FINANCIAL DISTRESS RISK AND STOCK RETURNS: EVIDENCE OF THE MALAYSIAN STOCK MARKET

MOHD AZHAR BIN MOHD YUSOF

FACULTY OF BUSINESS AND ACCOUNTANCY UNIVERSITY OF MALAYA

WRITTEN RESEARCH PROJECT REPORT

Financial Distress Risk and Stock Returns: Evidence of the Malaysian Stock Market

Mohd Azhar bin Mohd Yusof

Bachelor of Business Administration (Honours) (Finance)

Universiti Teknologi MARA 1998/1999

Submitted to the Graduate School of Business Faculty of Business and Accountancy

University of Malaya, in partial fulfilment of the requirements for the

Master of Business Administration

5 December 2008

ii

ABSTRACT

If financial distress risk can be accurately predicted, the stock price of high

distress risk firms should be discounted so as to enable investors to earn higher

expected returns. This is true if the distress risk is undiversifiable or systematic.

This study sets out a direct approach to examining the risk-return relationship of

distress-listed companies in Malaysia. Using Z-Score bankruptcy prediction

model as the proxy of distress risk and the subsequent realised stock returns of

the distress-listed companies as a proxy of systematic risk, this study finds that

the distress risk and the size and book-to-market equity effect are not statistically

significant enough to explain the expected stock returns. It is also found that the

theoretical expectation of the size and book-to-market equity effect on distress

risk also does not hold in the case of the Malaysian distress listed-firms.

However, similar to the findings of Griffin and Lemmon (2002), there is evidence

of a significant inverse relationship between distress risk and book-to-market

equity indicating that Malaysian distress listed-companies with higher probability

of distress risk display lower book-to-market value of equity ratio. This study

reports that it is inconclusive to deduce that distress risk is a systematic risk in

relation to the Malaysian stock market.

iii

ACKNLOWLEDGEMENT

In the name of God, the Beneficent, the Merciful

First and foremost, I wish to express my deepest gratitude to Prof. Madya Dr. M.

Fazilah Abdul Samad for her invaluable ideas, time, patience and continued

support towards completion of this paper.

I would also like to extend my gratitude to Keng Fui Chai, Yusuf Ma Pin and

Asyha Hui for their kind input and assistance during the course of this paper.

My special thanks to my family, especially to my wife and kids for their enormous

support, patience, encouragement, understanding and sacrifices rendered

throughout the MBA programme.

Lastly, my sincere appreciation to the lecturers, administrative personnel and

fellow course mates that have made my journey through the MBA programme an

unforgettable memory.

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TABLE OF CONTENT Page

Abstract ii Acknowledgement iii Table of content iv List of figures v List of tables vi List of appendices vii List of symbols and abbreviation viii CHAPTER 1: INTRODUCTION............................................................................1

1.1 Background...........................................................................................1 1.2 Purpose and Significance of the Study .................................................7 1.3 Objective of the Study...........................................................................9 1.4 Scope of the Study ............................................................................. 10 1.5 Limitation of the Study ........................................................................ 15 1.6 Organisation of the Study ................................................................... 16

CHAPTER 2: LITERATURE REVIEW................................................................ 17

2.1 Introduction ......................................................................................... 17 2.2 Empirical Studies on Bankruptcy Prediction ....................................... 20 2.3 Empirical Studies on Bankruptcy Risk and Stock Returns .................. 25 2.4 Empirical Studies on Size and Book-to-Market Equity Effect.............. 29

CHAPTER 3: RESEARCH METHODOLOGY.................................................... 33

3.1 Introduction ......................................................................................... 33 3.2 Sample Selection................................................................................ 35 3.3 Data Collection Procedures ................................................................ 37 3.4 Data Analysis Techniques .................................................................. 39 3.5 Research Hypotheses......................................................................... 42

CHAPTER 4: RESEARCH FINDINGS ............................................................... 44

4.1 Normality Test..................................................................................... 44 4.2 Descriptive Analysis............................................................................ 48 4.3 Predictive Ability of the Z-Score.......................................................... 51 4.4 Results from the Statistical Tests........................................................ 52

CHAPTER 5: CONCLUSION AND RECOMMENDATIONS .............................. 60

5.1 Summary and Conclusion................................................................... 60 5.2 Suggestion for Future Study ............................................................... 61

References Appendices

v

LIST OF FIGURES

Page

Figure 1: Initial scatter plots 44

Figure 2: Final scatter plots and histograms 46

vi

LIST OF TABLES

Page

Table 1: Total number of distress-listed companies from 2001 to

2007

11

Table 2: Selective chronological order of past failure prediction

research

17

Table 3: Failure prediction models 18

Table 4: Statistics for normal distribution test 47

Table 5: Shapiro-Wilk test 48

Table 6: Descriptive statistics 48

Table 7: Trend on average ratios and Z-Score for Y-1 to Y-3 50

Table 8: Tabulation of Z-Score by the predetermined cut-off score 52

Table 9: T-test for the realised stock returns for Y-1, Y-2 and Y-3 53

Table 10: T-test for the MV for Y-1, Y-2 and Y-3 54

Table 11: T-test for the BMV for Y-1, Y-2 and Y-3 55

Table 12: Pearson correlation 56

Table 13: Summary of empirical findings 59

vii

LIST OF APPENDICES

Page

Appendix 1: List of identified distress listed-companies up to 31

December 2007 based on announcements made on

Bursa Securities

69

Appendix 2: Key enhancement of the criteria of affected listed issuers

in APN17

74

Appendix 3: List of distress listed-companies selected as samples 75

Appendix 4: Ratios of the sample companies for Y-1, Y-2, Y-3 and 3-

year average

78

Appendix 5: Variables of sample companies for Y-1, Y-2, Y-3 and 3-

year average

81

Appendix 6: Average return, MV and BMV of the sample companies

for Y-1

85

Appendix 7: Average return, MV and BMV of the sample companies

for Y-2

88

Appendix 8: Average return, MV and BMV of the sample companies

for Y-3

91

viii

LIST OF SYMBOLS AND ABBREVIATION

APN17 Amended Practice Note 17 of the Listing Requirements

BMV Book-to-market equity ratio

Bursa Malaysia Bursa Malaysia Berhad

Bursa Securities Bursa Malaysia Securities Berhad

Listing Requirements Listing requirements of Bursa Securities for the Main

Board and the Second Board

LR Logistics regression

MV Market value

PN17 Practice Note 17 of the Listing Requirements

PN4 Practice Note 4 of the Listing Requirements

Z-Score Altman’s (1968) Z-Score bankruptcy prediction model

1

CHAPTER 1: INTRODUCTION

1.1 Background

Corporate failures are common in the competitive business environment where

market forces ensure the survival of the fittest. It is synonymous to an event of

default which refers to various events relating to financial distress including the

inability to meet debt payments, debt restructuring, filing for bankruptcy protection

or even winding up. Financial distress risk1 is the uncertainty introduced by the

method by which the company finances its investments. If a firm uses only

common stock to finance its investments, it incurs only business risks. If a firm

borrows money to finance its investments, it has to pay fixed financing charges in

the form of interest. Thus, the ability to meet the financing obligations to the

lenders determine the degree of financial distress of a company. In addition,

these financing charges have priority over the distribution of income to the

shareholders and hence the uncertainty of returns to the common equity

shareholders increases. The uncertainty of returns to the shareholders leads to

higher risk premium required of the stock.

Earlier studies have identified several factors that lead to corporate failures. For

example, Argenti (1976) identifies lack of accounting information, firm

unresponsiveness to changes, failure of big projects, changes and hostility in the

environment, normal business hazards, lack of financial control, overtrading, high

1 Similar to Dichev (1998), the term “bankruptcy risk” and “financial distress” are used loosely and interchangeably for the purpose of this study.

2

gearing and competition as causes of corporate decline. Similarly, high cost

structure, failure of major projects, poor acquisition, lack of financial control and

weak financial policy were also amongst the major factors that lead to corporate

decline found by Slatter (1984). Heng et. al (1995) analysed ten potential factors

for causes of corporate insolvency in small, medium and large size organisation

in Malaysia and found that five factors namely lack of financial control, failure of

major projects, weak financial policy, poor management and competition cause

insolvency in small size organisations whilst lack of financial control and failure of

major projects seem to influence medium size organisation. However, their study

shows that none of the causal factors applies towards large size organisation.

Ferri et. al (1998) reported that the problems of corporate financial structures in

East Asian corporations (including Malaysia) have been an important factor in

contributing to the Asian financial crisis which lead many corporations to

bankruptcy.

Corporate sector can be viewed as a broad representative of the producers group

of the economy. A recovery in the corporate sector can be viewed as getting the

country’s industrial output and economy growing again through new capital

investment, increased capacity utilisation and higher production. In short,

business investment activities must be revived in order to sustain the economic

recovery process. Therefore, it is imperative to keep in check corporate financial

distress since it could lead to serious corporate failure i.e. bankruptcy if left

unattended.

3

Corporate failure can be very costly in many ways. Not only it represents a drain

on the external value of the assets underlying an investors’ claim, it is also an

indication of resource misallocation which leads to undesirable social

implications. Documented evidence from developed economies (Altman, 1984)

showed that the total costs of bankruptcy are substantial and firms incur

bankruptcy costs in the range of 11% to 17% of the firm value three years prior to

bankruptcy. The economic cost of business failures are relatively large with direct

bankruptcy costs amounting to around 1% to 5% of market value of the

organisation (Warner, 1977; Altman, 1983). Opler and Titman (1994) reported

that highly leveraged firms in financial distress tend to lose substantial market

share. In a study relating to bankruptcy costs, Kaplan (1994) finds that the

estimated gains from the bankruptcy-induced financial restructuring process

exceeded the cost.

Branch (2002) categorised bankruptcy-related costs into four areas: (1) real costs

borne by the distressed firm; (2) real costs borne directly by the claimants; (3)

losses to the distressed firm that are offset by gains to other entities; and (4) real

costs borne by parties other than the distressed firm or its claimants. Branch

argues that cost categories 1, 2, and 3 are relevant for claimants, while

categories 1, 2, and 4 are relevant for society. Focusing on the first three

categories, Branch (2002) deduces that after allowing for their costs of

collections–

(a) claims holders recover approximately 56% of the bankrupt firm’s pre-

distress value;

4

(b) dealing with financial distress generally consumes between 12% and 20%

of the distressed firm’s pre-distress value; and

(c) taking the midpoint of this range (16%) implies that the losses that lead to

the firm’s distress average approximately 28% of its pre-distress value.

With much study has been directed at the factors leading to corporate failures,

researchers interest are drawn on the ability to predict impending failures through

some common identifiable attributes. A consistent pattern of changes in these

attributes can help formulate and implement pre-emptive measures to avoid such

failures. A reliable approach to predict corporate failures accurately and in a

timely manner is very much needed as this effort could reduce the costs of

bankruptcy, avoid financial distress to all stakeholders and contribute towards the

business and financial environment stability. Nam and Jinn (2000) provide a

valuable insight into failure prediction during the Asian financial crisis in Korea, by

documenting that the sampled companies showed signs of financial distress well

before the Asian financial crisis in 1997, implying that failures could be predicted

and prevented resulting in substantial financial and social costs savings.

As mentioned by Neill and Pfeiffer (2005), research in corporate restructuring

argues that the risk of bankruptcy reduces the firm value by present value of both

the direct and indirect costs of bankruptcy. Additionally, the potential for

bankruptcy affects both the investment horizon of investors and the discount rate

implicit in equity values. By estimating bankruptcy probabilities for an extensive

sample of more than 38,000 firm-year observations over a twelve-year period

using a valuation model that employs both book value and earnings, Neill and

5

Pfeiffer (2005) show that earnings multiples decrease as the estimated probability

of bankruptcy increases, which imply that investors and analyst rely less on

current earnings and more on book value as a firm’s probability of bankruptcy

increases.

In actual fact, the study on financial distress has drawn considerable interest

since 1930s, focusing mainly on the ability to predict the occurrence of such

event. Distress prediction models provide useful information to the stakeholders

such as management and investors as the early warning signs derived from

these models allow the stakeholders to take preventive measures in order to

minimise the expected losses incurred.

Ever since the study by Ramser and Foster (1931) on the characteristics of

healthy and unhealthy firms using financial ratios, numerous empirical research

have been undertaken to accurately and reliably predict corporate failures such

as Fitzpatrick (1932), Mervin (1942), Beaver (1966), Altman (1968), Deakin

(1972), Altman et. al (1977) and Ohlson (1980) to name a few. There are also

similar studies using non-U.S. data include Joo-Ha and Taehong (2000) for

Korea, Neophytou and Mar-Molinero (2004) for United Kingdom and Shirata

(1995, 1998) and Takahashi et. al (1984) for Japan.

