StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark

model but for which it is only 0710 for the benchmark model It This improvement is also notable

in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age

category dummies) gives higher AUCs than the benchmark model for several countries but none

of the improvements are significant The positive effect is particularly strong especially in

Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only

weakly supports H5 because these negative effects are relatively small

41

Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects

on AUCs when compared with the benchmark However the only significant effect is the negative

effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the

industry dummies However there are notable positive effects on the AUC in for example

France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak

support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in

AUC compared with the benchmark model in Colombia Iceland and the Unites States It also

leads and to notable improvements in AUC at least in Belgium Denmark Estonia France

Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are

found only in Austria and Slovenia Thus as a conclusion we can concludestate that the

classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can

remarkably increases in most countries when different effects are taken into account by

additional variables This increase is found in most European countries and in Colombia and the

United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-

modelLR model and it can only be slightly improved only slightly by additional variables

(Table 6 here)

7 Summary of the study and a suggested extension

The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model

originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is

assessed using a very large international data set We test how the original version of the Zrsquorsquo-

42

Score model performs in different countries and how re-estimations using another statistical

method and different additional variables affect the classification performance when the data

are very heterogeneous For this kind of testing seven research hypotheses on classification

performance are extractedformulated These research hypotheses are tested for all data and

separately for country data (country-level analysis) The estimation data are from 31 countries

and the results are validated for 34 countries The countries are mainly from Europe but three

non-European countries are included (China Colombia and the United StatesUS) The statuses

used in the classification are mainly bankruptcyactive but receivership firms are also considered

to have failed In the Chinese data ST (special treatment) and delisted firms are also separately

analyzed as failed firms

The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very

satisfactorily in an international context The effects of the four financial ratios on performance

are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small

contribution in re-estimation The original model performs very well in several countries such as

Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only

marginally improved the classification performance thus weakly supporting the obsolescence

hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust

across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion

holds for the re-estimation of the model using LRA because the performance results are very

similar with to those of MDA (H2) The use of additional variables in the model generally

improvesd the classification accuracy of the original model but the results for countries are

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

33

four variables among the eight LR-modelLR models (Models 1-8) are small For each model the

coefficient of BVETD is very close to zero This indicatinges that the original four variables and

the additional variables are quite independent of each other

(Table 3 here) Table 3 also shows the coefficients of the additional variables beyond the original four in the LR-

modelLR models The negative coefficients of the dummy (year) variables of Model 3 indicate

that after 2007 (the base category) this risk of failure significantly decreased year by year The

base year of 2007 indicates that a failure emerged during 2008ndash2009 This phenomenon is due

to as a result of the global financial crisis The crisis played a significant role in the failure of key

businesses and caused a downturn in economic activity leading to the 2008ndash2012 recession The

effects were especially felt in Europe The coefficients of Model 4 for the size variables show that

the contribution of size on to the logit (risk measure) is atreaches its maximum value when

logarithmic total assets is are 15 or when total assets is are approximately 33 million EUR Model

5 confirms the riskiness of young firms because the risk of failure is very high for newly founded

firms (less than 6 years old) as is shown by the coefficient of the first dummy variable The

coefficients of the industry dummies in Model 6 show that construction is an exceptionally risky

industry followed by manufacturing For Model 7 the coefficient of the country risk dummy is

statistically significant (because of the large sample) but negative and very close to zero Finally

the coefficients of all variables in Model 8 are directly comparable with to those in Models 3-7

62 All data performance of the Zrsquorsquo-Score models

34

Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1

refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of

all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average

accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)

Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its

performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The

lower part of the table shows the AUCs for the countries included only in the test data The

performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)

samples It is also low and for the liquidation firms in the United Kingdom

The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5

presents the differences of these medians between the non-failed and failed firms by country

This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of

medians This correlation which is high for each financial ratio showing that the effects of the

ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large

except for EBITTA which This implies with the exceptionally high AUC that the ST firms

systematically differ from the non-ST firms although the differences are not extremely large The

differences between the medians are very large in Poland for each ratio justifying the high AUC

They are also large and in Finland and in the Czech Republic where the difference in EBITTA is

average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in

all four ratios are below the average a result thatwhich is obviously associated with a low AUC

35

In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even

negative In Iceland and Ireland the differences only in EBITTA are exceptionally small

(Table 4 here)

(Table 5 here)

Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by

MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)

supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is

at approximately the average level The re-estimation of the coefficients has led to improved

classification accuracy in a number of countries especially in Bosnia China (ST) Norway and

Greece However it has impaired the classification accuracy in the UK and China (delisted)

Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-

modelLR model) showing the effect of the estimation method For the benchmark model the

AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between

among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR

model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country

This result was expected because the coefficients of the models are directly comparable The

similar results for the models may also indicate that the independent variables conform to

multinormality This last latter result confirms what most researchers in the field of default

classification models have concluded that the accuracy levels of MDA and logistical regression

36

models are extremely similar despite any statistical violations such as normality between MDA

and logistical regression models

Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)

than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects

are not positive for all countries The effects are positive for example for Russia Estonia

Germany Ireland and Latvia but these effects are not statistically significant There are

statistically significant negative effects for China (ST) and Serbia There are notable negative

effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are

due to the exceptional annual distributions of failed firms in these countries For the countries

with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the

countries with positive effects it is only a few percent For each group non-failed firms are quite

equally distributed over years When the coefficient of D3 is very low (-0666) it strongly

decreases the risk estimates of most failed firms in the former countries but of only a few failed

firms in the latter countries This which leads to the observed effects H3 is supported by

the level of the whole sample However in some country samples (at the level of individual

levelies) H3 is not supported

Model 4 (LR-modelLR model with size variables) performs better than the benchmark model

which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads

and to significant improvements in the AUC for eg China (delisted) and Austria For China

(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC

37

also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs

in a few countries but thise decrease is not significant Model 4 is based on the four original

variables and the size effect following a second-order parabola This kind of size effect is very

small for micro firms but increases when approaching its maximum value in middle-sized firms

The countries with a positive change in AUC typically have data where in which the percent of

failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized

failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In

contrast the countries with a negative change in AUC have data where in which the percentage

of failed micro firms is exceptionally high (70-80)

Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly

the same AUC as the benchmark model (AUC = 0748) However the difference is positive and

statistically significant giving at least marginal support to H5 (age effect) For nearly all countries

the effect of the age on AUC is small For Austria however thise effect is however positive and

significant For Austria the percentage of non-failed young firms (less than 6 years) among non-

failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed

firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong

positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only

a few non-failed firms which This obviously leads to an improvement in AUC

Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC

supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its

38

AUC is notably higher than the benchmark AUC only in only a few countries such asfor example

France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg

restaurants and hotels and the information technology industryies but it has a positive effect

for on the construction and manufacturing industries For the countries with a positive effect on

AUC the percent of non-failed firms in restaurants and hotels and information technology is

high while that of failed firms is low For the risky industries (construction and manufacturing)

these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many

failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)

firms Therefore the AUC will be increasesd This situation is reversed for the countries with a

negative effect on AUC (Austria and Slovenia) The samples of these countries include a high

percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC

Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher

classification performance (AUC = 0749) than the benchmark model This result gives only very

weak support for H7 (country of origin effect) However for each country the resultant AUC is

nearly identical to that given by the benchmark model This result was expected due to the

negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7

This result implies that country risk hasdoes not have noany effect on the boundary between

bankrupt and non-bankrupt firms

Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional

variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to

39

the benchmark AUC in all test data However the effect on the AUCs largely varies and is either

negative or positive in different countries The positive effect is large in several countries such

as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative

effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show

that the inclusion of additional variables in the original model will usually increase the AUC but

not in every country

The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with

the USUS firms being unlike the majority of other firms in this study being listed or delisted

companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese

firm samples is associated with very small differences between the medians of the non-failed and

failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable

with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed

firms is very high Prior studies based on Chinese ST -firms have also demonstrated good

predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for

additional research and modeling work regarding unlisted and delisted Chinese firms

63 Country-level data performance of the Zrsquorsquo-Score models

The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform

all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the

different models estimated for each country separately (country-level models) In this table the

ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated

40

from the country data this benchmark will beis clearly outperformed by the resulting MDA

(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and

Sweden) However these results give only weak support for H1 at the country level because the

effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and

2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs

given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed

by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation

method)

Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC

than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3

leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United

StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in

nearly every country supporting H4 This improvement is significant however only in the United

StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark

model but for which it is only 0710 for the benchmark model It This improvement is also notable

in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age

category dummies) gives higher AUCs than the benchmark model for several countries but none

of the improvements are significant The positive effect is particularly strong especially in

Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only

weakly supports H5 because these negative effects are relatively small

41

Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects

on AUCs when compared with the benchmark However the only significant effect is the negative

effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the

industry dummies However there are notable positive effects on the AUC in for example

France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak

support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in

AUC compared with the benchmark model in Colombia Iceland and the Unites States It also

leads and to notable improvements in AUC at least in Belgium Denmark Estonia France

Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are

found only in Austria and Slovenia Thus as a conclusion we can concludestate that the

classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can

remarkably increases in most countries when different effects are taken into account by

additional variables This increase is found in most European countries and in Colombia and the

United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-

modelLR model and it can only be slightly improved only slightly by additional variables

(Table 6 here)

7 Summary of the study and a suggested extension

The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model

originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is

assessed using a very large international data set We test how the original version of the Zrsquorsquo-

42

Score model performs in different countries and how re-estimations using another statistical

method and different additional variables affect the classification performance when the data

are very heterogeneous For this kind of testing seven research hypotheses on classification

performance are extractedformulated These research hypotheses are tested for all data and

separately for country data (country-level analysis) The estimation data are from 31 countries

and the results are validated for 34 countries The countries are mainly from Europe but three

non-European countries are included (China Colombia and the United StatesUS) The statuses

used in the classification are mainly bankruptcyactive but receivership firms are also considered

to have failed In the Chinese data ST (special treatment) and delisted firms are also separately

analyzed as failed firms

The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very

satisfactorily in an international context The effects of the four financial ratios on performance

are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small

contribution in re-estimation The original model performs very well in several countries such as

Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only

marginally improved the classification performance thus weakly supporting the obsolescence

hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust

across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion

holds for the re-estimation of the model using LRA because the performance results are very

similar with to those of MDA (H2) The use of additional variables in the model generally

improvesd the classification accuracy of the original model but the results for countries are

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

34

Table 4 shows the AUCs in the test data for the different ldquoall datardquo models by country Model 1

refers to the original Zrsquorsquo-Score model The classification performance of the score at the level of

all countries is fair because AUC = 0743 refers to AR = 0486 which is an approximately average

accuracy (05) However the score gives relatively good results (AUC gt 08) for China (ST firms)

Poland Finland Estonia the Czech Republic Croatia Bosnia Russia and Sweden Its

performance is quite low (AUC lt 07) for Norway Latvia Iceland Ireland and Germany The

lower part of the table shows the AUCs for the countries included only in the test data The

performance of the score is very low in the Chinese CN (primarily private) and DL (delisted)

samples It is also low and for the liquidation firms in the United Kingdom

The appendix shows the medians of the four ratios (X1 to X4) by status and country Table 5

presents the differences of these medians between the non-failed and failed firms by country

This table also presents the AUC of the Zrsquorsquo-Score and its correlation with the difference of

medians This correlation which is high for each financial ratio showing that the effects of the

ratios on AUC are well balanced For Chinarsquos ST firms the differences are not exceptionally large

except for EBITTA which This implies with the exceptionally high AUC that the ST firms

systematically differ from the non-ST firms although the differences are not extremely large The

differences between the medians are very large in Poland for each ratio justifying the high AUC

They are also large and in Finland and in the Czech Republic where the difference in EBITTA is

average In Germany Latvia China (CN and delisted) and the UK (liquidation) the differences in

all four ratios are below the average a result thatwhich is obviously associated with a low AUC

35

In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even

negative In Iceland and Ireland the differences only in EBITTA are exceptionally small

(Table 4 here)

(Table 5 here)

Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by

MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)

supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is

at approximately the average level The re-estimation of the coefficients has led to improved

classification accuracy in a number of countries especially in Bosnia China (ST) Norway and

Greece However it has impaired the classification accuracy in the UK and China (delisted)

Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-

modelLR model) showing the effect of the estimation method For the benchmark model the

AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between

among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR

model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country

This result was expected because the coefficients of the models are directly comparable The

similar results for the models may also indicate that the independent variables conform to

multinormality This last latter result confirms what most researchers in the field of default

classification models have concluded that the accuracy levels of MDA and logistical regression

36

models are extremely similar despite any statistical violations such as normality between MDA

and logistical regression models

Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)

than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects

are not positive for all countries The effects are positive for example for Russia Estonia

Germany Ireland and Latvia but these effects are not statistically significant There are

statistically significant negative effects for China (ST) and Serbia There are notable negative

effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are

due to the exceptional annual distributions of failed firms in these countries For the countries

with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the

countries with positive effects it is only a few percent For each group non-failed firms are quite

equally distributed over years When the coefficient of D3 is very low (-0666) it strongly

decreases the risk estimates of most failed firms in the former countries but of only a few failed

firms in the latter countries This which leads to the observed effects H3 is supported by

the level of the whole sample However in some country samples (at the level of individual

levelies) H3 is not supported

Model 4 (LR-modelLR model with size variables) performs better than the benchmark model

which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads

and to significant improvements in the AUC for eg China (delisted) and Austria For China

(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC

37

also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs

in a few countries but thise decrease is not significant Model 4 is based on the four original

variables and the size effect following a second-order parabola This kind of size effect is very

small for micro firms but increases when approaching its maximum value in middle-sized firms

The countries with a positive change in AUC typically have data where in which the percent of

failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized

failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In

contrast the countries with a negative change in AUC have data where in which the percentage

of failed micro firms is exceptionally high (70-80)

Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly

the same AUC as the benchmark model (AUC = 0748) However the difference is positive and

statistically significant giving at least marginal support to H5 (age effect) For nearly all countries

the effect of the age on AUC is small For Austria however thise effect is however positive and

significant For Austria the percentage of non-failed young firms (less than 6 years) among non-

failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed

firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong

positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only

a few non-failed firms which This obviously leads to an improvement in AUC

Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC

supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its

38

AUC is notably higher than the benchmark AUC only in only a few countries such asfor example

France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg

restaurants and hotels and the information technology industryies but it has a positive effect

for on the construction and manufacturing industries For the countries with a positive effect on

AUC the percent of non-failed firms in restaurants and hotels and information technology is

high while that of failed firms is low For the risky industries (construction and manufacturing)

these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many

failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)

firms Therefore the AUC will be increasesd This situation is reversed for the countries with a

negative effect on AUC (Austria and Slovenia) The samples of these countries include a high

percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC

Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher

classification performance (AUC = 0749) than the benchmark model This result gives only very

weak support for H7 (country of origin effect) However for each country the resultant AUC is

nearly identical to that given by the benchmark model This result was expected due to the

negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7

This result implies that country risk hasdoes not have noany effect on the boundary between

bankrupt and non-bankrupt firms

Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional

variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to

39

the benchmark AUC in all test data However the effect on the AUCs largely varies and is either

negative or positive in different countries The positive effect is large in several countries such

as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative

effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show

that the inclusion of additional variables in the original model will usually increase the AUC but

not in every country

The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with

the USUS firms being unlike the majority of other firms in this study being listed or delisted

companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese

firm samples is associated with very small differences between the medians of the non-failed and

failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable

with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed

firms is very high Prior studies based on Chinese ST -firms have also demonstrated good

predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for

additional research and modeling work regarding unlisted and delisted Chinese firms

63 Country-level data performance of the Zrsquorsquo-Score models

The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform

all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the

different models estimated for each country separately (country-level models) In this table the

ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated

40

from the country data this benchmark will beis clearly outperformed by the resulting MDA

(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and

Sweden) However these results give only weak support for H1 at the country level because the

effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and

2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs

given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed

by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation

method)

Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC

than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3

leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United

StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in

nearly every country supporting H4 This improvement is significant however only in the United

StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark

model but for which it is only 0710 for the benchmark model It This improvement is also notable

in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age

category dummies) gives higher AUCs than the benchmark model for several countries but none

of the improvements are significant The positive effect is particularly strong especially in

Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only

weakly supports H5 because these negative effects are relatively small

41

Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects

on AUCs when compared with the benchmark However the only significant effect is the negative

effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the

industry dummies However there are notable positive effects on the AUC in for example

France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak

support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in

AUC compared with the benchmark model in Colombia Iceland and the Unites States It also

leads and to notable improvements in AUC at least in Belgium Denmark Estonia France

Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are

found only in Austria and Slovenia Thus as a conclusion we can concludestate that the

classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can

remarkably increases in most countries when different effects are taken into account by

additional variables This increase is found in most European countries and in Colombia and the

United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-

modelLR model and it can only be slightly improved only slightly by additional variables

(Table 6 here)

7 Summary of the study and a suggested extension

The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model

originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is

assessed using a very large international data set We test how the original version of the Zrsquorsquo-

42

Score model performs in different countries and how re-estimations using another statistical

method and different additional variables affect the classification performance when the data

are very heterogeneous For this kind of testing seven research hypotheses on classification

performance are extractedformulated These research hypotheses are tested for all data and

separately for country data (country-level analysis) The estimation data are from 31 countries

and the results are validated for 34 countries The countries are mainly from Europe but three

non-European countries are included (China Colombia and the United StatesUS) The statuses

used in the classification are mainly bankruptcyactive but receivership firms are also considered

to have failed In the Chinese data ST (special treatment) and delisted firms are also separately

analyzed as failed firms

The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very

satisfactorily in an international context The effects of the four financial ratios on performance

are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small

contribution in re-estimation The original model performs very well in several countries such as

Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only

marginally improved the classification performance thus weakly supporting the obsolescence

hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust

across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion

holds for the re-estimation of the model using LRA because the performance results are very

similar with to those of MDA (H2) The use of additional variables in the model generally

improvesd the classification accuracy of the original model but the results for countries are

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

35

In the sample of Chinese delisted firms sample the differences in RETA and EBITTA are even

negative In Iceland and Ireland the differences only in EBITTA are exceptionally small

(Table 4 here)

(Table 5 here)

Model 2 in Table 4 is the re-estimated Zrsquorsquo-Score model where the coefficients are estimated by

MDA for all data Its AUC (0745) is only slightly higher than that for the original model (0743)

supporting H1o only very weakly if at all H1 The classification accuracy in terms of AR (0490) is

at approximately the average level The re-estimation of the coefficients has led to improved

classification accuracy in a number of countries especially in Bosnia China (ST) Norway and

Greece However it has impaired the classification accuracy in the UK and China (delisted)

Column The ldquoBenchmarkrdquo column reports the results for the benchmark model (Zrsquorsquo-Score LR-

modelLR model) showing the effect of the estimation method For the benchmark model the

AUC in all data is 0748 which is higher than that for Models 1 and 2 The differences between

among AUCs are very small only weakly supporting H2 (estimation method) The LR-modelLR

model (benchmark) and the MDA model (Model 2) give nearly identical AUCs for each country

This result was expected because the coefficients of the models are directly comparable The

similar results for the models may also indicate that the independent variables conform to

multinormality This last latter result confirms what most researchers in the field of default

classification models have concluded that the accuracy levels of MDA and logistical regression

36

models are extremely similar despite any statistical violations such as normality between MDA

and logistical regression models

Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)

than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects

are not positive for all countries The effects are positive for example for Russia Estonia