It is argued that the research findings from developed economies are not suitable

to apply to Malaysian firms due to the differences in market structures, socio-

economic factors, provision and implementation of law, the political environment

and accounting standards in these economies, which result in differences in

6

financial reporting (Her and Choe, 1999). Based on the need to develop a model

that can predict corporate failures in the context of Malaysian firms, successful

models were reported to have been developed by Fauzias and Chin (2002),

Muhamad Sori et. al (2006) and Rosliza (2006) to name a few. The model

developed by Rosliza (2006) indicates that overall classification accuracy for

discriminant analysis approach produces slightly better results with 84% accuracy

compared to logistics regression approach of 83%.

There have also been considerable academic attention given to the bankruptcy or

financial distress risk and stock returns relationship of late. Studies have

indicated that financial distress risk is related to the size (i.e. market value of

equity), and book-to-market value of equity ratio of a firm. Chan and Chen (1991)

found that small firms are mainly marginal firms in distress i.e. firms with low

production efficiency and high financial leverage. Similarly, Fama and French

(1995) and Chen and Zhang (1998) show that firms with high book-to-market

equity are firms that are relatively in distress. Empirical studies have also shown

that size and book-to-market effects are powerful predictors of stock returns. For

example, Rosenberg et. al (1985) show that stocks with high book-to-market

equity value outperform the market. Chan and Chen (1991) show that the book-

to-market ratio has most significant positive impact on expected returns whilst

Fama and French (1992) reported high average returns on negative and high

book-to-market equity firms. Meanwhile, Chen and Zhang (1998) show that value

stocks – characterised by low market value relative to a typical stock (size effect)

and low market price relative to book (book-to-market equity effect) – offer

7

reliably higher returns in the United States, Japan, Hong Kong and Malaysia,

corresponding to the higher risk.

1.2 Purpose and Significance of the Study

Although the evidence of size and book-to-market effects on stock returns is

considerably strong based on past studies, it is still inconclusive to generalise

that distress risk is a systematic risk. Classical asset pricing theory suggests that

the stock price of a firm with higher risk should be discounted to enable investor

to earn higher expected returns. This is especially true in the case where the risk

is undiversifiable. Based on this premise, if the distress risk is undiversifiable or

systematic, stock price of firms with high financial distress risk should be

discounted to allow investors to earn higher expected return. Of course, this is

based on the assumption that the capital market agents in the aggregate use the

financial statements information as though it was filtered though a multivariate

bankruptcy prediction model (Dugan and Forsyth, 1995) in pricing the distress-

listed stocks.

However, past studies are inconclusive on the impact of financial distress risk on

stock returns i.e. there were mixed results on whether distress risk is systematic

or unsystematic. Lang and Stulz (1992) and Denis and Denis (1995) in their

studies found that bankruptcy risk could be positively related to systematic risk.

Meanwhile, Shumway (1996) found that NYSE and AMEX listed firms with high

risk of delisting earns higher than average returns, suggesting that the risk of

default is systematic. In contrast, Dichev (1998) shows that bankruptcy risk are

8

negatively related to the stock returns in the United States, which suggest that

distress risk is unsystematic.

Interestingly, Griffin and Lemmon (2002) using O-Score as proposed by Ohlson

(1980) find that group of firms with the highest risk of distress include many firms

with high book-to-market ratios and low past stock returns, but actually include

more firms with low book-to-market ratios and high past stock returns.

Meanwhile, Vassalou and Xing (2004) found some evidence that distressed

stocks, mainly in small value group, earn higher returns. Whereas further

examination by Chava and Purnanandam (2008) show a significant and positive

relationship between default-risk and expected returns pre-1980 but lower

average realised return than the expected return post-1980 in the United States.

It is argued in Dichev (1998) and Griffin and Lemmon (2002) that the inverse

relationship between distress risk and stock returns could be due to mispricing.

Lakonishok et. al (1994) suggest that mispricing arises from investors

extrapolating past operating performance too far into the future.

Whilst study by Chen and Zhang (1998) indirectly implies that distress risk is

systematic, it is still inconclusive to generalise that there is a positive relationship

between distress risk and stock returns in the case of the Malaysian stock

market. Therefore, the purpose of this study is to provide an empirical evidence

of the risk-return relationship of distress-listed companies in Malaysia in a direct

approach using Altman’s (1968) Z-Score bankruptcy prediction model as the

proxy of distress risk and the subsequent realised stock returns of the distress-

9

listed companies, which can also presents as a proxy for systematic risk.

Although there have been wide criticism on the model developed by Altman

(1968) in term of the statistical procedures employed i.e. the time “bias” and the

sample which only consists manufacturing firms, the Z-Score model continues to

be the one most cited and used. Bengley et. al (1997) find that Altman’s Z-Score

models retain their predictive abilities for COMPUSTAT firms in the 1980s.

Meanwhile, Grice and Ingram (2001) found that the Z-Score model is also useful

for predicting financial distress conditions other than bankruptcy.

This study will also bring to light whether the findings by Dichev (1998) hold in

other market besides in the United States. In a similar study on the Japanese

market, it was found that bankruptcy risk and stock returns positive relationship is

only significant during the bubble period from 1985 to 1992. The stock returns

appear to be unrelated to bankruptcy risk in normal periods.

In addition to the above, this study also seeks to further validate the size and

book-to-market equity effect in the context of the Malaysian firms. This is due to

their well-documented association with returns and to test the hypothesised

relationship between size and book-to-market equity and the distress risk.

1.3 Objective of the Study

The aim of this study is mainly to provide an empirical evidence of risk-return

relationship of the distress-listed companies in Malaysia.

10

Therefore, the objectives of this study can be summarised as follows:

(a) To examine the relationship between stock returns and financial distress

risk of distress-listed companies in Malaysia;

(b) To test the evidence of size and book-to-market equity effect on distress

risk in the context of Malaysian distress-listed firms;

(c) To revisit the applicability of Altman’s Z-Score in predicting financially

distress-listed companies as defined under the Listing Requirements of

Bursa Malaysia Securities Berhad (Bursa Securities) (formerly known as

Kuala Lumpur Stock Exchange or KLSE) for the Main Board and the

Second Board (Listing Requirements); and

(d) To deduce whether financial distress risk is systematic risk in relation to

the Malaysia stock market.

1.4 Scope of the Study

This study covers all public companies listed on the Main Market2 of Bursa

Securities Malaysia Berhad (Bursa Securities) which have been classified as

distress-listed companies under the Practice Note 4 (PN4), Practice Note 17

(PN17) and Amended PN17 (APN17) of the Listing Requirements from year 2001

to 2007. The scope of the study is restricted from year 2001 onwards mainly

because PN4 was first introduced in 2001.

2 The Main Market is referring to the Main Board and Second Board of Bursa Securities. The other trading market on Bursa Securities is the MESDAQ Market.

11

Based on the media releases and announcements made on Bursa Securities, a

total of 194 distress-listed issuers have been identified, of which the breakdown is

as shown in Table 1 below:

Table 1: Total number of distress-listed companies from 2001 to 2007

PN 4

Companies

PN17

Companies

APN17

Companies

Total

125 23 46 194

Source: Author’s compilation

The detailed list of companies identified as distress-listed issuer is as shown in

Appendix 1.

In general, PN4 which took effect from 15 February 2001 set out the following

matters:

(a) The criteria in relation to the financial condition of a listed company;

(b) The requirements to be complied with by the listed companies which meet

the prescribed criteria (affected listed issuer);

(c) The time schedule for affected listed issuers to regularise their financial

condition; and

(d) The actions that may be taken against the affected listed issuers by Bursa

Securities.

A listed issuer is deemed as an affected listed issuer under PN4 if it fulfils one or

more of the following criteria:

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(a) Deficit in the adjusted shareholders’ equity of the listed issuer on a

consolidated basis;

(b) Receivers and/or managers have been appointed over the property of the

listed issuer, or over the property of its major subsidiary or major

associated company which accounts for at least 70% of the total assets

employed of the listed issuer on a consolidated basis;

(c) The auditors have expressed adverse or disclaimer opinion in respect of

the listed issuer’s going concern in its latest audited accounts; or

(d) Special administrators have been appointed over the listed issuer or the

major subsidiary or major associated company of the listed issuer

pursuant to the provisions of the Pengurusan Danaharta Nasional Berhad

Act 1998.

The aim of PN4 is to give effect to a requirement in the revamped Listing

Requirements that the financial position of all listed companies must warrant its

continued listing on the stock exchange. In this context, PN4 was introduced for

the following reasons:

(a) Ensure that affected listed issuers take positive steps to restructure within

the stipulated time frame;

(b) Protect the interest of investors by ensuring sufficient disclosure of

information on the status of a company’s restructuring plans and

staggered enforcement as an when a company fails to comply with its

obligations (e.g. trading restrictions followed by suspension and finally de-

listing); and

13

(c) Protect the integrity of the Malaysian stock exchange as a premium

exchange by ultimately de-list companies which have failed to improve

their financial condition after having been given the opportunity to do so.

Thus under the PN4, affected listed issuers are required to make a First

Announcement informing the market of their PN4 status within 7 market days of

them failing within any of the PN4 criteria. Subsequently, affected listed issuers

are generally given–

(a) six months from the date of the First Announcement to make Requisite

Announcement regarding their plans to regularise their financial

conditions;

(b) two months to subsequently make their submissions to all relevant

authorities for approval; and

(c) four months to obtain all necessary approvals for the implementation of

their plans.

According to media release by Bursa Securities, there were a total of 89 listed

companies which were subjected to PN4 when it was first introduced in February

2001. As at 29 November 2004, a total of 72 listed companies have successfully

regularised their financial condition, 40 listed companies are still categorised

under PN4 whist a total of 16 companies had been de-listed since the

introduction of PN4.

However, due to long and complex de-listing process, PN4 companies have

managed to buy time to maintain its listing status on the pretext of the company is

14

in the midst of formulating a comprehensive restructuring plan. Consequently,

PN17 was introduced by Bursa Securities on 30 November 2004 with the aim of

expediting the time taken by distress-listed companies to regularise its financial

condition and level of operations (Bursa Securities, 2004). Under the PN17, listed

companies with unsatisfactory financial conditions and level of operations (other

than cash companies) will have 8 months to submit their regularisation plans to

the relevant authorities for approval. In the event that the affected listed issuers

fail to do so, their securities will automatically be suspended on the 5th market

day after expiry of the 8-month period and de-listing procedures will be

undertaken against such companies, unlike the PN4 whereby there is no

requirement on automatic suspension of securities upon failure to comply with

obligations within given timeframes.

Subsequently, on 5 May 2006, APN17 was introduced by Bursa Securities with

the key objectives of to enhance the quality of listed issuers and to further

strengthen investor protection and promote investor confidence (Bursa

Securities, 2006). According to Bursa Securities, the amendments in APN17 are

aimed at expediting the restructuring efforts of the affected listed issuers and

those with inadequate level of operations. With the amendments, affected listed

issuers are compelled to commence restructuring early before their condition

worsens and undertake more meaningful restructuring in an expeditious manner

that will results in sustainable financial health. The key enhancement on the

criteria of affected listed issuers in APN17 is illustrated in Appendix 2.

15

1.5 Limitation of the Study

This study does not include healthy companies i.e. listed companies not

classified as distress-listed companies as control samples. As such, no

comparison is being made on the results of financial distress risk and stock

returns relationship between healthy and non-healthy companies. This is mainly

based on the premise that the main objective of the study is to examine the

relationship of stock returns and financial distress risk of Malaysian distress-listed

companies instead of the ability to predict the sign of distress risk which requires

the discrimination between healthy and unhealthy firms.

This study relies on the availability of the financial information as contained in the

annual reports or audited financial statements of the identified distress-listed

companies on the Bursa Malaysia Berhad’s (Bursa Malaysia) website. Certain

distress listed-companies have to be omitted from the sample in view of the

unavailability of their annual reports or audited financial statements from the

Bursa Malaysia website. Therefore, the results of this study may vary should the

financial information of those omitted distress listed-companies are made

available and included in the sample.

In addition, this study uses Z-Score by Altman (1968) as the proxy of distress

risk. While it has been argued that the research findings from developed

economies are not suitable to apply to Malaysian firms, the application of distress

risk prediction model developed to suit the Malaysian market may produce

different results from this study.

16

1.6 Organisation of the Study

The remaining of this paper has been organised in the following manner:

Chapter 2 presents reviews of the literatures on bankruptcy risk prediction and

past studies on distress risk and stock returns.

Section 3 presents on the methodology employed in the study in which the

research hypotheses, sampling and data collection procedures and analysis

techniques employed will be discussed in detail.

The findings of the study will be presented in Chapter 4 and followed by the

conclusion and recommendations in Chapter 5.

17

CHAPTER 2: LITERATURE REVIEW

2.1 Introduction

The issues of corporate failure are not new in the area of financial research.

Since 1930s and over the subsequent 60 years, various types of failure prediction

models have been developed to accurately predict the occurrence of financial

distress. Table 2 shows the selective past failure prediction research and its

contributors.

Table 2: Selective chronological order of past failure prediction research

Period Description Contributors

1930s Different financial characteristics between

healthy and unhealthy firms

1931 – Ramser and Foster

1932 – Fitzpatrick

1935 – Winnakor and Smith

1942 – Merwin

1960s Bankruptcy prediction models using

univariate and multivariate analysis

1966 – Beaver

1968 – Altman

1970 – Meyer and Pifer

1972 – Deakin

1974 – Blum

1977 – Altman et. al

1980s Improvement in the prediction model using

logit, probit, etc.

1980 – Ohlson

1983 – Zavgren

1984 – Zmijewski

1990s Improvement in the prediction model and

accuracy using neural network, decision

tree, genetic algorithm, etc.

1993 – Serrano Cinca et. al

1994 – Back et. al

1994 – Wilson and Sharda

1999 – Tae, Namsik, Gunhee

Present Revalidation of the prediction model under

normal versus crisis condition

New research areas

Source: Rosliza (2006)

18

Table 3 below summarised the models of failure prediction available.

Table 3: Failure prediction models

Type of Models Derivation

Univariate Multivariate

Interative (simulation) a) Experimental (credit scoring)

b) Recursive Partitioning

c) Artificial Intelligence

d) Neural Networking

Statistical a) Conventional ratio analysis

b) Systematical ratio analysis

c) Balance sheet decomposition

d) Gambler’s Ruin

a) Descriminant analysis

b) Regression analysis

c) Logit/probit analysis

d) Expanded logit

e) Survival analysis

Behavioural Reaction a) Share price

b) Matched pair controls

c) Systematic factor controls

d) Laboratory experiments

e) Subjects informed of failure rate

f) Subjects not informed of failure

rate

Source: Morris (1998)

The documented findings on corporate failures in developed economies provide

some general guide on characteristics of firms that fail. However, these findings

cannot be generally applied to firm failures in different economic environments

such as emerging markets due to differences in market structure, provision and

implementation of law, accounting and corporate governance standards.

Predicting corporate failure is based on the premise that there are identifiable

patterns or symptoms consistent for all failed firms and failure is a gradual

process. These symptoms might take the form of declining profits, working

capital, liquidity, asset quality, arrears interest and loan repayment, delay in

payment to suppliers, staffs and other creditors, and implementation of some

19

form of austerity measures. The consistent changes in these common symptoms

enable researchers to formulate corporate prediction models in an effort to

identify the potential failures and implement corrective measures to avoid and/or

minimise the cost of failures in cases where failure is inevitable.

Apart from the early detection of the sign of potential corporate failures, the

bankruptcy prediction models may be used as a tool in trading strategies to earn

high or abnormal returns. According to Chen and Zhang (1998), over the years

investors have been searching for "value stocks'' that promise high returns.

These “value stocks” can be identified having the following characteristics: a low

market price relative to its book value (BMV effect); a low market price relative to

earnings or cash flow (cash flow effect); a low market value relative to a typical

stock (size effect); a low market price relative to dividends (dividend effect); and a

low market price relative to the historical price (contrarian).

Chen and Zhang (1998) imply that the value stocks are riskier because there are

usually firms under distress, have high financial leverages, and face substantial

uncertainty in future earnings. They found that the value stocks offer reliably

higher returns in the United States, Japan, Hong Kong and Malaysia,

corresponding to the higher risk.

Katz et. al (1985) suggest that trading strategies for earning abnormal returns

may be developed by following the signals of corporate distress or recovery.

They discovered that for the 15-month period prior to the issuance of annual

report that triggered a shift in state, firms classified by the Z-Score as recovering

20

from distress showed significant abnormal positive returns while those classified

as deteriorating showed significant abnormal negative returns.

2.2 Empirical Studies on Bankruptcy Prediction

Dugan and Forsyth (1995) in their study categorised previous research on

bankruptcy prediction into two broad categories. The first category entails the

development of models that can discriminate between healthy and non-healthy

company which rely mostly on the variables (i.e. accounting ratios) derived from

the financial statements of the sample companies. The second category of

research related to bankruptcy prediction attempts to relate bankruptcy prediction

model to the behaviour of stock prices.

Although accounting ratio models have no theoretical foundation, observations

made from the early studies in the 1930s have concluded that financial ratios

vary systematically between healthy and failing firms. Companies with weak and

unstable financial ratios are more likely to fail than companies with stronger and

more stable financial ratios. Ramser and Foster (1931), Fitzpatrick (1932),

Winakor and Smith (1935) and Merwin (1942) reported significant difference

between the comparable financial ratios of failed and non-failed firms.

Later in the 60s, Beaver (1966) presented a univariate analysis that documented

the predictive ability of financial ratios to correctly classify both failed and non-

failed firms to a much greater extent than would be expected from random

prediction. Beaver (1966) analysed 79 pairs of bankrupt and non-bankrupt firms

21

over the period from 1954 to 1964. From the samples, he accurately classified

78% of the firms five years before failure occurred. He concluded that “based

solely upon knowledge of the financial ratios, the financial state of the firm can be

correctly predicted to a much greater extent than would be expected from a

random prediction”. By adopting the dichotomous classification technique, Beaver

found that the cash flow to total debt ratio was the best predictor of failure.

However, there exists argument that univariate analysis is prone to faulty

interpretation as ratios were analysed in isolation i.e. one at a time. As a matter of

fact, Beaver (1966) did suggest in his study that it is possible a multi-ratio

analysis using several different ratios would predict even better than single ratio.

In view of the above, there was a need for a predictive model based on

multivariate approach that can take into account the interaction effects between

the ratios. This leads to a breakthrough in bankruptcy prediction with the

discovery of Z-Score model by Altman (1968) which employed MDA technique.

He investigated a set of financial and economic ratios as possible determinants of

corporate failure. The study used 66 corporations from manufacturing industries

comprising of bankrupt and non-bankrupt firms and 22 ratios from five categories,

namely, liquidity, profitability, leverage, solvency and activity ratios. Five ratios

were finally selected for their performance in the prediction of corporate

bankruptcy. The derived Z-Score model correctly classified 95% of the total

sample (correctly classifying 94% as bankrupt firms and 97% as non-bankrupt

firms) one-year prior to bankruptcy. The percentage of accuracy diminishes with

increasing number of years before bankruptcy.

22

Subsequently, Altman et. al (1977) presented a more robust model known as

ZETA model which can be applied to larger firms with no limitations to specific

business. The ZETA model is found to be more accurate in classifying

bankruptcy up to five prior to the failure based on a sample of companies in the

manufacturing and retailing industry compared to the original Z-Score model.

Although it was found to be successful in predicting bankruptcy, criticisms were

made on the Z-Score model in relation to the statistical approach employed. The

MDA was criticised as it violates the multivariate normality assumptions. Karels

and Prakash (1987) stressed that MDA procedure will be optimal only if the

normality conditions are met. Another criticism on the Z-Score model is that time

“bias” might have been incorporated into the classic business failure model of

which was developed in the late 1960’s. Bengley et. al (1997) studied the issue of

time bias by applying the Z-Score model to a matched sample of failed and non-

failed firms from the 1980’s and found that predictive accuracy was significantly

reduced when applied to the 1980’s data.

Another notable research in bankruptcy prediction is by James A. Ohlson who

successfully developed a model known as O-Score. Ohlson (1980) employed the

conditional logit model to predict distress probabilities and identified four basic

factors in affecting probability of bankruptcy within one year namely (1) the firm

size; (2) the financial structure; (3) the firm performance; and (4) the current

liquidity. The greater the O-Score, the higher the bankruptcy risk.

23

Muhamad Sori et. al (2001) developed a failure prediction model for Malaysian

industrial sector listed firms that discriminates between 24 failed and non-failed

for the period 1980 to 1996. They found that their model correctly and

significantly classified 91.1% and 89.3% of the failed and non-failed firms

respectively. On the other hand, they constructed an alternative prediction model

developed based solely on accounting information also showed similar results.

These models predict failure up to 4 years before the actual event.

In spite of the wide usage of ratio analysis in determining the likelihood of

financial distress, there has been little agreement between the researchers on the

best accounting ratios in the prediction models. Hamer (1983) suggested the

ability of models to predict failure was relatively independent of the ratios

selected. On the other hand, Karels and Prakash (1987) advocated careful

selection to improve prediction accuracy.

Having analysed over 25 ratios from the literature survey of corporate failure,

Bhattacharyn (1995) observed that funding was the all pervading variable

affecting the health of a firm. To determine the financial health of a firm, he

derived a single comprehensive ratio known as health ratio. The health ratio

could explain analytically the health condition of a firm at its various stages of

growth and decline.

Nuha (1996) explored five categories of financial ratios ranging from liquidity,

profitability, leverage, solvency and activity using 70 samples of industrial

companies listed on Bursa Securities by applying the MDA. The study was based

24

on data ranging from 1984-1992. He adopted Tobin-Q Ratio to separate the

samples into two categories namely high value companies and lower value

companies. The lower value companies were defined as facing higher risk of

bankruptcy. The findings suggested that a distinctive difference in financial ratios

of bankruptcy vis-à-vis non-bankrupt companies exists.

Using MDA and Logistic Regression (LR) and based on 448 total observations

selected from 32 matched samples of healthy and unhealthy firms in Malaysia

from 1998-2004, Rosliza (2006) found that liquidity ratios play the most important

role when determining the reasons for corporate failures in Malaysia. In addition,

she also found that MDA approach produces slightly better results with 84%

accuracy compared to LR approach of 83%.

In another study by Ng (2000) using Section 176 companies in Malaysia as

samples, he found that the Z-Score was effective in detecting signs of bankruptcy

for all sectors, save and except for finance sector, for a period of 3 years

preceding the year of classification. The model has an accuracy rate of 73% in

predicting corporate failure.

Altman (2000) examined distress companies over three separate periods i.e.

from 1969-1975, 1976-1995 and 1997-1999 using Z-Score model. In the

repeated case, the evidence highlighted that the accuracy of Z-Score has been in

the range of 80% to 90% based on data from one financial reporting period prior

to bankruptcy.

25

The abovementioned literatures revealed the ability of financial ratios as

predictors of financial distress. In general, profitability, liquidity and solvency

ratios were found to be the highly significant indicators in most of the studies.

However, their order of importance is unclear since almost every study has

produced different findings. Another conclusion that can be drawn from the above

literatures is that the existing bankruptcy prediction models have been

considerably successful in accurately predicting corporate failures. The notable

models are Z-Score which has been able to predict bankruptcy within 80% - 90%

accuracy in different time period and the ZETA model which is reported to be

accurate in classifying bankruptcy up to five prior to the failure.

2.3 Empirical Studies on Bankruptcy Risk and Stock Returns

Several studies have argued for a “default risk component" within the well-known

factors that have successfully accounted for the cross section of stock returns.

This argument implies that investors would demand a premium for investing in

firms with high risk of default and, consequently, high default risk should be

associated with high expected returns in the cross section. Using different

measures of probability of default, the existing empirical literatures have failed to

produce consistent evidence to confirm the above conjecture. In fact, some

studies have documented the opposite result, i.e. stocks of companies with a

higher probability of default usually earn lower returns. A common interpretation

of this empirical evidence is that, when it comes to default, markets seem to be

less capable of fully assessing the risk embedded in a company and do not

demand a sufficiently high premium to compensate for the risk of default.

26

If financial distress risk can be accurately predicted, the stock price of high

distress risk firms should be discounted so as to enable investors to earn higher

expected returns. This is to compensate for the high uncertainty in obtaining the

anticipated return and opportunity costs of investing in lower risk asset. The rate

of discount on the stock price however depends on extent to which the

bankruptcy risk cannot be diversified. In one of the most prominent and widely

used asset pricing model in finance i.e. the Capital Assets Pricing Model (CAPM)

by Sharpe (1964), Lintner (1965) and Black (1972), systematic risk is reflected by

the volatility of return of a stock against the market return i.e. the beta. As the

beta goes higher, the required rate of return of the stock will also be higher and

this will determined the degree of the price discounts.

Extensive cross-sectional and time series analysis conducted earlier indicated

certain risk measures based on market data exhibit significantly different

behaviour between failed and non-failed firms. Aharony et. al (1980) studied 45

industrial firms which went bankrupt during 1970 – 1978. The data indicated a

significant negative cumulative differential portfolio return around four years

before bankruptcy. The unexpected deterioration in the bankruptcy group was

high with investors constantly adjusting for declining solvency up to the time of

bankruptcy. These results suggested that a solvency deterioration signal using

capital market data was available for some four years before the happening of

the bankruptcy event.

Altman and Brenner (1981) concluded that the bankrupt firms experienced

deteriorating capital market returns for at least one year prior to bankruptcy. Clark

27

and Weinstein (1983) indicated that financially distressed firms exhibited negative

market returns for at least three years preceding the occurrence of the event.

Katz et. al (1985) observed that abnormal stock returns occurred for the 12

months period immediately preceding and immediately following the release of

financial information for firms that were deteriorating and for firms that were

recovering as classified by the Z-Score model. The findings indicated that the

market was not entirely efficient for the bankruptcy prediction model to capture

the information released or the information not captures has an impact on market

behaviour.