Germany Ireland and Latvia but these effects are not statistically significant There are

statistically significant negative effects for China (ST) and Serbia There are notable negative

effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are

due to the exceptional annual distributions of failed firms in these countries For the countries

with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the

countries with positive effects it is only a few percent For each group non-failed firms are quite

equally distributed over years When the coefficient of D3 is very low (-0666) it strongly

decreases the risk estimates of most failed firms in the former countries but of only a few failed

firms in the latter countries This which leads to the observed effects H3 is supported by

the level of the whole sample However in some country samples (at the level of individual

levelies) H3 is not supported

Model 4 (LR-modelLR model with size variables) performs better than the benchmark model

which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads

and to significant improvements in the AUC for eg China (delisted) and Austria For China

(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC

37

also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs

in a few countries but thise decrease is not significant Model 4 is based on the four original

variables and the size effect following a second-order parabola This kind of size effect is very

small for micro firms but increases when approaching its maximum value in middle-sized firms

The countries with a positive change in AUC typically have data where in which the percent of

failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized

failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In

contrast the countries with a negative change in AUC have data where in which the percentage

of failed micro firms is exceptionally high (70-80)

Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly

the same AUC as the benchmark model (AUC = 0748) However the difference is positive and

statistically significant giving at least marginal support to H5 (age effect) For nearly all countries

the effect of the age on AUC is small For Austria however thise effect is however positive and

significant For Austria the percentage of non-failed young firms (less than 6 years) among non-

failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed

firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong

positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only

a few non-failed firms which This obviously leads to an improvement in AUC

Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC

supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its

38

AUC is notably higher than the benchmark AUC only in only a few countries such asfor example

France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg

restaurants and hotels and the information technology industryies but it has a positive effect

for on the construction and manufacturing industries For the countries with a positive effect on

AUC the percent of non-failed firms in restaurants and hotels and information technology is

high while that of failed firms is low For the risky industries (construction and manufacturing)

these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many

failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)

firms Therefore the AUC will be increasesd This situation is reversed for the countries with a

negative effect on AUC (Austria and Slovenia) The samples of these countries include a high

percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC

Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher

classification performance (AUC = 0749) than the benchmark model This result gives only very

weak support for H7 (country of origin effect) However for each country the resultant AUC is

nearly identical to that given by the benchmark model This result was expected due to the

negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7

This result implies that country risk hasdoes not have noany effect on the boundary between

bankrupt and non-bankrupt firms

Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional

variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to

39

the benchmark AUC in all test data However the effect on the AUCs largely varies and is either

negative or positive in different countries The positive effect is large in several countries such

as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative

effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show

that the inclusion of additional variables in the original model will usually increase the AUC but

not in every country

The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with

the USUS firms being unlike the majority of other firms in this study being listed or delisted

companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese

firm samples is associated with very small differences between the medians of the non-failed and

failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable

with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed

firms is very high Prior studies based on Chinese ST -firms have also demonstrated good

predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for

additional research and modeling work regarding unlisted and delisted Chinese firms

63 Country-level data performance of the Zrsquorsquo-Score models

The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform

all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the

different models estimated for each country separately (country-level models) In this table the

ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated

40

from the country data this benchmark will beis clearly outperformed by the resulting MDA

(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and

Sweden) However these results give only weak support for H1 at the country level because the

effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and

2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs

given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed

by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation

method)

Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC

than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3

leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United

StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in

nearly every country supporting H4 This improvement is significant however only in the United

StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark

model but for which it is only 0710 for the benchmark model It This improvement is also notable

in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age

category dummies) gives higher AUCs than the benchmark model for several countries but none

of the improvements are significant The positive effect is particularly strong especially in

Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only

weakly supports H5 because these negative effects are relatively small

41

Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects

on AUCs when compared with the benchmark However the only significant effect is the negative

effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the

industry dummies However there are notable positive effects on the AUC in for example

France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak

support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in

AUC compared with the benchmark model in Colombia Iceland and the Unites States It also

leads and to notable improvements in AUC at least in Belgium Denmark Estonia France

Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are

found only in Austria and Slovenia Thus as a conclusion we can concludestate that the

classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can

remarkably increases in most countries when different effects are taken into account by

additional variables This increase is found in most European countries and in Colombia and the

United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-

modelLR model and it can only be slightly improved only slightly by additional variables

(Table 6 here)

7 Summary of the study and a suggested extension

The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model

originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is

assessed using a very large international data set We test how the original version of the Zrsquorsquo-

42

Score model performs in different countries and how re-estimations using another statistical

method and different additional variables affect the classification performance when the data

are very heterogeneous For this kind of testing seven research hypotheses on classification

performance are extractedformulated These research hypotheses are tested for all data and

separately for country data (country-level analysis) The estimation data are from 31 countries

and the results are validated for 34 countries The countries are mainly from Europe but three

non-European countries are included (China Colombia and the United StatesUS) The statuses

used in the classification are mainly bankruptcyactive but receivership firms are also considered

to have failed In the Chinese data ST (special treatment) and delisted firms are also separately

analyzed as failed firms

The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very

satisfactorily in an international context The effects of the four financial ratios on performance

are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small

contribution in re-estimation The original model performs very well in several countries such as

Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only

marginally improved the classification performance thus weakly supporting the obsolescence

hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust

across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion

holds for the re-estimation of the model using LRA because the performance results are very

similar with to those of MDA (H2) The use of additional variables in the model generally

improvesd the classification accuracy of the original model but the results for countries are

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

36

models are extremely similar despite any statistical violations such as normality between MDA

and logistical regression models

Model 3 (LR-modelLR model with year dummies) leads in all test data to a higher AUC (0752)

than the benchmark model supporting H3 (bankruptcy year effect) However the AUC effects

are not positive for all countries The effects are positive for example for Russia Estonia

Germany Ireland and Latvia but these effects are not statistically significant There are

statistically significant negative effects for China (ST) and Serbia There are notable negative

effects especially for Bosnia Croatia Hungary Norway and Slovenia These diverse results are

due to the exceptional annual distributions of failed firms in these countries For the countries

with negative effects the percent of failed firms from 2010 (D3 = 1) exceeds 50 while for the

countries with positive effects it is only a few percent For each group non-failed firms are quite

equally distributed over years When the coefficient of D3 is very low (-0666) it strongly

decreases the risk estimates of most failed firms in the former countries but of only a few failed

firms in the latter countries This which leads to the observed effects H3 is supported by

the level of the whole sample However in some country samples (at the level of individual

levelies) H3 is not supported

Model 4 (LR-modelLR model with size variables) performs better than the benchmark model

which lends support to H4 (size effect) and It leads to AUC = 0760 indicating AR = 0520 It leads

and to significant improvements in the AUC for eg China (delisted) and Austria For China

(delisted) the increase in AUC is extremely strong and There are also improvements in the AUC

37

also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs

in a few countries but thise decrease is not significant Model 4 is based on the four original

variables and the size effect following a second-order parabola This kind of size effect is very

small for micro firms but increases when approaching its maximum value in middle-sized firms

The countries with a positive change in AUC typically have data where in which the percent of

failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized

failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In

contrast the countries with a negative change in AUC have data where in which the percentage

of failed micro firms is exceptionally high (70-80)

Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly

the same AUC as the benchmark model (AUC = 0748) However the difference is positive and

statistically significant giving at least marginal support to H5 (age effect) For nearly all countries

the effect of the age on AUC is small For Austria however thise effect is however positive and

significant For Austria the percentage of non-failed young firms (less than 6 years) among non-

failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed

firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong

positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only

a few non-failed firms which This obviously leads to an improvement in AUC

Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC

supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its

38

AUC is notably higher than the benchmark AUC only in only a few countries such asfor example

France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg

restaurants and hotels and the information technology industryies but it has a positive effect

for on the construction and manufacturing industries For the countries with a positive effect on

AUC the percent of non-failed firms in restaurants and hotels and information technology is

high while that of failed firms is low For the risky industries (construction and manufacturing)

these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many

failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)

firms Therefore the AUC will be increasesd This situation is reversed for the countries with a

negative effect on AUC (Austria and Slovenia) The samples of these countries include a high

percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC

Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher

classification performance (AUC = 0749) than the benchmark model This result gives only very

weak support for H7 (country of origin effect) However for each country the resultant AUC is

nearly identical to that given by the benchmark model This result was expected due to the

negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7

This result implies that country risk hasdoes not have noany effect on the boundary between

bankrupt and non-bankrupt firms

Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional

variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to

39

the benchmark AUC in all test data However the effect on the AUCs largely varies and is either

negative or positive in different countries The positive effect is large in several countries such

as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative

effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show

that the inclusion of additional variables in the original model will usually increase the AUC but

not in every country

The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with

the USUS firms being unlike the majority of other firms in this study being listed or delisted

companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese

firm samples is associated with very small differences between the medians of the non-failed and

failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable

with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed

firms is very high Prior studies based on Chinese ST -firms have also demonstrated good

predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for

additional research and modeling work regarding unlisted and delisted Chinese firms

63 Country-level data performance of the Zrsquorsquo-Score models

The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform

all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the

different models estimated for each country separately (country-level models) In this table the

ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated

40

from the country data this benchmark will beis clearly outperformed by the resulting MDA

(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and

Sweden) However these results give only weak support for H1 at the country level because the

effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and

2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs

given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed

by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation

method)

Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC

than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3

leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United

StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in

nearly every country supporting H4 This improvement is significant however only in the United

StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark

model but for which it is only 0710 for the benchmark model It This improvement is also notable

in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age

category dummies) gives higher AUCs than the benchmark model for several countries but none

of the improvements are significant The positive effect is particularly strong especially in

Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only

weakly supports H5 because these negative effects are relatively small

41

Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects

on AUCs when compared with the benchmark However the only significant effect is the negative

effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the

industry dummies However there are notable positive effects on the AUC in for example

France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak

support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in

AUC compared with the benchmark model in Colombia Iceland and the Unites States It also

leads and to notable improvements in AUC at least in Belgium Denmark Estonia France

Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are

found only in Austria and Slovenia Thus as a conclusion we can concludestate that the

classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can

remarkably increases in most countries when different effects are taken into account by

additional variables This increase is found in most European countries and in Colombia and the

United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-

modelLR model and it can only be slightly improved only slightly by additional variables

(Table 6 here)

7 Summary of the study and a suggested extension

The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model

originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is

assessed using a very large international data set We test how the original version of the Zrsquorsquo-

42

Score model performs in different countries and how re-estimations using another statistical

method and different additional variables affect the classification performance when the data

are very heterogeneous For this kind of testing seven research hypotheses on classification

performance are extractedformulated These research hypotheses are tested for all data and

separately for country data (country-level analysis) The estimation data are from 31 countries

and the results are validated for 34 countries The countries are mainly from Europe but three

non-European countries are included (China Colombia and the United StatesUS) The statuses

used in the classification are mainly bankruptcyactive but receivership firms are also considered

to have failed In the Chinese data ST (special treatment) and delisted firms are also separately

analyzed as failed firms

The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very

satisfactorily in an international context The effects of the four financial ratios on performance

are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small

contribution in re-estimation The original model performs very well in several countries such as

Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only

marginally improved the classification performance thus weakly supporting the obsolescence

hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust

across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion

holds for the re-estimation of the model using LRA because the performance results are very

similar with to those of MDA (H2) The use of additional variables in the model generally

improvesd the classification accuracy of the original model but the results for countries are

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

37

also improves for eg Estonia Italy Slovakia Spain and the UK Model 4 also led to lower AUCs

in a few countries but thise decrease is not significant Model 4 is based on the four original

variables and the size effect following a second-order parabola This kind of size effect is very

small for micro firms but increases when approaching its maximum value in middle-sized firms

The countries with a positive change in AUC typically have data where in which the percent of

failed micro firms (in all failed firms) is relatively low (40-60) while there are many middle-sized

failed firms Because the size effect is strongest for middle-sized firms the AUC will increases In

contrast the countries with a negative change in AUC have data where in which the percentage

of failed micro firms is exceptionally high (70-80)

Model 5 (LR-modelLR model with age category dummies) gives for all test data almost nearly

the same AUC as the benchmark model (AUC = 0748) However the difference is positive and

statistically significant giving at least marginal support to H5 (age effect) For nearly all countries

the effect of the age on AUC is small For Austria however thise effect is however positive and

significant For Austria the percentage of non-failed young firms (less than 6 years) among non-

failed firms is only approximately 10 (of non-failed firms) whereas this percentage for failed

firmsamong failed firms is more than 20 (of failed firms) Because Model 5 includes a strong

positive age risk effect for young firms (D2) it increases the risk of many failed firms but of only

a few non-failed firms which This obviously leads to an improvement in AUC

Model 6 (LR-modelLR model with industry dummies) outperforms the benchmark model in AUC

supporting H6 (industry effect) It gives an AUC = 0751 indicating an AR = 0502 However its

38

AUC is notably higher than the benchmark AUC only in only a few countries such asfor example

France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg

restaurants and hotels and the information technology industryies but it has a positive effect

for on the construction and manufacturing industries For the countries with a positive effect on

AUC the percent of non-failed firms in restaurants and hotels and information technology is

high while that of failed firms is low For the risky industries (construction and manufacturing)

these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many

failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)

firms Therefore the AUC will be increasesd This situation is reversed for the countries with a

negative effect on AUC (Austria and Slovenia) The samples of these countries include a high

percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC

Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher

classification performance (AUC = 0749) than the benchmark model This result gives only very

weak support for H7 (country of origin effect) However for each country the resultant AUC is

nearly identical to that given by the benchmark model This result was expected due to the

negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7

This result implies that country risk hasdoes not have noany effect on the boundary between

bankrupt and non-bankrupt firms

Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional

variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to

39

the benchmark AUC in all test data However the effect on the AUCs largely varies and is either

negative or positive in different countries The positive effect is large in several countries such

as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative

effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show

that the inclusion of additional variables in the original model will usually increase the AUC but

not in every country

The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with

the USUS firms being unlike the majority of other firms in this study being listed or delisted

companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese

firm samples is associated with very small differences between the medians of the non-failed and

failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable

with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed

firms is very high Prior studies based on Chinese ST -firms have also demonstrated good

predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for

additional research and modeling work regarding unlisted and delisted Chinese firms

63 Country-level data performance of the Zrsquorsquo-Score models

The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform

all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the

different models estimated for each country separately (country-level models) In this table the

ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated

40

from the country data this benchmark will beis clearly outperformed by the resulting MDA

(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and

Sweden) However these results give only weak support for H1 at the country level because the

effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and

2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs

given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed

by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation

method)

Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC

than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3

leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United

StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in

nearly every country supporting H4 This improvement is significant however only in the United

StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark

model but for which it is only 0710 for the benchmark model It This improvement is also notable

in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age

category dummies) gives higher AUCs than the benchmark model for several countries but none

of the improvements are significant The positive effect is particularly strong especially in

Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only

weakly supports H5 because these negative effects are relatively small

41

Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects

on AUCs when compared with the benchmark However the only significant effect is the negative

effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the

industry dummies However there are notable positive effects on the AUC in for example

France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak

support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in

AUC compared with the benchmark model in Colombia Iceland and the Unites States It also

leads and to notable improvements in AUC at least in Belgium Denmark Estonia France

Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are

found only in Austria and Slovenia Thus as a conclusion we can concludestate that the

classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can

remarkably increases in most countries when different effects are taken into account by

additional variables This increase is found in most European countries and in Colombia and the

United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-

modelLR model and it can only be slightly improved only slightly by additional variables

(Table 6 here)

7 Summary of the study and a suggested extension

The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model

originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is

assessed using a very large international data set We test how the original version of the Zrsquorsquo-

42

Score model performs in different countries and how re-estimations using another statistical

method and different additional variables affect the classification performance when the data

are very heterogeneous For this kind of testing seven research hypotheses on classification

performance are extractedformulated These research hypotheses are tested for all data and

separately for country data (country-level analysis) The estimation data are from 31 countries

and the results are validated for 34 countries The countries are mainly from Europe but three

non-European countries are included (China Colombia and the United StatesUS) The statuses

used in the classification are mainly bankruptcyactive but receivership firms are also considered

to have failed In the Chinese data ST (special treatment) and delisted firms are also separately

analyzed as failed firms

The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very

satisfactorily in an international context The effects of the four financial ratios on performance

are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small

contribution in re-estimation The original model performs very well in several countries such as

Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only

marginally improved the classification performance thus weakly supporting the obsolescence

hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust

across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion

holds for the re-estimation of the model using LRA because the performance results are very

similar with to those of MDA (H2) The use of additional variables in the model generally

improvesd the classification accuracy of the original model but the results for countries are

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

38

AUC is notably higher than the benchmark AUC only in only a few countries such asfor example

France Latvia Portugal and Spain Model 6 has a negative effect on failure risk for eg

restaurants and hotels and the information technology industryies but it has a positive effect

for on the construction and manufacturing industries For the countries with a positive effect on

AUC the percent of non-failed firms in restaurants and hotels and information technology is

high while that of failed firms is low For the risky industries (construction and manufacturing)

these distributions are reversed Thus Model 6 gives a positive (negative) risk effect for many

failed (non-failed) firms and a negative (positive) risk effect only for only a few failed (non-failed)

firms Therefore the AUC will be increasesd This situation is reversed for the countries with a

negative effect on AUC (Austria and Slovenia) The samples of these countries include a high

percentage of non-failed firms in the manufacturing industry leading to a decrease in AUC

Model 7 (LR-modelLR model with the country risk measure) leads to a marginally higher

classification performance (AUC = 0749) than the benchmark model This result gives only very

weak support for H7 (country of origin effect) However for each country the resultant AUC is

nearly identical to that given by the benchmark model This result was expected due to the

negligible coefficient (-0003) of the country risk measure (SP cCountry rating rank) in Model 7

This result implies that country risk hasdoes not have noany effect on the boundary between

bankrupt and non-bankrupt firms

Model 8 (LR-modelLR model with all variables) includes the four financial ratios and all additional

variables and It leads in all test data to a sizeable increase in AUC (AUC = 0771) compared to

39

the benchmark AUC in all test data However the effect on the AUCs largely varies and is either

negative or positive in different countries The positive effect is large in several countries such

as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative

effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show

that the inclusion of additional variables in the original model will usually increase the AUC but

not in every country

The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with

the USUS firms being unlike the majority of other firms in this study being listed or delisted

companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese

firm samples is associated with very small differences between the medians of the non-failed and

failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable

with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed

firms is very high Prior studies based on Chinese ST -firms have also demonstrated good

predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for

additional research and modeling work regarding unlisted and delisted Chinese firms

63 Country-level data performance of the Zrsquorsquo-Score models

The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform

all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the

different models estimated for each country separately (country-level models) In this table the

ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated

40

from the country data this benchmark will beis clearly outperformed by the resulting MDA

(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and

Sweden) However these results give only weak support for H1 at the country level because the

effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and

2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs

given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed

by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation

method)

Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC

than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3

leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United

StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in

nearly every country supporting H4 This improvement is significant however only in the United

StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark

model but for which it is only 0710 for the benchmark model It This improvement is also notable

in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age

category dummies) gives higher AUCs than the benchmark model for several countries but none

of the improvements are significant The positive effect is particularly strong especially in

Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only

weakly supports H5 because these negative effects are relatively small

41

Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects

on AUCs when compared with the benchmark However the only significant effect is the negative

effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the

industry dummies However there are notable positive effects on the AUC in for example

France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak

support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in

AUC compared with the benchmark model in Colombia Iceland and the Unites States It also

leads and to notable improvements in AUC at least in Belgium Denmark Estonia France

Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are

found only in Austria and Slovenia Thus as a conclusion we can concludestate that the

classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can

remarkably increases in most countries when different effects are taken into account by

additional variables This increase is found in most European countries and in Colombia and the