If distress risk is systematic as suggested by Lang and Stulz (1992) and Denis

and Denis (1995), the subsequent realised returns of firms with high bankruptcy

score should be positive. However, studies by Opler and Titman (1994) and

Asquith et. al (1994) show that bankruptcy risk is mostly due to idiosyncratic risk

which suggest that there might be no significant positive relation between

bankruptcy risk and expected returns.

Shumway (1996) found that New York Stock Exchange (NYSE) and American

Stock Exchange (AMEX) firms with high risk of exchange delisting for

performance reasons earn higher than average returns. However, Dichev (1998)

found that stock returns are in fact negatively related to the bankruptcy risk in the

US for the period from 1981 to 1995. In a similar study on the Japanese stock

market, it was found that there is positive relationship between returns and

bankruptcy risk. However, it was only significant during the period from 1985 to

28

1992 which is considered as bubble period. In normal periods, stock returns

appear to be unrelated to the bankruptcy risk.

Taffler (1999) expanded Dichev’s study by adopting Fama-Macbeth methodology

which took into account other potential key determinants of the return generating

process. The results confirmed that financially distress companies, on average,

under-performed by 3% to 4% per year subject to size, beta, book-to-market and

momentum. However, there was also evidence of such returns being a function

of economic state variables over time. Weak Z-score firms out-performed healthy

firms in the period leading up to 1987 economic crash in the United Kingdom.

Unlike Dichev’s conclusion, Taffler (1999) implied that the rational capital pricing

model cannot be rejected.

Zavgren et. al (1988) tested the association between market reaction and

unanticipated failure or survival using probit technique. Non-failed firms that had

been predicted to fail were expected to demonstrate abnormal positive returns

upon recognised survival. The opposite holds true for failed firms. However, firms

predicted by the model to survive but actually fail, experienced no dramatic

market reaction in the year prior to failure. It supported the hypotheses that

publicly available information which was not contained in the annual financial

statements influenced stock market behaviour.

Dugan and Forsyth (1995) concluded that to a certain extent, market agents use

financial statements information in investment decisions. Nonetheless, the

association exists was not necessarily liner or monotonic.

29

While the past studies are inconclusive on the impact of bankruptcy on stock

returns, it has nevertheless acknowledge the relationship between business

failure and stock market returns and the significant differences shown by ex-post

samples.

2.4 Empirical Studies on Size and Book-to-Market Equity Effect

The history of modern investment theory have seen significant research being

done to develop models that are able to measure the risks in investments and

convert them into expected returns. Thus, the birth of CAPM in the 60’s was a

breakthrough in the asset pricing theory. Although there have been wide

criticisms on the empirical record of the CAPM, the model is still arguably the

default model for measuring market risk in finance. According to Aswath

Damodaran, the CAPM is a remarkable model insofar as it captures an asset’s

exposure to all market risk in one number i.e. the asset’s beta.

The CAPM implies that the expected stock returns are a positive linear function of

their market beta and the market beta suffice to describe the cross-section of

expected returns. This is based on the premised that if stocks are priced

rationally, systematic differences in the average returns are due to differences in

risk (Fama and French, 1995).

However, studies have also shown that the size and book-to-market equity ratio

of a firm can be a strong predictor of expected stock returns. Banz (1981) found

that market value of equity adds to the explanation of the cross-section of

30

average returns provided by market beta whereby the average returns on small

(low market value of equity) stocks are too high given their beta estimates, and

average returns on large stocks are too low. Fama and French (1992) in their

studies pointed out another contradiction of the CAPM whereby based on a study

by Bhandari (1988), leverage has been found to have positive relationship with

average return of stocks. Under the CAPM, leverage risk should be captured by

market beta. However, Bhandari found that leverage helps explain the cross-

section of average stock returns in tests that include size as well as beta.

Rosenberg et. al (1985) found that average returns on U.S. stocks are positively

related to the book-to-market equity. Meanwhile, Chan et. al (1991) found that

book-to-market equity has a strong role in explaining the cross-section of average

returns on Japanese stocks. Fama and French (1993) show that size and book-

to-market equity ratio proxy for sensitivity to risk factors that capture strong

common variation in stock returns and help explain the cross-section of average

returns. Subsequent study by Fama and French (1995) further validates that size

and book-to-market equity ratio is related to risk factors in stock returns.

Chan and Chen (1991) argued that small firms (i.e. in term of market value) on

the New York Stock Exchange tend to be firms that are less efficiently run and

with high financial leverage. In a cross-sectional test, they find out that the size

has reliable explanatory power on the dispersion of returns among the size

portfolios.

31

In a study on the risk and return of value stocks, Chen and Zhang (1998)

concluded that value stocks are riskier because they are usually firms under

distress. Value stocks are characterised by low market value relative to a typical

stock (size effect) and low market price relative to book (book-to-market equity

effect). They also found that these value stocks offer reliably higher returns in the

United States, Japan, Hong Kong and Malaysia, corresponding to the higher risk.

Using Ohlson’s O-Score to examine the relationship between book-to-market

equity, distress risk and stock returns, Griffin and Lemmon (2002) found that

among firms with the highest distress risk, the difference in returns between high

and low book-to-market equity securities is more than twice as large as return in

other groups and is driven by extremely low returns by firms with low book-to-

market equity. In addition, Zaretzky and Zumwalt (2007) also found that the

highest distress risk firms that are small and those that accumulated large losses

display low BMV values, which therefore suggest that the relation between

distress risk, BMV and return is found to be inconsistent with the BMV factor

representing a premium to compensate for the risk of financial distress. This is in

contrast to previous findings by Chan and Chen (1991), Fama and French (1992,

1993, 1995) and Chan and Zheng (1998) which all suggested that firms with high

book-to-market equity show higher returns as a compensation for higher risk

factor. However, the anomalous findings by Griffin and Lemmon (2002) was also

found earlier by Dichev (1998) whereby from a portfolio results, he found that

firms with higher book-to-market equity firms actually earned lower average

returns in comparison to firms with lower book-to-market equity.

32

The above literatures reveal the existence of relationship between size and book-

to-market equity with financial distress and stock returns. In general, firms in

distress tend to have low market value (small size). This scenario could be due to

investors discounting the price of the stock in anticipation of the distress risk so

as to earn higher expected return in the future. Consequently, with lower market

value, the firms in distress book-to-market value of equity will be higher.

Therefore, it can be argued that the findings Chan and Chen (1991), Fama and

French (1992, 1995) and Chan and Zheng (1991) are consistent with the asset

pricing theory. Nevertheless, the empirical findings by Dichev (1998) and Griffin

and Lemmon (2002) may suggest that the evidence of size and book-to-market

effect is still inconclusive.

33

CHAPTER 3: RESEARCH METHODOLOGY

3.1 Introduction

This study uses the Z-Score as a proxy of distress risk which took the following

form:

Z = 1.2*X1 + 1.4*X2 + 3.3*X3 + 0.6*X4 + 1.0*X5

where X1 = Working capital/Total assets

X2 = Retained earnings/Total assets

X3 = Earnings before interest and taxes/Total assets

X4 = Market value of equity/Book value of total debt

X5 = Sales/Total assets

Z = Overall index

The working capital/total assets ratio measures the liquid assets in relation to the

firm’s size (working capital is defined as the difference between current assets

and current liabilities). The retained earnings/total assets ratio measures the

cumulative profitability that reflects the firm’s age as well as earning power. The

earnings before interest and taxes/total assets ratio measures the operating

efficiency separated from any leverage and tax effects. The market value of

equity/book value of total debt ratio adds a market dimension into the model. It

reflects how much the firm’s assets can decline in value before the liabilities

exceed the assets and the firm becomes insolvent. The sales/total assets ratio is

34

a standard turnover measure which indicates the sales generating ability of the

firm’s assets.

To assess firm’s likelihood of bankrupt, the Z-Score result will be compared with

the predetermined cut-offs shown below:

Non-bankrupt > 2.99

Zone of ignorance 1.81 – 2.99

Bankrupt < 1.81

Any score which is greater than 2.99 represents the company is in the non-

distress zone while a score of less than 1.81 indicates the company is on a

distress position. The zone of ignorance of 1.81 – 2.99 or also known as the “grey

area” contained both distress and non-distress companies. This means that the

higher value of the Z-Score signify lower distress risk and vice versa.

For the purpose of this study, the size (MV) is defined as log (market price of

share multiplied by the number of outstanding issued and paid-up share capital

as at financial year end) and book-to-market equity ratio (BMV) is calculated as

the book value of equity as at the end of the financial year divided by the market

value of the equity as at the same financial year end.

35

3.2 Sample Selection

The population of this study is made up of all public listed companies on Bursa

Securities which have been classified as affected listed issuers under PN4, PN17

and APN17 from 2001 to 2007. The affected listed-issuers were identified based

on the announcement made on media release by Bursa Securities and

announcements made by the affected listed-issuers. Distress companies under

the financial sector are excluded from the sample to avoid possible interference

on the overall findings due to their nature of business which is different from other

sectors.

The samples for this study were selected using the following procedure:

(a) The affected listed-issuer must be listed on Bursa Securities for a period of

at least three years prior to the classification date. This is to allow for

comparison of the financial information for a different time period;

(b) The financial information of the affected listed-issuer must be available for

the past three financial years prior to the classification date;

(c) The affected listed issuer must be classified as distress listed-company

mainly due to deterioration in its financial position. This is mainly because

company which is not essentially in distress financial position may be

classified as affected listed-issuer under the PN17 and APN17 due to

insignificant business or operations3. For example, in the case of Petaling

Tin Berhad, according to its announcement dated 13 January 2005, the

3 Under the practice notes, "insignificant business or operations" means business or operations which generates revenue on a consolidated basis that represents 5% or less of the issued and paid-up capital (excluding any redeemable preference shares) of the listed issuer based on its latest annual audited or unaudited accounts.

36

company was classified as affected listed issuer due to significant drop in

its consolidated revenue for the financial year ended 31 October 2004.

The significant drop in revenue was mainly arising from the group's

rationalisation of its property development projects;

(d) The trading of the affected listed-issuers’ shares must remain active i.e.

not suspended for at least eighteen months after the financial year end

preceding the date of classification as an affected listed-issuer. This is to

allow for an analysis on the effect of the announcement of the company’s

results on the stock price; and

(e) To avoid duplication of sample, if an affected listed issuer was classified

under more than one category of distress listed-company, only the first

category will be taken as the sample. For example, if the listed company is

classified as an affected listed issuer under the PN4 and subsequently

under PN17, this study will only take the listed company as a PN4 affected

listed issuer.

After the filtering process based on the above procedures, a total of 98 affected

listed-issuers have been selected as the samples for this study of which consist

43 PN4 companies, 18 PN17 companies and 37 Amended PN17 companies. The

list of the selected affected listed-issuers as samples for the study is as set out in

Appendix 3.

37

3.3 Data Collection Procedures

This study is conducted primarily using secondary data. Financial data of the

selected samples were extracted from the published annual reports obtained

from the Bursa Malaysia’s website. The period of analysis covers three financial

years prior to the year of classification as affected listed-issuer, identified as Y-1,

Y-2 and Y-3 respectively. Y-1 refers to the most recent financial year end before

the sample was classified as an affected listed issuer.

For example, if the company has its financial year ended as at 30 June and it was

classified as a PN4 company in February 2001, the Y-1 refers to the financial

year ended 30 June 2000. Thus, the financial information to calculate the

financial ratios under the Z-Score model will be sourced from the annual reports

for the financial year ended 30 June 2000. Accordingly, Y-2 and Y-3 refer to

financial ratios sourced from the annual reports for the financial year ended 30

June 1999 and 30 June 1998 respectively.

For each period of review:

(a) Information on the samples’ consolidated current assets, current liabilities,

total assets, retained earnings, earning before interest and tax (EBIT),

number of outstanding issued and paid-up share capital, book value of

debt, sales and book value of equity are extracted as of each financial

year-end;

38

(b) The market value of the distress stocks is calculated as the market price

of share multiplied by the number of outstanding issued and paid-up share

capital as at financial year end;

(c) Subsequently, the financial ratios of X1 to X5 are computed using the

identified formula; and

(d) Z-Score for each sample are derived over the period by applying the

Altman’s formula.

The stock returns of the selected samples will be the 12-month average monthly

returns starting from the beginning of the seventh month until the end of the

eighteenth month after the financial year end. This is based on the premise that

listed companies are required to announce its annual reports not later than six

months after the financial year end under the Listing Requirements. A cursory

review of the announcement of the listed companies’ annual reports on Bursa

Securities shows that by the sixth month after the financial year end, most of the

listed companies have released its annual reports to Bursa Securities. This is

also to minimise the “bias” on the movement of the stock price in any particular

month. The stock return is mainly based on the capital gain or loss from investing

in the distress listed-stocks and it is assumed that no divided has been paid

during the period of three financial years prior to the classification date and after

the classification date.

The monthly closing market price of the selected samples is extracted from

Bloomberg Financial Services and the following formula is used to calculate the

average 12-month stock returns of the samples:

39

(Pit – Pit-1)

Rit = �

Pit-1

12

Where Rit = Average monthly return (in percentage) of stock i;

Pit = Closing market price of stock i on trading month t;

Pit-1 = Closing market price of stock i on trading month t-1;

t = +7 to +18 months after the financial year end; and

i = affected listed issuers selected as samples.

3.4 Data Analysis Techniques

Analysis and comparison of the test variables used in this study are done within

the identified sample of the distress-listed companies. The test variables for this

study are stock return, Z-Score, MV and BMV. This study utilises Statistical

Package for Social Sciences (SPSS) Version 13.0 for Windows to analyse the

test variables. The SPSS has been chosen for the data analysis because it is

widely used tool for statistical analysis.

(a) Normality Test

Prior to analysing the data, the scatter plot, Shapiro-Wilk, skewness and kurtosis

statistical tests are used to check the normality of the data. Scatter plot is a two-

dimensional graph, displaying data points of two variables which can provide

initial information about the relationship between the two variables such as the

strength, shape, direction and as well as presence of outliers. The closer the data

40

points to make a straight line, the higher the correlation between the two

variables, or the stronger the relationship. Even though a scatter plot shows a

relationship between variables, it does not indicate a cause and effect

relationship. The scatter plot is used in this study to identify presence of outliers

and to remove it from the samples.

Shapiro-Wilk test which is designed to detect all departures from normality is

used in view that the sample size is less than 100. The Shapiro-Wilk test rejects

the hypotheses of normality when the p-value is less than or equal to 0.05 i.e. if

the p-value of the Shapiro-Wilk test is higher than 0.05, it can be deduced that

the data fit the normal distribution.

Skewness and kurtosis refer to the shape of the distribution. Values for skewness

and kurtosis are zero if the observed distribution is normal. Positive values for

skewness indicate an observation that is positively skewed and vice versa.

Positive values for kurtosis indicate a distribution that peaked (leptokurtic) while

negative values for kurtosis indicate a distribution that is flatter (platykurtic).

Since the main focus of the study is on the stock returns and distress risk, the

normality test is undertaken only on the distribution of stock returns and Z-Score.

For the purpose of this study, it is assumed that the distribution MV and BMV is

normal. The average score of 3-year average monthly realised returns and Z-

Score is used for the normality test purpose.

41

(b) T-test

To examine the returns of the distress-listed-companies, the portfolio returns of

the distress-listed companies are analysed by segregating the companies into

two portfolios namely distress firms and most distress firms based on the distress

probability score. The distress firms consist of the distress listed-companies with

positive Z-Scores and the most distress firms include the distress listed-

companies with negative Z-Scores. The test is to ascertain whether the stock

returns of distress firms are different from the most distressed firms. The portfolio

returns of the distress firms and most distress firms are compared for the 3-year

period.

To test whether there are significant differences in the size and book-to-market

equity of the distress-listed-companies, similar approach is applied whereby the

distress listed-companies are segregated into two portfolios namely distress firms

and most distress firms based on the distress probability score. The test is to

ascertain whether the size and book-to-market equity of the distress listed-

companies are different from the most distressed firms.

The t-test has been used in all of the above tests. By employing the t-test, we can

determine whether the differences in each of the above tests are statistically

significant. It is assumed under the t-test that the sample scores meet the

normality condition.

42

(c) Correlation Analysis

The statistical significant difference in the t-test is just showing some differences

in the trend of the variables. The t-test however does indicate the correlation

between the variables. Therefore, in the second part of the statistical test, the

strength of the relationship between the variables is tested. Correlation analysis

is conducted for this purpose. Pearson correlation coefficient, r, is used to

measure the strength of relationship between stock returns, distress risk, MV and

BMV. It ranges from value -1.0 to +1.0 which measures the degree t which two

variables are linearly related. If there is a perfect positive correlation (+1.0), their

variables will tend to move simultaneously in the same direction and vice versa. A

value of zero indicates that there is no relationship at all between the variables.

The 3-year average of the variables is used for the correlation analysis.

3.5 Research Hypotheses

As conjectured by the rational asset pricing theory and assuming the market is

semi-strong efficient, distress risk should be rewarded with positive subsequent

realised returns. As suggested by Chan et. al (1991), Shumway (1996) and Chen

and Zhang (1998), firms with high distress risk earn higher positive returns.

Therefore, this study tests the following hypotheses–

H1: Stock returns are positively related to distress risk.

H2: There is a difference between the mean returns of the distress firms and

most distress firm.

43

Empirical findings have also suggested that stock returns and financial distress

risk is related to the size and book-to-market effect as discovered by Chan and

Chen (1991), Fama and French (1992, 1995), Dichev (1998), Chen and Zhang

(1998) and Griffin and Lemmon (2002). Therefore, it is also hypothesised that –

H3: Stock returns are negatively related to the firm size.

H4: Stock returns are positively related to book-to-market equity.

H5: Distress risk is negatively related to the firm size.

H6: Distress risk is positively related to the book-to-market equity.

H7: There is a difference in the mean size of the distress firms and most

distress firms.

H8: There is a difference in the mean book-to-market equity of distress firms

and most distress firms.

44

CHAPTER 4: RESEARCH FINDINGS

This chapter presents the result of the normality test, descriptive analysis of the

sample data used in this study, result from the t-test, Pearson correlations and

the result of the hypotheses testing.

4.1 Normality Test

Scatter plot 1 and 2 as shown in Figure 1 below display the distribution of the 3-

year average realised stock returns and Z-Score of the initial 98 distress listed-

companies identified as sample for the study.

Figure 1: Initial scatter plots

20.00010.0000.000-10.000

Z-Score

20.000

15.000

10.000

5.000

0.000

-5.000

-10.000

Ret

urns

Scatter plot 1: Distribution of returns and Z-Score (Test 1)

45

6.0004.0002.0000.000-2.000-4.000-6.000

Z-Score

6.000

4.000

2.000

0.000

-2.000

-4.000

-6.000

-8.000

Ret

urns

Scatter plot 2: Distribution of returns and Z-Score (Test 2)

Based on the above scatter plots, item D19, D24, D25, D31, D37, D50, D52, D53

and D57 have been identified as outliers and are subsequently removed from the

samples for the study to meet the normality condition for the data analysis.

Figure 2 below shows the distribution of the 3-year average realised stock returns

and Z-Score of the final 89 samples used in the study.

46

Figure 2: Final scatter plots and histograms

2.0001.0000.000-1.000-2.000-3.000-4.000

Z-Score

6.000

4.000

2.000

0.000

-2.000

-4.000

-6.000

-8.000

Ret

urns

Scatter plot 3: Distribution of returns and Z-Score (Final)

6.0004.0002.0000.000-2.000-4.000-6.000-8.000

Returns

14

12

10

8

6

4

2

0

Freq

uenc

y

Mean = -2.36678Std. Dev. = 2.586292N = 89

Histogram: Returns

47

2.0001.0000.000-1.000-2.000-3.000-4.000

Z-Score

20

15

10

5

0

Freq

uenc

y

Mean = -0.420Std. Dev. = 1.150342N = 89

Histogram: Z-Score

Statistical values for skewness and kurtosis are reported in Table 4 below after

removal of the outliers.

Table 4: Statistics for normal distribution test

Returns Z-Score

N 89 89

Mean -2.367 -0.420

Standard deviation 2.586 1.150

Skewness 0.135 -0.447

Kurtosis -0.144 0.030

The actual values of skewness and kurtosis should be zero if the distribution is

perfectly normal. The further the value is from zero, the more likely it is that the

data are not normally distributed. Based on the above, the values of skewness

and kurtosis for the two variables are acceptably small i.e. less than 1, which

support a fairly normal or symmetrical distribution of the data.

48

The result of the Shapiro-Wilk test for the samples after removal of the outliers is

reported in Table 5 below.

Table 5: Shapiro-Wilk test

Shapiro-Wilk

Statistics df p-value

Returns 0.977 89 0.124

Z-Score 0.992 89 0.872

The result of Shapiro-Wilk test revealed that the p-values of the two variables are

higher than 0.05, which therefore indicate that no significant departure from

normality was noted for the distribution of the two main variables data i.e. returns

and Z-Score.

Based on the histogram, the statistical values for skewness and kurtosis and the

p-value of the Shapiro-Wilk test, it is assumed that the distribution of the realised

stock returns and Z-Score is normal and fit for further analysis.

4.2 Descriptive Analysis

The descriptive statistics for the average 3-year scores of the variables are as

shown in Table 6 below.

Table 6: Descriptive statistics

Variables N Mean Median Standard

deviation

Lowest Highest

Returns 89 -2.368 -2.413 2.586 -7.863 4.863

Z-Score 89 -0.420 -0.346 1.150 -3.364 1.976

MV 89 4.743 4.689 0.400 3.862 6.061

BMV 89 0.767 0.517 1.273 -3.203 6.618

49

Based on the sample of 89 distress listed-companies, the 3-year arithmetic mean

value of stock returns for distress listed-companies is -2.34% which indicates that

on average the subsequent realised returns of most of the distress listed-

companies are negative. The negative median returns of -2.41% show that the

subsequent realised returns of the distress listed-companies are skewed toward

negative returns. This is inconsistent with the general notion that distress risk is

rewarded with positive returns. Nevertheless, as indicated by the maximum

returns score of 4.86%, it is noted that some of the distress-listed companies

offer positive returns.

The 3-year arithmetic mean value of the Z-Score for the sample is -0.42, which

fall within the bankruptcy zone of the predetermined cut-off. This indicates that

within 3 years from the companies are being classified as affected listed-issuers,

the distress listed-companies are already in distress zone. It is also interesting to

note that the highest Z-Score of the identified distressed listed-companies is 1.98,

whereby according to the predetermined cut-off of the Z-Score model, the

company should fall within the zone of ignorance.

The financial position of the distressed listed-companies is expected to

deteriorate as the company approaches the actual distress event. Therefore, all

the financial ratios are expected to weaken from Y-3 to Y-1. The trend on the

financial ratios and the Z-Score of the sample companies for the 3 years can be

seen from Table 7 below:

50

Table 7: Trend on average ratios and Z-Score for Y-1 to Y-3

PN4 PN17 APN17 Mean

Y-1 Y-2 Y-3 Y-1 Y-2 Y-3 Y-1 Y-2 Y-3

X1 -0.634 -0.446 -0.189 -0.458 -0.110 -0.058 -0.256 -0.106 -0.067

X2 -1.037 -0.608 -0.127 -1.109 -0.650 -0.485 -1.366 -0.999 -0.875

X3 -0.799 -0.603 -0.236 -0.785 -0.127 -0.155 -0.569 -0.157 -0.193

X4 0.313 0.507 0.602 0.371 0.616 0.604 0.483 0.685 0.904

X5 0.415 0.484 0.432 0.377 0.417 0.366 0.669 0.605 0.636

Z -1.742 -0.666 0.483 -1.604 0.147 0.272 -1.039 0.029 0.405

Consistent with the initial expectation, all the distress listed-companies selected

as samples showed deteriorating trend in their financial ratios over the 3 years

period. The trend on X1 shows that even at Y-3, the distress listed-companies

have already shown a negative working capital position i.e. the companies were

having difficulties in meeting its short-term obligations. The negative trend on X2

indicates that within 3 years prior to the occurrence of the distress event, most of

the distress listed-companies accumulated profits have already been wiped out.

The negative trend on the working capital and retained earnings of the distress

listed-companies over the 3 years period prior to the occurrence of the distress

event suggest that the financial position of the distress listed-companies were in

a dire state as the companies were not able to meet its short term obligations as

well as did not have sufficient internally generated funds to support its operations.

The negative trend in X3 over the 3 years period further indicates the distress

listed-companies were having operating problems as the companies were

recording losses at operating level before taking into account the financing

obligations.

51

The deteriorating trend in X4 reflects the decline in value of the distress listed-

companies’ assets over the 3-year period as well as increasing trend of the

companies leverage. Meanwhile, the trend in X5 shows the distress listed-

companies’ inefficiency in generating sales from the companies’ assets over the

3-year period.

In line with the above observations, the Z-Score of all the distress listed-

companies weaken over the 3-year period as it get closer to the year when the

distress listed-companies are classified as an affected listed issuer.

4.3 Predictive Ability of the Z-Score

To measure the predictive ability of the Z-Score, the Z-Scores of all the identified

distress listed-companies are segregated according to the predetermined cut-off

as suggested by Altman (1968). The percentage of the aggregate number of

companies that fall under the bankruptcy cut-off i.e. below 1.81 to the total

number of identified distress listed-company will represent the success rate of the

Z-Score in predicting financial distress.

Table 8 below set out the tabulation of the Z-Score of all 98 distress listed-

company for the 3 years prior to classification as an affected listed issuer.