United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-

modelLR model and it can only be slightly improved only slightly by additional variables

(Table 6 here)

7 Summary of the study and a suggested extension

The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model

originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is

assessed using a very large international data set We test how the original version of the Zrsquorsquo-

42

Score model performs in different countries and how re-estimations using another statistical

method and different additional variables affect the classification performance when the data

are very heterogeneous For this kind of testing seven research hypotheses on classification

performance are extractedformulated These research hypotheses are tested for all data and

separately for country data (country-level analysis) The estimation data are from 31 countries

and the results are validated for 34 countries The countries are mainly from Europe but three

non-European countries are included (China Colombia and the United StatesUS) The statuses

used in the classification are mainly bankruptcyactive but receivership firms are also considered

to have failed In the Chinese data ST (special treatment) and delisted firms are also separately

analyzed as failed firms

The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very

satisfactorily in an international context The effects of the four financial ratios on performance

are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small

contribution in re-estimation The original model performs very well in several countries such as

Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only

marginally improved the classification performance thus weakly supporting the obsolescence

hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust

across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion

holds for the re-estimation of the model using LRA because the performance results are very

similar with to those of MDA (H2) The use of additional variables in the model generally

improvesd the classification accuracy of the original model but the results for countries are

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

39

the benchmark AUC in all test data However the effect on the AUCs largely varies and is either

negative or positive in different countries The positive effect is large in several countries such

as Estonia France Iceland Italy Latvia and China (delisted) However it also has a negative

effect on the AUCs in several countries such as Bosnia Hungary and Norway These results show

that the inclusion of additional variables in the original model will usually increase the AUC but

not in every country

The ldquoall datardquo benchmark also performs fairly well for the USUS and Colombian samples (with

the USUS firms being unlike the majority of other firms in this study being listed or delisted

companies) The poor performance of the predominantly private (CN) and delisted (DL) Chinese

firm samples is associated with very small differences between the medians of the non-failed and

failed groups as shown in Table 5 It is clear that the status ldquodelistedrdquo status is not comparable

with ldquobankruptcyrdquo status When the status is defined as ldquoSTrdquo the predictability of Chinese listed

firms is very high Prior studies based on Chinese ST -firms have also demonstrated good

predictability (Zhang et al 2010 Wang and Campbell 2010) Nevertheless this puzzle calls for

additional research and modeling work regarding unlisted and delisted Chinese firms

63 Country-level data performance of the Zrsquorsquo-Score models

The heterogeneity of the firms and their distributions in ldquoall datardquo makes it difficult for a uniform

all data model to increase AUCs across all countries Table 6 presents the test data AUCs for the

different models estimated for each country separately (country-level models) In this table the

ldquoall datardquo Zrsquorsquo-Score LR-modelLR model acts as the benchmark When the models are estimated

40

from the country data this benchmark will beis clearly outperformed by the resulting MDA

(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and

Sweden) However these results give only weak support for H1 at the country level because the

effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and

2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs

given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed

by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation

method)

Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC

than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3

leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United

StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in

nearly every country supporting H4 This improvement is significant however only in the United

StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark

model but for which it is only 0710 for the benchmark model It This improvement is also notable

in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age

category dummies) gives higher AUCs than the benchmark model for several countries but none

of the improvements are significant The positive effect is particularly strong especially in

Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only

weakly supports H5 because these negative effects are relatively small

41

Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects

on AUCs when compared with the benchmark However the only significant effect is the negative

effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the

industry dummies However there are notable positive effects on the AUC in for example

France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak

support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in

AUC compared with the benchmark model in Colombia Iceland and the Unites States It also

leads and to notable improvements in AUC at least in Belgium Denmark Estonia France

Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are

found only in Austria and Slovenia Thus as a conclusion we can concludestate that the

classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can

remarkably increases in most countries when different effects are taken into account by

additional variables This increase is found in most European countries and in Colombia and the

United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-

modelLR model and it can only be slightly improved only slightly by additional variables

(Table 6 here)

7 Summary of the study and a suggested extension

The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model

originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is

assessed using a very large international data set We test how the original version of the Zrsquorsquo-

42

Score model performs in different countries and how re-estimations using another statistical

method and different additional variables affect the classification performance when the data

are very heterogeneous For this kind of testing seven research hypotheses on classification

performance are extractedformulated These research hypotheses are tested for all data and

separately for country data (country-level analysis) The estimation data are from 31 countries

and the results are validated for 34 countries The countries are mainly from Europe but three

non-European countries are included (China Colombia and the United StatesUS) The statuses

used in the classification are mainly bankruptcyactive but receivership firms are also considered

to have failed In the Chinese data ST (special treatment) and delisted firms are also separately

analyzed as failed firms

The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very

satisfactorily in an international context The effects of the four financial ratios on performance

are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small

contribution in re-estimation The original model performs very well in several countries such as

Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only

marginally improved the classification performance thus weakly supporting the obsolescence

hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust

across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion

holds for the re-estimation of the model using LRA because the performance results are very

similar with to those of MDA (H2) The use of additional variables in the model generally

improvesd the classification accuracy of the original model but the results for countries are

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

40

from the country data this benchmark will beis clearly outperformed by the resulting MDA

(Model 1) and LR (Model 2) models in only in a few countries (Bulgaria France Latvia Spain and

Sweden) However these results give only weak support for H1 at the country level because the

effects are not significant In addition the benchmark leads to higher AUCs than in Models 1 and

2 at least in Austria Bosnia Ireland Slovenia and the United StatesUS The differences in AUCs

given by Models 1 and 2 are generally small In Romania only Model 1 is clearly outperformed

by Model 2 in Romania only Thus country-level evidence does not support H2 (estimation

method)

Model 3 (LR-modelLR model with year dummies) leads in most countries to a clearly higher AUC

than the benchmark model This evidence gives support to H3 (bankruptcy year effect) Model 3

leads to a lower AUC than the benchmark model in Austria Bosnia Slovenia and United

StatesUS Model 4 (LR-modelLR model with size variables) leads to improved performance in

nearly every country supporting H4 This improvement is significant however only in the United

StatesUS where the AUC is nowincreases to 0816 in Model 4 in contrast to the benchmark

model but for which it is only 0710 for the benchmark model It This improvement is also notable

in Bulgaria France Latvia Spain and the United Kingdom Model 5 (LR-modelLR model with age

category dummies) gives higher AUCs than the benchmark model for several countries but none

of the improvements are significant The positive effect is particularly strong especially in

Bulgaria France and Iceland Although there are also negative effects on AUC this evidence only

weakly supports H5 because these negative effects are relatively small

41

Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects

on AUCs when compared with the benchmark However the only significant effect is the negative

effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the

industry dummies However there are notable positive effects on the AUC in for example

France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak

support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in

AUC compared with the benchmark model in Colombia Iceland and the Unites States It also

leads and to notable improvements in AUC at least in Belgium Denmark Estonia France

Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are

found only in Austria and Slovenia Thus as a conclusion we can concludestate that the

classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can

remarkably increases in most countries when different effects are taken into account by

additional variables This increase is found in most European countries and in Colombia and the

United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-

modelLR model and it can only be slightly improved only slightly by additional variables

(Table 6 here)

7 Summary of the study and a suggested extension

The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model

originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is

assessed using a very large international data set We test how the original version of the Zrsquorsquo-

42

Score model performs in different countries and how re-estimations using another statistical

method and different additional variables affect the classification performance when the data

are very heterogeneous For this kind of testing seven research hypotheses on classification

performance are extractedformulated These research hypotheses are tested for all data and

separately for country data (country-level analysis) The estimation data are from 31 countries

and the results are validated for 34 countries The countries are mainly from Europe but three

non-European countries are included (China Colombia and the United StatesUS) The statuses

used in the classification are mainly bankruptcyactive but receivership firms are also considered

to have failed In the Chinese data ST (special treatment) and delisted firms are also separately

analyzed as failed firms

The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very

satisfactorily in an international context The effects of the four financial ratios on performance

are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small

contribution in re-estimation The original model performs very well in several countries such as

Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only

marginally improved the classification performance thus weakly supporting the obsolescence

hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust

across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion

holds for the re-estimation of the model using LRA because the performance results are very

similar with to those of MDA (H2) The use of additional variables in the model generally

improvesd the classification accuracy of the original model but the results for countries are

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

41

Model 6 (LR-modelLR model with industry dummies) shows both negative and positive effects

on AUCs when compared with the benchmark However the only significant effect is the negative

effect found in Austria In Bulgaria and Slovenia the AUC has also notably decreasesd due to the

industry dummies However there are notable positive effects on the AUC in for example

France Iceland Latvia and Sweden Thus the effect is not systematic and gives only weak

support to H6 Model 7 (LR-modelLR model with all variables) leads to a remarkable increase in

AUC compared with the benchmark model in Colombia Iceland and the Unites States It also

leads and to notable improvements in AUC at least in Belgium Denmark Estonia France

Germany Hungary Latvia Norway Spain and the United Kingdom Negative effects on AUC are

found only in Austria and Slovenia Thus as a conclusion we can concludestate that the

classification performance of the Zrsquorsquo-Score model with ldquoall testrdquo data Zrsquorsquo-Score model can

remarkably increases in most countries when different effects are taken into account by

additional variables This increase is found in most European countries and in Colombia and the

United StatesUS In China the AUC for the ST sample is extremely high for the Zrsquorsquo-Score LR-

modelLR model and it can only be slightly improved only slightly by additional variables

(Table 6 here)

7 Summary of the study and a suggested extension

The purpose of this study was to assess the classification performance of the Zrsquorsquo-Score model

originally introduced by Altman (1983) The classification performance of the Zrsquorsquo-Score model is

assessed using a very large international data set We test how the original version of the Zrsquorsquo-