52

Table 8: Tabulation of Z-Score by the predetermined cut-off score

No. of companies

Z-Score Y-1 Y-2 Y-3

Z > 2.99 2 4 3

1.81 < Z < 2.99 - 7 12

Z < 1.81 96 87 83

Total 98 98 98

Based on the tabulation of Z-Score by the predetermined cut-off, it shows that the

Z-Score has accurately predict financial distress up to 3 years prior to the year

that the distress listed-companies were classified as affected listed-issuers. This

is consistent with the study by Ng (2000) using Section 176 companies in

Malaysia as samples in which he find that the Z-Score was effective in detecting

signs of bankruptcy for a period of 3 years preceding the year of classification.

The result also shows that the success rate of the Z-Score in predicting financial

distress diminished as the number of years increased. The success rate

decreased from 98% in Y-1 to 89% in Y-2 and further decreased to 85% in Y-3.

However, this is consistent Altman’s (1968) findings during the construction of his

Z-Score model whereby he finds that bankruptcy can be accurately predicted

using the Z-Score of up to two years prior to actual failure with the accuracy

diminishing rapidly after the second year.

4.4 Result from the Statistical Tests

(a) T-test

53

Table 9 below reports the t-test results for the realised stock returns of the two

portfolios for each of the 3 years.

Table 9: T-test for the realised stock returns for Y-1, Y-2 and Y-3

Year

Portfolio Statistics Y-1 Y-2 Y-3

Distress Mean 1.868 -4.859 -2.844

Standard deviation 6.286 4.722 3.778

Lowest -8.510 -12.360 -11.480

Highest 12.400 6.350 7.410

n 13 39 58

Most distress Mean -1.101 -3.338 -1.652

Standard deviation 6.709 4.407 5.639

Lowest -16.130 -14.820 -12.450

Highest 18.530 4.820 15.700

n 76 50 31

p-value^ 0.141 0.121 0.296

^ p-value of t-test of differences in variable means between the distress and most distress firms

A comparison of the distress and most distress firms realised stock returns

means shows that the realised stock returns in Y-1 (after the distress listed-

companies have been classified as affected listed issuers) were positive for the

distress firms whilst the most distress firms were negative. The realised stock

returns means for the distress and most distress firms were negative in Y-2 and

Y-3. It is interesting to note that the most distress portfolio has higher positive

realised stock returns in Y-1 and Y-3, which could suggest that stock with higher

distress risk offer higher returns. However, the p-values for the test of means

differences between the distress and most distress firms realised stock returns

were not statistically significant in any one of the year. Therefore the higher and

lower positive and negative returns in the most distress portfolio in Y-1 and Y-3

could also indicate outliers in the sample. As indicated by the t-test, hypotheses

54

H2 is rejected as the difference in the realised stock returns means of the distress

and most distress firms is not statistically significant.

Table 10 below reports the t-test results for the firms size the two portfolios for

each of the 3 years.

Table 10: T-test for the MV for Y-1, Y-2 and Y-3

Year

Portfolio Statistics Y-1 Y-2 Y-3

Distress Mean 4.536 4.865 4.938

Standard deviation 0.290 0.521 0.436

Lowest 4.153 3.505 3.997

Highest 5.139 6.461 6.060

n 13 39 58

Most distress Mean 4.554 4.749 4.767

Standard deviation 0.433 0.400 0.364

Lowest 3.538 3.786 3.822

Highest 5.661 5.754 5.818

n 76 50 31

p-value^ 0.883 0.238 0.067

^ p-value of t-test of differences in variable means between the distress and most distress firms

A comparison of the distress and most distress firms’ size shows that the MV of

the distress and most distress firms are quite similar in Y-1, Y-2 and Y-3.

However, it is noted that the smallest MV in the most distress portfolio is lower as

compared to the distress portfolio in Y-1 and Y-3. However, the p-values for the

test of means differences between the distress and most distress firms’ size were

not statistically significant in any one of the year. The result implies that the lower

the MV of the firms does not indicate a higher distress risk. As indicated by the t-

test, hypotheses H7 is rejected as the difference in the mean value of the distress

and most distress firms’ size is not statistically significant.

55

Table 11 below reports the t-test results for the book-to-market equity ratio of the

two portfolios for each of the 3 years.

Table 11: T-test for the BMV for Y-1, Y-2 and Y-3

Year

Portfolio Statistics Y-1 Y-2 Y-3

Distress Mean 1.564 1.137 1.128

Standard deviation 1.504 0.938 1.083

Lowest 0.071 -0.545 0.047

Highest 5.099 3.178 4.983

n 13 39 58

Most distress Mean 0.157 0.780 0.766

Standard deviation 2.158 1.282 1.944

Lowest -4.144 -1.417 -8.107

Highest 9.462 5.831 3.926

n 76 50 31

p-value^ 0.027 0.148 0.261

^ p-value of t-test of differences in variable means between the distress and most distress firms

A comparison of the distress and most distress firms’ book-to-market equity

means reveals that the book-to-market equity of the most distress portfolio is

consistently lower in Y-1, Y-2 and Y-3. It is also noted that the book-to-market

equity mean value of the most distress portfolio is shrinking as firms approach the

distress event i.e. the firms formally classified as affected listed issuers. In fact,

the difference in the book-to-market equity mean value for the two portfolios in Y-

1 is found to be statistically significant as indicated by the p-value of 0.027.

However, difference in the book-to-market equity mean value for the two

portfolios in Y-2 and Y-3 is not statistically significant. The result may imply that

the shareholders’ equity of the firms with the higher probability of financial

distress is eroding as the firm approaches the distress event. In contrast to the

earlier empirical findings which suggest that higher book-to-market equity is

56

associated with higher distress risk, the result from the t-test reveals that the

most distress firms in actual fact have lower book-to-market equity than the

distress firms. Therefore, hypotheses H8 cannot be rejected as the difference in

the book-to-market equity mean value of the distress and most distress firms in

Y-1 is found to be statistically significant.

(b) Pearson correlations

Table 12 presents the results from using Pearson correlations in examining the

relationship between the variables.

Table 12: Pearson correlation

Pearson Correlation

Returns Z-Score MV BMV

Returns 1.000

Z-Score -0.059 1.000

MV 0.012 0.016 1.000

BMV 0.087 0.272 -0.101 1.000

Pearson Probabilities

Returns -

Z-Score .582 -

MV .914 .884 -

BMV .416 .010* .347 -

* Correlation is significant at 0.01 level (2-tailed)

The negative Pearson correlation of the stock returns and Z-Score of -0.059 is

consistent with the distress hypotheses. This is in view that under the Z-Score

bankruptcy prediction model, lower Z-Score is interpreted as higher probability of

corporate failure to occur. Therefore the inverse relationship between stock

returns and Z-Score as indicated by the Pearson correlation of -0.059 agrees with

57

the distress hypotheses which suggest that the higher the distress risk, the higher

the expected stock returns. However, the positive relationship between stock

returns and distress risk is found to be statistically insignificant at 1% significance

level as shown by the Pearson probabilities.

If size and book-to-market equity can be a proxy of distress risk and a predictor of

stock returns, the stock returns should be negatively related to size (i.e. the lower

the size of the firm, the higher the stock returns) and positively related to book-to-

market equity (i.e. the higher the book-to-market equity, the higher the stock

returns). The positive Pearson correlation of the stock returns and size of 0.012 is

inconsistent with the risk and return hypotheses and statistically insignificant at

1% significance level as shown by the Pearson probabilities. The positive

Pearson correlation of the stock returns and book-to-market equity of 0.087 is

consistent with the risk and return hypotheses. However, the relationship is found

to be statistically insignificant at 1% significance level as shown by the Pearson

probabilities.

The positive correlation between Z-Score and MV of 0.016 indicates that the

lower the Z-Score i.e. higher probability of distress risk, the lower the market

value of the firm. The result is consistent with the previous studies on the size

effect which suggested that firm under distress displays low market value relative

to a typical stock. However, the positive relationship between the distress risk

and MV is found to be statistically insignificant at 1% significance level as shown

by the Pearson probabilities.

58

The positive correlation between Z-Score and BMV of 0.272 appears to be

inconsistent with the findings of Chan and Chen (1991), Fama and French (1992,

1993, 1995) and Chan and Zheng (1998) on the book-to-market effect. The

positive correlation between Z-Score and BMV implies that firms with higher

probability of distress risk display lower book-to-market value of equity ratio.

However, this is similar to the findings of Griffin and Lemmon (2002) whereby

they found that the group of firms with the highest risk of distress include more

firms with low book-to-market equity ratio. The positive relationship between the

distress risk and BMV is found to be statistically significant at 1% significance

level as shown by the Pearson probabilities.

One plausible explanation on this anomalous finding is that considerable number

of affected listed issuers recorded negative shareholders’ equity as the financial

distress event approaches. This is based on the fact that 67% and 33% of the

identified PN4 and PN17 companies respectively show negative shareholders’

equity in Y-1, which result in lower book-to-market ratio of the identified affected

listed-issuers. This indicates that the distress listed-company especially those

under the PN4 category were already in dire state by the time the company was

identified as affected listed issuers in view of the fact that all the company’s

shareholders’ equity have been totally wiped-out due to significant losses. In

cognisant of this, Bursa Securities had, on 5 May 2006, introduced the

amendments in APN17 which are aimed at expediting the restructuring efforts of

the affected listed issuers. Under the APN17, the distress listed-company need

not be in a deficit shareholders’ equity position before it is being classified as an

affected listed-issuer. If the listed company shareholders’ equity is equal to or

59

less than 25% of its issued and paid up capital and such shareholders’ equity is

less than the minimum issued and paid up capital as required under paragraph

8.16A(1) of the Listing Requirements, the listed company is classified as an

affected listed-issuer under the APN17.

Based on the above correlation test, the empirical findings on the research

hypotheses are summarised in the following table:

Table 13: Summary of empirical findings

Pearson Correlations

Hypotheses Correct Sign Statistical

Significance

Result

H1 Yes No Inconclusive

H3 No No Inconclusive

H4 Yes No Inconclusive

H5 Yes No Inconclusive

H6 No Yes Inconclusive

The inconclusive result could not accept hypotheses H6 and this is inconsistent

with the book-to-market equity effect. However, it is noted that the inverse

relationship between distress risk and book-to-market equity of distress listed-

companies in Malaysia is found to be statistically significant, which is similar to

the previous findings of Griffin and Lemmon (2002) and Zaretzky and Zumwalt

(2007).

60

CHAPTER 5: CONCLUSION AND RECOMMENDATIONS

5.1 Summary and Conclusion

This study sets out to examine risk-return relationship of distress-listed

companies in Malaysia. Using Altman’s Z-Score bankruptcy prediction model as

the proxy of distress risk and the subsequent realised stock returns of the

distress-listed companies, this study tests the empirical relationship between the

two variables. Due to its well-documented association with returns and distress

risk, this study also presents empirical findings on the size and book-to-market

equity effect on the distress listed-companies in Malaysia.

The findings of this study suggest that the relationship between financial distress

risk and stock returns in the case of the Malaysian distress listed-companies

appear to be consistent with the outcome of past research such as Lang and

Stulz (1992), Denis and Denis (1995), Shumway (1996) and Chen and Zhang

(1998). However, the finding is not conclusive as the positive correlation found

between the stock returns and distress risk is not statistically significant. In fact,

the distress listed-companies appear to be under-performed as indicated by the

negative mean and median value of the returns for the 3 years. It is also found in

this study that the theoretical expectation and past empirical findings of size and

book-to-market equity effect on stock returns does not hold in the case of the

Malaysian distress listed-firms.

61

Similarly, it is found that the theoretical expectation of the size and book-to-

market equity effect on distress risk also does not hold in the case of the

Malaysian distress listed-firms. However, similar to the findings of Griffin and

Lemmon (2002), a significant inverse relationship between distress risk and

book-to-market equity is found in this study which show that Malaysian distress

listed-companies with higher probability of distress risk display lower book-to-

market value of equity ratio. This is further supported with the finding from the t-

test on the difference in the book-to-market equity mean value of distress and

most distress firms which is found to be statistically significant in Y-1.

Nonetheless, this study further validates the accuracy of the Altman’s Z-Score is

detecting signs of bankruptcy for a period of 3 years preceding the year of

classification, which is consistent with the previous findings on the applicability of

the Z-Score in predicting the probability of bankruptcy or financial distress.

Based on the above findings, it is inconclusive to deduce that distress risk is a

systematic risk in relation to the Malaysian stock market.

5.2 Suggestion for Future Study

This study is subject to several limitations as mentioned earlier in this report. It is

suggested that control sample to be included in a future similar study to enhance

the chances of improving statistical results and ability to infer significant

implications. As the sample of this study include those public firms which have

been classified as distress listed-companies under the PN4, PN17 and APN17

62

only, the sample can be enlarged to include companies which are under the

protection of the Section 176 of the Companies Act 1965.

This study only uses the subsequent average realised returns of the distress

listed-companies for the two years prior to the classification as an affected listed

issuer and one year post classification as an affected listed issuer. Future studies

may consider using the market adjusted returns in order to establish whether the

distress listed-stocks offer higher average returns than the market returns.

In addition, this study uses Altman’s (1968) Z-Score as a proxy for distress risk,

which can be confusing given the reverse nature of the score i.e. the lower the Z-

Score, the higher the probability of bankruptcy risk and vice versa. Different

bankruptcy model may be adopted such as the O-Score by Ohlson (1980) to

proxy the distress risk which may give different dimensions to the result of the

study.

63

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69

Appendix 1

List of identified distress listed-companies up to 31 December 2007 based on announcements made on Bursa Securities

No. Company name Classification Date classified

1. ABRAR CORPORATION BERHAD PN4 23/01/2001

2. ACTACORP HOLDINGS BERHAD PN4 26/02/2001

3. AKTIF LIFESTYLE CORPORATION BHD PN4 08/05/2002

4. AMSTEEL CORPORATION BERHAD PN4 25/05/2001

5. ANSON PERDANA BERHAD PN4 29/07/2003

6. AOKAM PERDANA BHD PN4 26/02/2001

7. ARTWRIGHT HOLDINGS BERHAD PN4 22/02/2001

8. ARUS MURNI CORPORATION BERHAD PN4 26/02/2001

9. ASIAN PAC HOLDINGS BERHAD PN4 26/02/2001

10. ASSOCIATED KAOLIN INDUSTRIES BERHAD PN4 26/02/2001

11. AUSTRAL AMALGAMATED BERHAD PN4 26/02/2001

12. AUTOINDUSTRIES VENTURES BERHAD PN4 28/02/2002

13. AUTOWAYS HOLDINGS BERHAD PN4 26/02/2001

14. AYER HITAM TIN DREDGING MALAYSIA BERHAD PN4 28/10/2004

15. BERJUNTAI TIN DREDGING BERHAD PN4 26/02/2001

16. BESCORP INDUSTRIES BERHAD PN4 20/02/2001

17. BRIDGECON HOLDINGS BERHAD PN4 23/02/2001

18. BUKIT KATIL RESOURCES BERHAD PN4 14/10/2004

19. CHASE PERDANA BERHAD PN4 26/02/2001

20. CHG INDUSTRIES BERHAD PN4 06/09/2001

21. CONSOLIDATED FARMS BERHAD PN4 19/05/2004

22. CONSTRUCTION AND SUPPLIES HOUSE BERHAD PN4 26/02/2001

23. CSM CORPORATION BERHAD PN4 26/02/2001

24. CYGAL BERHAD PN4 23/02/2001

25. DATAPREP HOLDINGS BHD PN4 26/02/2001

26. DENKO INDUSTRIAL CORPORATION BERHAD PN4 23/02/2001

27. DEWINA BERHAD PN4 26/02/2001

28. EDEN ENTERPRISES (M) BERHAD PN4 21/09/2001

29. EMICO HOLDINGS BERHAD PN4 23/02/2001

30. EPE POWER CORPORATION BERHAD PN4 28/02/2002

31. ESPRIT GROUP BERHAD PN4 26/02/2001

32. FABER GROUP BERHAD PN4 18/11/2003

33. FORESWOOD GROUP BERHAD PN4 22/08/2002

34. FW INDUSTRIES BERHAD PN4 04/02/2002

35. GEAHIN ENGINEERING BERHAD PN4 26/02/2001

36. GENERAL LUMBER FABRICATORS & BUILDERS BHD PN4 04/03/2002

37. GENERAL SOIL ENGINEERING HOLDINGS BERHAD PN4 30/08/2002

38. GLOBAL CARRIERS BERHAD PN4 26/02/2001

39. HAI MING HOLDINGS BHD PN4 26/02/2001

40. HIAP AIK CONSTRUCTION BERHAD PN4 09/04/2002

70

Appendix 1

No. Company name Classification Date classified

41. HOTLINE FURNITURE BERHAD PN4 23/02/2001

42. IDRIS HYDRAULIC (MALAYSIA) BERHAD PN4 26/02/2001

43. INNOVEST BERHAD PN4 23/02/2001

44. INSTANGREEN CORPORATION BHD PN4 23/02/2001

45. JASATERA BERHAD PN4 26/02/2001

46. JIN LIN WOOD INDUSTRIES BERHAD PN4 27/08/2004

47. JUTAJAYA HOLDING BERHAD PN4 07/12/2001

48. K.P. KENINGAU BERHAD PN4 22/09/2004

49. KELANAMAS INDUSTRIES BERHAD PN4 20/02/2001

50. KEMAYAN CORPORATION BHD PN4 23/02/2001

51. KIARA EMAS ASIA INDUSTRIES BERHAD PN4 23/02/2001

52. KILANG PAPAN SERIBU DAYA BERHAD PN4 26/02/2001

53. KRETAM HOLDINGS BERHAD PN4 04/03/2002

54. KSU HOLDINGS BERHAD PN4 20/08/2003

55. KUALA LUMPUR INDUSTRIES HOLDINGS BERHAD PN4 26/02/2001

56. KUMPULAN FIMA BERHAD PN4 08/08/2002

57. L&M CORPORATION (M) BHD PN4 23/02/2001

58. LION CORPORATION BERHAD PN4 26/02/2001

59. LONG HUAT GROUP BERHAD PN4 01/08/2001

60. MALAYSIAN GENERAL INVESTMENT CORPORATION BERHAD PN4 20/02/2001

61. MAN YAU HOLDINGS BHD PN4 26/02/2001

62. MANCON BERHAD PN4 26/02/2001

63. MAY PLASTICS INDUSTRIES BHD PN4 26/02/2001

64. MBF CAPITAL BERHAD PN4 23/02/2001

65. MBF HOLDINGS BERHAD PN4 20/02/2001

66. MEASUREX CORPORATION BERHAD PN4 26/02/2001

67. MENTIGA CORPORATION BERHAD PN4 26/02/2001

68. MGR CORPORATION BERHAD PN4 23/02/2001

69. MOL.COM BERHAD PN4 07/09/2001

70. MYCOM BERHAD PN4 26/02/2001

71. NAUTICALINK BERHAD PN4 23/02/2001

72. NCK CORPORATION BHD PN4 26/02/2001

73. OCEAN CAPITAL BERHAD PN4 28/04/2003

74. OLYMPIA INDUSTRIES BERHAD PN4 26/02/2001

75. OMEGA HOLDINGS BERHAD PN4 26/02/2001

76. PAN MALAYSIA HOLDINGS BERHAD PN4 26/02/2001

77. PAN PACIFIC ASIA BERHAD PN4 30/10/2001

78. PANCARAN IKRAB BHD PN4 23/02/2001

79. PANGLOBAL BERHAD PN4 26/02/2001

80. PARIT PERAK HOLDINGS BERHAD PN4 23/02/2001

81. PARK MAY BERHAD PN4 21/02/2001

82. PENAS CORPORATION BERHAD PN4 23/02/2001

83. PERDANA INDUSTRI HOLDINGS BERHAD PN4 26/02/2001

71

Appendix 1

No. Company name Classification Date classified

84. PICA (M) CORPORATION BERHAD PN4 28/02/2002

85. PLANTATION & DEVELOPMENT (MALAYSIA) BERHAD PN4 26/02/2001

86. PROMET BERHAD PN4 23/02/2001

87. RAHMAN HYDRAULIC TIN BERHAD PN4 22/02/2001

88. REKAPACIFIC BERHAD PN4 26/02/2001

89. REPCO HOLDINGS BHD PN4 23/02/2001

90. S&P FOOD INDUSTRIES (M) BHD PN4 22/02/2001

91. SASHIP HOLDINGS BERHAD PN4 26/02/2001

92. SATERAS RESOURCES (MALAYSIA) BERHAD PN4 23/02/2001

93. SCK GROUP BERHAD PN4 26/02/2001

94. SELOGA HOLDINGS BERHAD PN4 01/03/2001

95. SENG HUP CORPORATION BERHAD PN4 23/02/2001

96. SILVERSTONE CORPORATION BERHAD PN4 20/05/2002

97. SIN HENG CHAN (MALAYA) BERHAD PN4 26/02/2001

98. SISTEM TELEVISYEN MALAYSIA BERHAD PN4 23/02/2001

99. SOUTHERN PLASTIC HOLDINGS BERHAD PN4 22/02/2001

100. SPORTMA CORPORATION BERHAD PN4 21/02/2001

101. SRI HARTAMAS BERHAD PN4 22/02/2001

102. SRIWANI HOLDINGS BERHAD PN4 23/02/2001

103. SUNWAY BUILDING TECHNOLOGY BERHAD PN4 13/05/2002

104. TAI WAH GARMENTS MANUFACTURING BERHAD PN4 23/02/2001

105. TAIPING CONSOLIDATED BERHAD PN4 23/02/2001

106. TAJO BHD PN4 23/02/2001

107. TANAH EMAS CORPORATION BERHAD PN4 26/02/2001

108. TAP RESOURCES BERHAD PN4 27/06/2002

109. TAT SANG HOLDINGS BERHAD PN4 17/09/2002

110. TECHNO ASIA HOLDINGS BERHAD PN4 23/02/2001

111. THE NORTH BORNEO CORPORATION BHD PN4 27/04/2001

112. TIMBERMASTER INDUSTRIES BHD PN4 23/02/2001

113. TONGKAH HOLDINGS BERHAD PN4 06/09/2001

114. TRANS CAPITAL HOLDING BERHAD PN4 23/02/2001

115. TRANSWATER CORPORATION BHD PN4 23/02/2001

116. TRU-TECH HOLDINGS BHD PN4 27/02/2004

117. UCP RESOURCES BERHAD PN4 21/02/2001

118. UH DOVE HOLDINGS BHD PN4 26/02/2001

119. UNIPHOENIX CORPORATION BERHAD PN4 23/02/2001

120. UNITED CHEMICAL INDUSTRIES BERHAD PN4 21/06/2001

121. WEMBLEY INDUSTRIES HOLDINGS BERHAD PN4 23/02/2001

122. WING TIEK HOLDINGS BERHAD PN4 26/02/2001

123. WOO HING BROTHERS (MALAYA) BERHAD PN4 23/02/2001

124. YCS CORPORATION BERHAD PN4 04/07/2003

125. ZAITUN BERHAD PN4 26/02/2001

126. ANTAH HOLDINGS BERHAD PN17 09/01/2006

72

Appendix 1

No. Company name Classification Date classified

127. ARK RESOURCES BERHAD PN17 24/04/2006

128. AVANGARDE RESOURCES BERHAD PN17 14/03/2005

129. BOUSTEAD HEAVY INDUSTRIES CORPORATION BHD PN17 01/12/2005

130. COMSA FARMS BERHAD PN17 07/04/2006

131. FEDERAL FURNITURE HOLDINGS (M) BERHAD PN17 08/12/2005

132. KIG GLASS INDUSTRIAL BERHAD PN17 08/11/2005

133. LITYAN HOLDINGS BERHAD PN17 10/05/2005

134. METROPLEX BERHAD PN17 10/04/2006

135. MULPHA LAND BERHAD PN17 15/03/2005

136. PAN MALAYSIA CAPITAL BERHAD PN17 02/03/2006

137. PAN MALAYSIA HOLDINGS BERHAD PN17 02/03/2006

138. PAN MALAYSIA INDUSTRIES BERHAD PN17 08/03/2006

139. PERDUREN (M) BERHAD PN17 09/03/2006

140. PETALING TIN BERHAD PN17 13/01/2005

141. POHMAY HOLDINGS BERHAD PN17 29/04/2005

142. POLY GLASS FIBRE (M) BERHAD PN17 11/07/2005

143. POLYMATE HOLDINGS BERHAD PN17 01/12/2005

144. SCOMI ENGINEERING BHD PN17 28/02/2005

145. SETEGAP BERHAD PN17 04/03/2005

146. SINORA INDUSTRIES BERHAD PN17 07/07/2005

147. SUREMAX GROUP BERHAD PN17 09/05/2006

148. TANCO HOLDINGS BERHAD PN17 24/05/2005

149. AMSTEEL CORPORATION BHD APN17 08/05/2006

150. APL INDUSTRIES BERHAD APN17 31/10/2007

151. CHIN FOH BHD APN17 08/05/2006

152. CNLT ( FAR EAST ) BERHAD APN17 11/06/2007

153. CRIMSON LAND BHD APN17 08/05/2006

154. DATAPREP HOLDINGS BERHAD APN17 08/05/2006

155. DATUK KERAMAT HOLDINGS BERHAD APN17 04/08/2006

156. DCEIL INTERNATIONAL BERHAD APN17 30/08/2006

157. EKRAN BHD APN17 08/05/2006

158. ELBA HOLDINGS BHD APN17 08/05/2006

159. FA PENINSULAR BHD APN17 08/05/2006

160. FCW HOLDINGS BERHAD APN17 08/05/2006

161. FOREMOST HOLDINGS BHD APN17 11/05/2006

162. GEORGE TOWN HOLDINGS BERHAD APN17 09/05/2006

163. HALIFAX CAPITAL BERHAD APN17 08/05/2006

164. HARVEST COURT INDUSTRIES BHD APN17 08/05/2006

165. JOHAN CERAMICS BERHAD APN17 12/06/2006

166. KAI PENG BHD APN17 09/05/2006

167. KL INFRASTRUCTURE GROUP BERHAD APN17 28/10/2005

168. KUMPULAN BELTON BERHAD APN17 08/05/2006

169. MANGIUM INDUSTRIES BHD APN17 22/05/2007

170. MBF CORPORATION BHD APN17 09/05/2006

73

Appendix 1

No. Company name Classification Date classified

171. MCSB SYSTEMS (M) BHD APN17 08/05/2006

172. MEGAN MEDIA HOLDINGS BERHAD APN17 19/06/2007

173. MERGE ENERGY BERHAD APN17 08/05/2006

174. MOL.COM BERHAD APN17 08/05/2006

175. MP TECHNOLOGY RESOURCES BERHAD APN17 26/01/2007

176. OCI BERHAD APN17 06/11/2006

177. PARACORP BHD APN17 08/05/2006

178. PAXELENT CORPORATION BHD APN17 08/05/2006