42

Score model performs in different countries and how re-estimations using another statistical

method and different additional variables affect the classification performance when the data

are very heterogeneous For this kind of testing seven research hypotheses on classification

performance are extractedformulated These research hypotheses are tested for all data and

separately for country data (country-level analysis) The estimation data are from 31 countries

and the results are validated for 34 countries The countries are mainly from Europe but three

non-European countries are included (China Colombia and the United StatesUS) The statuses

used in the classification are mainly bankruptcyactive but receivership firms are also considered

to have failed In the Chinese data ST (special treatment) and delisted firms are also separately

analyzed as failed firms

The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very

satisfactorily in an international context The effects of the four financial ratios on performance

are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small

contribution in re-estimation The original model performs very well in several countries such as

Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only

marginally improved the classification performance thus weakly supporting the obsolescence

hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust

across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion

holds for the re-estimation of the model using LRA because the performance results are very

similar with to those of MDA (H2) The use of additional variables in the model generally

improvesd the classification accuracy of the original model but the results for countries are

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

42

Score model performs in different countries and how re-estimations using another statistical

method and different additional variables affect the classification performance when the data

are very heterogeneous For this kind of testing seven research hypotheses on classification

performance are extractedformulated These research hypotheses are tested for all data and

separately for country data (country-level analysis) The estimation data are from 31 countries

and the results are validated for 34 countries The countries are mainly from Europe but three

non-European countries are included (China Colombia and the United StatesUS) The statuses

used in the classification are mainly bankruptcyactive but receivership firms are also considered

to have failed In the Chinese data ST (special treatment) and delisted firms are also separately

analyzed as failed firms

The analyses at the level offor all data show that the original Zrsquorsquo-Score model performs very

satisfactorily in an international context The effects of the four financial ratios on performance

are well balanced although Book Value of Equity Total Liabilities (BVETD) showed a very small

contribution in re-estimation The original model performs very well in several countries such as

Poland Finland and China (ST firms) The re-estimation of the coefficients using MDA only

marginally improved the classification performance thus weakly supporting the obsolescence

hypothesis (H1) or to put it differently showing that the original coefficients are extremely robust

across countries and over time (opposite to eg Grice and Ingram 2001) This same conclusion

holds for the re-estimation of the model using LRA because the performance results are very

similar with to those of MDA (H2) The use of additional variables in the model generally

improvesd the classification accuracy of the original model but the results for countries are

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

43

dependent on the distribution of failed and non-failed firms When the coefficients are estimated

for all data the effects on performance in a country depend on how the distributions in that

country correspond to the distributions in all data For all sets of additional variables the

performance is generally improved but the improvement is not strong and the effects vary

country by country Thus the evidence gives weak support to for the effects of all additional

variables For the effects of bankruptcy year (H3) and size (H4) the effects are stronger but the

variations in the effects between countries are also stronger The effects of age (H5) industry

(H6) and country (H7) are marginal When all additional variables are included in the same model

the performance generally significantly increases but at the same time the variations among

countries become stronger

In summary our evidence indicates that the original Zrsquorsquo-Score model performs well in an

international context It is however possible to extract a more efficient country model for most

European countries and for non-European countries using the four original variables

accompanied with by a set of additional background variables Considering practical applications

it is obvious that while a general international model works reasonably well for most countries

the classification accuracy may be somewhat improved with country-specific estimation (a

conclusion similar to Xu and Zhang 2009) In a country model the information provided even by

simple additional variables may help boost the classification accuracy to a much higher level

In finance and accounting research failure prediction models may be utilized as risk measures in

many different contexts Where failure prediction modeling is not the primary focus it would be

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

44

time-consuming uneconomical and superfluous to first estimate a failure prediction model (or

models) and only then proceed to study the phenomenon of interest In such instances a well-

tested general model that works reliably and consistently across different countries is highly

desirable Based on our empirical tests in this study the original Zrsquorsquo-Score model and its re-

estimated version containing the four Altman (1983) study variables with coefficients re-

estimated using a large data set work consistently well internationally and are easy to implement

and interpret Thus this kind of accounting-based model may can be used by all interested

parties especially internationally active banks or other financial institutions not only for failure

or distress prediction but also for other managerial purposes such as provisioning or and

economic capital calculation Internationally active banks need to develop a universal tool that

can be applied in all subsidiaries and branches to control risk across the whole banking group

Further research should focus on other modifications and extensions than those presented in our

paper such as using alternative modeling techniques (eg panel data analysis) the

introducingction of new variables (eg macroeconomic data) and testing its usefulness on with

data from other countries (eg emerging markets)

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

45

References Acosta-Gonzaacutelez E and F Fernaacutendez-Rodriacuteguez lsquoForecasting Financial Failure of Firms via

Genetic Algorithmsrsquo Computational Economics 43 (2014) pp 133ndash157

Agarwal V and R Taffler lsquoComparing the Performance of Market-Based and Accounting-Based

Bankruptcy Prediction Modelsrsquo Journal of Banking and Finance 32 (2008) pp 1541ndash1551

Altman EI lsquoFinancial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcyrsquo

Journal of Finance 23 (1968) pp 589ndash609

Altman EI Corporate Financial Distress A Complete Guide to Predicting Avoiding and Dealing

with Bankruptcy (Hoboken Wiley Interscience John Wiley and Sons 1983)

Altman EI lsquoRevisiting the Recidivism ndash Chapter 22 Phenomenon in the US Bankruptcy

Systemrsquo The Brooklyn Journal of Corporate Financial amp Commercial Law 8 (2013) p

253

Altman EI and A Saunders lsquoCredit Risk Measurement Developments over the Last 20 Yearsrsquo

Journal of Banking and Finance 21 (1997) pp 1721ndash1742

Altman EI and E Hotchkiss Corporate Financial Distress amp Bankruptcy (third ed John Wiley

2006)

Balcaen S and H Ooghe lsquo35 Years of Studies on Business Failure An Overview of the Classic

Statistical Methodologies and Their Related Problemsrsquo The British Accounting Review 38

(2006) pp 63ndash93

Bauer J and V Agarwal lsquoAre Hazard Models Superior to Traditional Bankruptcy Prediction

Approaches A Comprehensive Testrsquo Journal of Banking and Finance 40 (2014) pp 432ndash

442

Commented [AFS3] Your references and in-text citations have been formatted to conform to your target journalrsquos guidelines posted on the journalrsquos website We encourage you to keep these changes

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

46

Beaver WH lsquoFinancial Ratios as Predictors of Failurersquo Journal of Accounting Research 4 (1966)

pp 71ndash111

Beaver WH lsquoAlternative Accounting Measures as Predictors of Failurersquo The Accounting Review

43 (1968) pp 113ndash122

Bharath ST and T Shumway lsquoForecasting Default with the Merton Distance to Default Modelrsquo

Review of Financial Studies 21 (2008) pp 1339-1369

Campbell JY et al lsquoIn Search of Distress Riskrsquo Journal of Finance 63 (2008) pp 2899ndash2939

Chava S and RA Jarrow lsquoBankruptcy Prediction with Industry Effectsrsquo Review of Finance 8

(2004) pp 537ndash569

Das SR et al lsquoAccounting-Based versus Market-Based Cross-Sectional Models of CDS Spreadsrsquo

Journal of Banking and Finance 33 (2009) pp 719-730

Dichev ID lsquoIs the Risk of Bankruptcy a Systematic Riskrsquo Journal of Finance 53 (1998) pp 1131-

1147

Dimitras AI et al lsquoA Survey of Business Failures with an Emphasis on Prediction Methods and

Industrial Applicationsrsquo European Journal of Operational Research 90 (1996) pp 487ndash

513

Grice JS Jr and MT Dugan lsquoRe-Estimations of the Zmijewski and Ohlson Bankruptcy Prediction

Modelsrsquo Advances in Accounting 20 (2003) pp 77ndash93

Grice JS and RW Ingram lsquoTests of the Generalizability of Altmanrsquos Bankruptcy Prediction

Modelrsquo Journal of Business Research 54 (2001) pp 53ndash61

Hillegeist SA et al lsquoAssessing the Probability of Bankruptcyrsquo Review of Accounting Studies 9

(2004) pp 5ndash34

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

47

Hosmer D and S Lemeshow Applied Logistic Regression (Hoboken Wiley Interscience Prentice

Hall International 1989)

Jackson RHG and A Wood lsquoThe Performance of Insolvency Prediction and Credit Risk Models

in the UK A Comparative Studyrsquo British Accounting Review 45 (2013) pp 183ndash202

Kwak W et al lsquoMultiple Criteria Linear Programming Data Mining Approach an Application for

Bankruptcy Predictionrsquo Data Mining and Knowledge Management 3327 (2005) pp 164ndash

173

Lyandres E and A Zhdanov lsquoInvestment Opportunities and Bankruptcy Predictionrsquo Journal of

Financial Markets 16 (2013) pp 439ndash476

Merkevicius E et al lsquoA Hybrid SOM-Altman Model for Bankruptcy Predictionrsquo International

Conference on Computational Science 3994 (2006) pp 364ndash371

Ohlson JA lsquoFinancial Ratios and the Probabilistic Prediction of Bankruptcyrsquo Journal of

Accounting Research 18 (1980) pp 109-131

Ooghe H and S Balcaen lsquoAre Failure Prediction Models Widely Usable An Empirical Study Using

a Belgian Datasetrsquo Multinational Finance Journal 11 (2007) pp 33-76

Philippe and Partners and Deloitte and Touche Corporate Finance Bankruptcy and a Fresh Start

Stigma on Failure and Legal Consequences of Bankruptcy (Brussels Belgium International

Insolvency Institute 2002)

Pindado J et al lsquoEstimating Financial Distress Likelihoodrsquo Journal of Business Research 61

(2008) pp 995ndash1003

Reisz AS and C Perlich lsquoA Market-Based Framework for Bankruptcy Predictionrsquo Journal of

Financial Stability 3 (2007) pp 85ndash131

Commented [AFS4] We could not verify this reference using online resources Please review this information to ensure that it is correct

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

48

Ribeiro SP et al The OECD ORBIS Database Responding to the Need for Firm-Level Micro-Data

in the OECD No 20101 (OECD Publishing 2010)

Shumway T lsquoForecasting Bankruptcy More Accurately A Simple Hazard Modelrsquo Journal of

Business 74 (2001) pp 101-124

Smith M and D Liou lsquoIndustrial Sector and Financial Distressrsquo Managerial Auditing Journal 22

(2007) pp 376-391

Taffler RJ lsquoEmpirical Models for the Monitoring of UK Corporationsrsquo Journal of Banking and

Finance 8 (1984) pp 199-227

Tinoco M and N Wilson lsquoFinancial Distress and Bankruptcy Prediction among Listed

Companies Using Accounting Market and Macroeconomic Variablesrsquo International

Review of Financial Analysis 30 (2013) pp 394-419

Kumar P and V Ravi lsquoBankruptcy Prediction in Banks and Firms via Statistical and Intelligent

Techniques ndash A Reviewrsquo European Journal of Operational Research 180 (2007) pp 1ndash28

Vassalou M and Y Xing lsquoDefault Risk in Equity Returnsrsquo Journal of Finance 59 (2004) pp 831-

868

Wang Y and M Campbell lsquoBusiness Failure Prediction for Publicly Listed Companies in Chinarsquo

Journal of Business and Management 16 (2010) pp 75-88

Wu Y et al lsquoA Comparison of Alternative Bankruptcy Prediction Modelsrsquo Journal of

Contemporary Accounting and Economics 6 (2010) pp 34ndash45

Xu M and C Zhang lsquoBankruptcy Prediction the Case of Japanese Listed Companiesrsquo Review of

Accounting Studies 14 (2009) pp 534ndash558

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

49

Zhang L et al lsquoCorporate Financial Distress Diagnosis Model and Application in Credit Rating

for Listing Firms in Chinarsquo Frontiers of Computer Science in China 4 (2010) pp 220-236

Zmijewski ME lsquoMethodological Issues Related to the Estimation of Financial Distress

Prediction Modelsrsquo Journal of Accounting Research 22 (1984) pp 59-82

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

50

Tables

Table 1 Number of observations by country

Country Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7430 55 7526 44

Belgium (BE) 179979 2994 179818 2944

Bosnia (BA) 29391 35 29139 32

Bulgaria (BG) 50041 48 42351 44

China (CN) 39315 198

China delisted dataset DL 16291 29

China ST data 846 16 1020 16

Colombia (CO) 8366 139 6982 125

Greece (GR) 51763 28

Croatia (HR) 59541 249 58478 275

Czech Republic (CZ) 92835 556 92562 564

Denmark (DK) 167934 1334 168538 1398

Estonia (EE) 34313 234 34196 242

Finland (FI) 90878 481 91227 459

France (FR) 160749 6124 161653 6318

Germany (DE) 98814 910 99496 921

Hungary (HU) 19421 303 20155 313

Iceland (IS) 17399 248 17624 243

Ireland (IE) 6665 121 6406 139

Italy (IT) 167113 8101 166258 8124

Latvia (LV) 8064 433 8241 477

Lithuania (LT) 10000 56

Netherlands (NL) 20885 154 15854 147

Norway (NO) 172467 1294 170985 1206

Poland (PL) 87200 291 86233 264

Portugal (PT) 180114 3390 178646 3422

Romania (RO) 161992 97 164259 93

Russian Federation (RU) 116903 2534 115711 2481

Serbia (RS) 100100 68

Slovakia (SK) 7856 120 7788 124

Slovenia (SI) 14419 59 14081 41

Spain (ES) 156746 3036 158122 2991

Sweden (SE) 169810 2256 169999 2314

Ukraine (UA) 133342 1787 133980 1765

United Kingdom (GB) 171493 760 170930 716

UK (GB) liquidation dataset 342423

4990

United States (US) 9557 56 9929 53

TIn total 2602563 38215 3148079 43664

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

51

Table 2 Descriptive statistics (all data)

WCTA RETA EBITTA BVETD

Statistic Non-failed Failed Non-failed Failed

Non-failed Failed

Non-failed Failed

Median 0152 -0059 0189 -0024 0041 -0020 0451 0025

Mean 0147 -0213 0188 -0317 0055 -0108 3594 0703

Standard deviation 0442 0604 0509 0767 0227 0296 11499 5712

Upper quartile 0420 0142 0476 0087 0131 0042 1548 0215

Lower quartile -0040 -0440 0011 -0450 -0008 -0208 0100 -0240

Maximum 0956 0956 0958 0958 0785 0785 68606 68606

Minimum -1637 -1637 -2453 -2453 -0828 -0828 -0649 -0649

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

52

Table 3 The coefficients of the different models estimated for all data

Coefficients for different statistical models

Variable Zrsquo-Score

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Constant 325 -0042 0035 0207 -

13466 0007 0048 0049 -

13302

WCTA 656 -0561 -0495 -0483 -0441 -0487 -0540 -0496 -0459

RETA 326 -0724 -0862 -0891 -1146 -0846 -0859 -0863 -1160

EBITTA 672 -1791 -1721 -1790 -1619 -1757 -1695 -1717 -1682

BVETD 105 -0021 -0017 -0016 -0012 -0017 -0016 -0017 -0013

Year dummies

Year 2008 -0055 -0034

Year 2009 -0179 -0150

Year 2010 -0666 -0631

Size variables

Total assets (log) 1830 1837

Total assets squared (log) -0061 -0061

Age dummies

Less than 6 years 0135 0186

15 years or more -0058 -0099

Country risk

SP cCountry rating rank -0003 -0014

Industry dummies

Restaurants and hotels -0653 -0628

Construction 0445 0365

Whole sale and retailing -0112 -0157

Agriculture -0180 -0176

Manufacturing 0139 0095

Energy and water production -0454 -0472

Information technology -0913 -0915

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

53

Significance

Coefficients are statistically significant at 00001 Models

Zrsquo-Score = Original Altman (1983) Zrsquo-Score Model coefficients

Model 1 = The MDA model

Model 2 = The LR model

Model 3 = The LR model estimated for all data with year dummies

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

54

Table 4 Test data AUCs for different countries based on all data model versions Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

All dData 0748 0743---- 0745---- 0752++++ 0760++++ 0748+++ 0751++++ 0749++++ 0771++++

Countries with estimation data

Austria (AT) 0800 0788 0797 0805 0818+ 0814+ 0782 0800 0819

Belgium (BE) 0772 0760 0770 0777 0747 0777 0765 0772 0758

Bosnia (BA) 0862 0805 0857 0776 0863 0847 0855 0862 0784

Bulgaria (BG) 0684 0630 0680 0654 0691 0680 0659 0684 0632

China ST companies 0985 0911---- 0983 0958- 0977 0978 0987 0985 0968

Colombia (CO) 0726 0724 0727 0715 0758 0728 0726 0726 0757

Croatia (HR) 0844 0812 0839 0803 0837 0835 0832 0844 0801

Czech Republic (CZ) 0811 0813 0811 0819 0828 0807 0820 0811 0838

Denmark (DK) 0803 0798 0800 0781 0813 0802 0801 0803 0796

Estonia (EE) 0823 0827 0823 0847 0866 0826 0833 0823 0890

Finland (FI) 0867 0864 0866 0835 0862 0870 0878 0867 0853

France (FR) 0739 0723 0735 0749 0771 0741 0762 0739 0799

Germany (DE) 0673 0658 0666 0695 0656 0684 0677 0673 0688

Hungary (HU) 0742 0746 0740 0660 0738 0755 0735 0742 0696

Iceland (IS) 0664 0674 0666 0694 0678 0673 0672 0664 0716

Ireland (IE) 0679 0672 0676 0708 0677 0681 0688 0679 0712

Italy (IT) 0806 0799 0804 0833 0835 0806 0799 0806 0849

Latvia (LV) 0678 0691 0678 0704 0676 0686 0698 0678 0724

Netherlands (NL) 0752 0754 0750 0775 0769 0754 0746 0752 0787

Norway (NO) 0716 0694 0713 0658 0682 0720 0715 0716 0645

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

55

Poland (PL) 0903 0904 0904 0908 0902 0903 0899 0903 0904

Portugal (PT) 0741 0724 0736 0749 0773 0738 0755 0741 0785

Romania (RO) 0758 0740 0754 0709 0749 0755 0748 0758 0703

Russian Federation (RU) 0811 0802 0812 0843 0799 0807 0799 0811 0814

Slovakia (SK) 0777 0774 0776 0780 0811 0769 0786 0777 0808

Slovenia (SI) 0737 0725 0733 0674 0740 0747 0718 0737 0721

Spain (ES) 0734 0713 0732 0707 0793 0728 0753 0734 0774

Sweden (SE) 0813 0801 0809 0799 0784 0817 0823 0814 0800

Ukraine (UA) 0708 0714 0710 0715 0721 0708 0702 0708 0722

United Kingdom (GB) 0699 0719 0699 0686 0736 0695 0706 0699 0729

United States (US) 0710 0701 0711 0709 0722 0705 0716 0710 0723

Countries only in test data

European

Greece (GR) 0715 0670 0702 0725 0711 0713 0717 0715 0718

Lithuania (LT) 0767 0782 0767 0764 0768 0769 0775 0767 0778

Serbia (RS) 0736 0713 0730 0603- 0826 0720 0753 0736 0738

United KingdomUK lLiquidation dataset 0606 0621 0603 0620 0618 0610 0607 0607 0635

Non-European

China (CN) 0558 0570 0557 0572 0543 0554 0567 0558 0556

China delisted firms DL 0529 0546 0519 0563 0740++++ 0542 0520 0529 0707+++

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The original Altman (1983) Zrsquorsquo-Score Model

Model 2 = The MDA model estimated for all data

Model 3 = The LR model estimated for all data with year dummies

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

56

Model 4 = The LR model estimated for all data with size variables

Model 5 = The LR model estimated for all data with age category dummies

Model 6 = The LR model estimated for all data with industry dummies

Model 7 = The LR model estimated for all data with country risk rankings

Model 8 = The LR model estimated for all data with all variables

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

57

Table 5 Differences of medians between non-failed and failed groups

Country WCTA RETA EBITTA BVETD AUC of

Z-Score

Austria (AT) 0448 0405 0126 0487 0788

Belgium (BE) 0223 0264 0077 0431 0760

Bosnia (BA) 0106 0249 0088 0580 0805

Bulgaria (BG) 0114 0291 0094 0436 0630

China (CN) 0068 0032 0016 0280 0570

China delisted data DL 0089 -0061 -0032 0083 0546

China ST data 0298 0293 0139 0468 0911

Colombia (CO) 0226 0235 0099 0705 0724

Croatia (HR) 0275 0335 0044 0274 0812

Czech Republic (CZ) 0404 0397 0069 0667 0813

Denmark (DK) 0267 0356 0046 0733 0798

Estonia (EE) 0331 0388 0113 1033 0827

Finland (FI) 0402 0606 0207 0907 0864

France (FR) 0140 0213 0065 0424 0723

Germany (DE) 0131 0136 0032 0262 0658

Greece (GR) 0171 0262 0049 0349 0670

Hungary (HU) 0175 0224 0052 0587 0746

Iceland (IS) 0246 0261 0051 0337 0674

Ireland (IE) 0181 0266 0039 0560 0672

Italy (IT) 0277 0164 0073 0207 0799

Latvia (LV) 0117 0120 0042 0254 0691

Lithuania (LT) 0246 0218 0051 0569 0782

Netherlands (NL) 0204 0253 0077 0432 0754

Norway (NO) 0157 0219 0115 0329 0694

Poland (PL) 1340 0920 0124 1351 0904

Portugal (PT) 0215 0200 0052 0318 0724

Romania (RO) 0222 0271 0056 0298 0740

Russian Federation (RU) 0350 0242 0069 0245 0802

Serbia (RS) 0120 0148 0045 0389 0713

Slovakia (SK) 0256 0184 0061 0431 0774

Slovenia (SI) 0111 0172 0035 0326 0725

Spain (ES) 0143 0143 0076 0285 0713

Sweden (SE) 0255 0346 0099 0663 0801

Ukraine (UA) 0204 0200 0031 0449 0714

United Kingdom (GB) 0211 0245 0033 0472 0719

UK lLiquidation dataset 0156 0234 0031 0447 0621

United States (US) 0195 0378 0248 0722 0701

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

58

Average of column items 0245 0265 0073 0481 0740

Correlation with Z-Score AUC 0611 0681 0516 0574 1000

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal Assets

BVETD = Book Value of EquityTotal Liabilities

SALTA = SalesTotal Assets

AUC = Area under the ROC curve

Z-Score = Altman (1983) Z-Score in the test data

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

61

61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

59

Table 6 Test data AUCs for different estimated country models Comparisons are with the AUC of the LR model estimated for all data (benchmark)

Test data AUC for different models

Country Benchmark Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Austria (AT) 0800 0782 0770- 0774 0829 0787 0707- 0764

Belgium (BE) 0772 0779 0780 0826 0776 0783 0781 0834

Bosnia (BA) 0862 0850 0832 0833 0838 0838 0820 0853

Bulgaria (BG) 0684 0785 0785 0785 0796 0794 0677 0707

China ST companies 0985 0984 0982 0980 0986 0986 0986 0989

Colombia (CO) 0726 0755 0754 0818+ 0789 0749 0774 0824+

Croatia (HR) 0844 0862 0858 0865 0853 0863 0847 0863

Czech Republic (CZ) 0811 0812 0812 0823 0830 0813 0818 0842

Denmark (DK) 0803 0806 0809 0833 0818 0804 0813 0843

Estonia (EE) 0823 0824 0839 0866 0874 0839 0847 0899

Finland (FI) 0867 0867 0870 0885 0868 0873 0879 0894

France (FR) 0739 0773 0799 0805 0826 0800 0810 0845

Germany (DE) 0673 0684 0685 0734 0704 0701 0690 0762

Hungary (HU) 0742 0736 0743 0788 0767 0765 0747 0820

Iceland (IS) 0664 0670 0671 0784+ 0683 0714 0708 0824+

Ireland (IE) 0679 0663 0666 0718 0661 0665 0680 0724

Italy (IT) 0806 0802 0810 0835 0839 0809 0816 0868

Latvia (LV) 0678 0690 0707 0745 0720 0711 0730 0771

Netherlands (NL) 0752 0752 0756 0768 0784 0760 0759 0807

Norway (NO) 0716 0741 0741 0803 0749 0744 0743 0814

Poland (PL) 0903 0891 0897 0915 0899 0896 0895 0913

Portugal (PT) 0741 0753 0757 0761 0780 0758 0765 0797

Romania (RO) 0758 0718 0773 0805 0757 0755 0753 0774

Russian Federation (RU) 0811 0808 0810 0854 0811 0811 0818 0862

Slovakia (SK) 0777 0773 0775 0782 0810 0761 0773 0807

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

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Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

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United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

60

Slovenia (SI) 0737 0703 0719 0704 0711 0739 0686 0698

Spain (ES) 0734 0755 0767 0768 0849 0766 0776 0858

Sweden (SE) 0813 0826 0829 0843 0831 0831 0892 0901

Ukraine (UA) 0708 0712 0712 0720 0742 0718 0718 0758

United Kingdom (GB) 0699 0708 0713 0718 0764 0714 0731 0785

United States (US) 0710 0689 0687 0686 0816+ 0689 0714 0816+

Average of AUC 0768 0773 0778 0801 0799 0782 0779 0823

Significance

AUC better than benchmark 00001 = ++++ 0001 = +++ 001 = ++ 01 = +

AUC worse than benchmark 00001 = ---- 0001 = --- 001 = -- 01 = -

Models

Benchmark = The LR model estimated for all data with Zrsquorsquo-model (1983) variables

Model 1 = The MDA model estimated for country data

Model 2 = The LR model estimated for country data

Model 3 = The LR model estimated for country data with year dummies

Model 4 = The LR model estimated for country data with size variables

Model 5 = The LR model estimated for country data with age category dummies

Model 6 = The LR model estimated for country data with industry dummies

Model 7 = The LR model estimated for country data with all variables

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61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

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United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

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61

Appendix Medians of the Altman (1983) model (Z) variables by status and country

Non-failed Failed

Non-failed Failed

Non-failed Failed Non-failed Failed

Country WCTA

RETA

EBITTA

BVETD

Austria (AT) 0170 -0278 0260 -0145 0055 -0071 0465 -0022

Belgium (BE) 0136 -0087 0157 -0107 0052 -0025 0460 0029

Bosnia (BA) 0087 -0019 0148 -0101 0032 -0056 0580 0000

Bulgaria (BG) 0216 0102 0272 -0019 0075 -0019 0504 0068

China (CN) 0105 0037 0064 0032 0064 0048 0870 0590

China delisted data DL 0158 0069 0286 0347 0059 0091 0923 0840

China ST data 0106 -0192 0281 -0012 0052 -0087 0776 0308

Colombia (CO) 0244 0018 0282 0047 0104 0005 0940 0235

Croatia (HR) 0093 -0182 0130 -0205 0030 -0014 0274 0000

Czech Republic (CZ) 0196 -0208 0230 -0167 0050 -0019 0591 -0076

Denmark (DK) 0128 -0139 0306 -0050 0005 -0041 0758 0025

Estonia (EE) 0222 -0109 0455 0067 0050 -0063 1169 0136

Finland (FI) 0233 -0169 0359 -0247 0082 -0125 0744 -0163

France (FR) 0146 0006 0223 0010 0059 -0006 0501 0077

Germany (DE) 0307 0176 0150 0014 0069 0037 0335 0073

Greece (GR) 0127 -0044 0049 -0213 0039 -0010 0460 0111

Hungary (HU) 0135 -0040 0271 0047 0054 0002 0717 0130

Iceland (IS) 0051 -0195 0046 -0215 0051 0000 0196 -0141

Ireland (IE) 0198 0017 0383 0117 0029 -0010 0728 0168

Italy (IT) 0107 -0170 0077 -0087 0032 -0041 0178 -0029

Latvia (LV) 0102 -0015 0150 0030 0069 0027 0342 0088

Lithuania (LT) 0205 -0041 0273 0055 0054 0003 0686 0117

Netherlands (NL) 0204 0000 0303 0050 0065 -0012 0511 0079

Norway (NO) 0185 0028 0139 -0080 0068 -0047 0398 0069

Poland (PL) 0198 -1142 0258 -0662 0076 -0048 0850 -0501

Portugal (PT) 0164 -0051 0133 -0067 0028 -0024 0343 0025

Romania (RO) 0071 -0151 0158 -0113 0041 -0015 0250 -0048

Russian Federation (RU) 0088 -0262 0106 -0136 0043 -0026 0194 -0051

Serbia (RS) 0093 -0027 0129 -0019 0027 -0018 0389 0000

Slovakia (SK) 0137 -0119 0185 0001 0060 -0001 0496 0065

Slovenia (SI) 0109 -0002 0215 0043 0039 0004 0434 0108

Spain (ES) 0117 -0026 0139 -0004 0029 -0047 0336 0051

Sweden (SE) 0266 0011 0357 0011 0058 -0041 0749 0086

Ukraine (UA) 0053 -0151 0034 -0166 0003 -0028 0442 -0007

United Kingdom (GB) 0179 -0032 0294 0049 0031 -0002 0579 0107

UK lLiquidation dataset 0179 0023 0294 0060 0031 0000 0579 0132

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United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages

62

62

United States (US) 0164 -0031 0374 -0004 0003 -0245 0800 0078

Average of medians 0153 -0092 0213 -0050 0048 -0025 0555 0075

Legend

WCTA = Working CapitalTotal Assets

RETA = Retained EarningsTotal Assets

EBITTA = EBITTotal assets

BVETD = Book Value of EquityTotal Liabilities Because the two datasets for non-failed UK firms are identical the non-failed medians are used only once in the

calculation of the averages