179. PUTERA CAPITAL BHD APN17 08/05/2006

180. SBBS CONSORTIUM BERHAD APN17 09/05/2006

181. SELOGA HOLDINGS BERHAD APN17 11/05/2006

182. SILVERSTONE CORPORATION BHD APN17 08/05/2006

183. SUNWAY INFRASTRUCTURE BERHAD APN17 28/11/2006

184. SUREMAX GROUP BERHAD APN17 09/05/2006

185. SYARIKAT KAYU WANGI BHD APN17 08/05/2006

186. TALAM CORPORATION BERHAD APN17 01/09/2006

187. TAP RESOURCES BERHAD APN17 08/05/2006

188. TECHVENTURE BERHAD APN17 08/05/2006

189. TENCO BHD APN17 09/05/2006

190. TENGGARA OIL BHD APN17 08/05/2006

191. TRIPLC BHD APN17 08/05/2006

192. TT RESOURCES BHD APN17 08/05/2006

193. UBG BERHAD APN17 22/05/2007

194. WONDERFUL WIRE & CABLE BHD APN17 30/11/2007

74

Appendix 2 Key enhancement of the criteria of affected listed issuers in APN17

Existing PN17 criteria Enhanced PN17 criteria (APN17 criteria) (a) Deficit in the adjusted shareholders’ equity

of the listed issuer on a consolidated basis (a) Shareholders’ equity of the listed issuer on

a consolidated basis is equal to or less than 25% of the issued and paid up capital of the listed issuer and such shareholders’ equity is less than the minimum issued and paid up capital as required under paragraph 8.16A(1) of the Listing Requirements

(b) appointment of receivers and/or managers over the property of the listed issuer or its major subsidiary or major associated company which property accounts for at least 70% of the total assets employed of the listed issuer on a consolidated basis

(b) appointment of receivers and/or managers over the asset of the listed issuer, its subsidiary or associated company which asset accounts for at least 50% of the total assets employed of the listed issuer on a consolidated basis

None (c) a winding up order of a listed issuer’s subsidiary or associated company which accounts for at least 50% of the total assets employed of the listed issuer on a consolidated basis

(c) auditors have expressed adverse or disclaimer opinion in respect of the listed issuer’s going concern, in its latest audited accounts

(d) auditors have expressed an adverse or disclaimer opinion in the listed issuer’s latest audited accounts

None (e) the auditors have expressed a modified opinion with emphasis on the listed issuer’s going concern in the listed issuer’s latest audited accounts and the shareholders’ equity of the listed issuer on a consolidated basis is equal to or less than 50% of the issued and paid up capital of the listed issuer

None (f) a default in payment by a listed issuer, its major subsidiary or major associated company, as the case may be and the listed issuer is unable to provide a solvency declaration

(d) listed issuer has suspended or ceased all of its business or its major business or its entire or major operations for any reasons whatsoever

(g) No change

(e) listed issuer has an insignificant business or operations

(h) No change

75

Appendix 3

List of distress listed-companies selected as samples

No. Code Company Name Classification Date classified

1. D1 ANSON PERDANA BERHAD PN4 29/07/2003

2. D2 AUTOINDUSTRIES VENTURES BERHAD PN4 28/02/2002

3. D3 AYER HITAM TIN DREDGING MALAYSIA BERHAD PN4 28/10/2004

4. D4 CHG INDUSTRIES BERHAD PN4 06/09/2001

5. D5 CONSOLIDATED FARMS BERHAD PN4 19/05/2004

6. D6 CSM CORPORATION BERHAD PN4 26/02/2001

7. D7 CYGAL BERHAD PN4 23/02/2001

8. D8 DEWINA BERHAD PN4 26/02/2001

9. D9 EDEN ENTERPRISES (M) BERHAD PN4 21/09/2001

10. D10 EMICO HOLDINGS BERHAD PN4 23/02/2001

11. D11 EPE POWER CORPORATION BERHAD PN4 28/02/2002

12. D12 FORESWOOD GROUP BERHAD PN4 22/08/2002

13. D13 FW INDUSTRIES BERHAD PN4 04/02/2002

14. D14 GEAHIN ENGINEERING BERHAD PN4 26/02/2001

15. D15 GENERAL LUMBER FABRICATORS & BUILDERS BHD PN4 04/03/2002

16. D16 INNOVEST BERHAD PN4 23/02/2001

17. D17 JIN LIN WOOD INDUSTRIES BERHAD PN4 27/08/2004

18. D18 JUTAJAYA HOLDING BERHAD PN4 07/12/2001

19. D19 K.P. KENINGAU BERHAD PN4 22/09/2004

20. D20 KRETAM HOLDINGS BERHAD PN4 04/03/2002

21. D21 KUMPULAN FIMA BERHAD PN4 08/08/2002

22. D22 MEASUREX CORPORATION BERHAD PN4 26/02/2001

23. D23 MENTIGA CORPORATION BERHAD PN4 26/02/2001

24. D24 MGR CORPORATION BERHAD PN4 23/02/2001

25. D25 MOL.COM BERHAD PN4 07/09/2001

26. D26 NCK CORPORATION BHD PN4 26/02/2001

27. D27 OCEAN CAPITAL BERHAD PN4 28/04/2003

28. D28 PAN MALAYSIA HOLDINGS BERHAD PN4 26/02/2001

29. D29 PAN PACIFIC ASIA BERHAD PN4 30/10/2001

30. D30 PARIT PERAK HOLDINGS BERHAD PN4 23/02/2001

31. D31 PICA (M) CORPORATION BERHAD PN4 28/02/2002

32. D32 SILVERSTONE CORPORATION BERHAD PN4 20/05/2002

33. D33 SISTEM TELEVISYEN MALAYSIA BERHAD PN4 23/02/2001

34. D34 SRI HARTAMAS BERHAD PN4 22/02/2001

35. D35 SUNWAY BUILDING TECHNOLOGY BERHAD PN4 13/05/2002

36. D36 TAP RESOURCES BERHAD PN4 27/06/2002

37. D37 THE NORTH BORNEO CORPORATION BHD PN4 27/04/2001

38. D38 TONGKAH HOLDINGS BERHAD PN4 06/09/2001

39. D39 TRANS CAPITAL HOLDING BERHAD PN4 23/02/2001

40. D40 TRU-TECH HOLDINGS BHD PN4 27/02/2004

41. D41 UNITED CHEMICAL INDUSTRIES BERHAD PN4 21/06/2001

76

Appendix 3

No. Code Company Name Classification Date classified

42. D42 WEMBLEY INDUSTRIES HOLDINGS BERHAD PN4 23/02/2001

43. D43 ZAITUN BERHAD PN4 26/02/2001

44. D44 BOUSTEAD HEAVY INDUSTRIES CORPORATION BHD PN17 01/12/2005

45. D45 COMSA FARMS BERHAD PN17 07/04/2006

46. D46 FEDERAL FURNITURE HOLDINGS (M) BERHAD PN17 08/12/2005

47. D47 KIG GLASS INDUSTRIAL BERHAD PN17 08/11/2005

48. D48 LITYAN HOLDINGS BERHAD PN17 10/05/2005

49. D49 METROPLEX BERHAD PN17 10/04/2006

50. D50 MULPHA LAND BERHAD PN17 15/03/2005

51. D51 PAN MALAYSIA CAPITAL BERHAD PN17 02/03/2006

52. D52 PAN MALAYSIA INDUSTRIES BERHAD PN17 08/03/2006

53. D53 PERDUREN (M) BERHAD PN17 09/03/2006

54. D54 POHMAY HOLDINGS BERHAD PN17 29/04/2005

55. D55 POLY GLASS FIBRE (M) BERHAD PN17 11/07/2005

56. D56 POLYMATE HOLDINGS BERHAD PN17 01/12/2005

57. D57 SCOMI ENGINEERING BHD PN17 28/02/2005

58. D58 SETEGAP BERHAD PN17 04/03/2005

59. D59 SINORA INDUSTRIES BERHAD PN17 07/07/2005

60. D60 SUREMAX GROUP BERHAD PN17 09/05/2006

61. D61 TANCO HOLDINGS BERHAD PN17 24/05/2005

62. D62 AMSTEEL CORPORATION BHD APN17 08/05/2006

63. D63 CHIN FOH BHD APN17 08/05/2006

64. D64 CNLT ( FAR EAST ) BERHAD APN17 11/06/2007

65. D65 CRIMSON LAND BHD APN17 08/05/2006

66. D66 DATAPREP HOLDINGS BERHAD APN17 08/05/2006

67. D67 DATUK KERAMAT HOLDINGS BERHAD APN17 04/08/2006

68. D68 EKRAN BHD APN17 08/05/2006

69. D69 ELBA HOLDINGS BHD APN17 08/05/2006

70. D70 FA PENINSULAR BHD APN17 08/05/2006

71. D71 FOREMOST HOLDINGS BHD APN17 11/05/2006

72. D72 GEORGE TOWN HOLDINGS BERHAD APN17 09/05/2006

73. D73 HALIFAX CAPITAL BERHAD APN17 08/05/2006

74. D74 HARVEST COURT INDUSTRIES BHD APN17 08/05/2006

75. D75 JOHAN CERAMICS BERHAD APN17 12/06/2006

76. D76 KAI PENG BHD APN17 09/05/2006

77. D77 KL INFRASTRUCTURE GROUP BERHAD APN17 28/10/2005

78. D78 KUMPULAN BELTON BERHAD APN17 08/05/2006

79. D79 MANGIUM INDUSTRIES BHD APN17 22/05/2007

80. D80 MCSB SYSTEMS (M) BHD APN17 08/05/2006

81. D81 MEGAN MEDIA HOLDINGS BERHAD APN17 19/06/2007

82. D82 MERGE ENERGY BERHAD APN17 08/05/2006

83. D83 MP TECHNOLOGY RESOURCES BERHAD APN17 26/01/2007

84. D84 OCI BERHAD APN17 06/11/2006

77

Appendix 3

No. Code Company Name Classification Date classified

85. D85 PARACORP BHD APN17 08/05/2006

86. D86 PAXELENT CORPORATION BHD APN17 08/05/2006

87. D87 PUTERA CAPITAL BHD APN17 08/05/2006

88. D88 SBBS CONSORTIUM BERHAD APN17 09/05/2006

89. D89 SELOGA HOLDINGS BERHAD APN17 11/05/2006

90. D90 SUNWAY INFRASTRUCTURE BERHAD APN17 28/11/2006

91. D91 SYARIKAT KAYU WANGI BHD APN17 08/05/2006

92. D92 TALAM CORPORATION BERHAD APN17 01/09/2006

93. D93 TECHVENTURE BERHAD APN17 08/05/2006

94. D94 TENCO BHD APN17 09/05/2006

95. D95 TENGGARA OIL BHD APN17 08/05/2006

96. D96 TRIPLC BHD APN17 08/05/2006

97. D97 TT RESOURCES BHD APN17 08/05/2006

98. D98 WONDERFUL WIRE & CABLE BHD APN17 30/11/2007

Appendix 4

78

Ratios of the sample companies for Y-1, Y-2, Y-3 and 3-year average

Y-1 Y-2 Y-3 Average 3 years X1 X2 X3 X4 X5 X1 X2 X3 X4 X5 X1 X2 X3 X4 X5 X1 X2 X3 X4 X5

No. Code times times times times times times times times times times times times times times times times times times times times PN4 Companies

1. D1 -0.919 -0.692 0.037 0.109 0.043 -0.871 -0.656 -0.351 0.154 0.032 -0.625 -0.483 -0.371 0.100 0.100 -0.805 -0.610 -0.228 0.121 0.058 2. D2 -0.530 -0.737 -0.290 0.415 0.827 -0.203 -0.435 -0.753 0.447 0.943 -0.090 -0.020 -0.013 0.881 0.825 -0.274 -0.398 -0.352 0.181 0.865 3. D3 -0.562 -1.186 -0.319 0.616 0.170 -0.296 -0.876 -0.240 0.655 0.079 0.131 -0.641 1.004 0.702 0.120 -0.243 -0.901 0.148 0.359 0.123 4. D4 -0.335 -0.603 0.254 0.094 0.925 -0.375 -0.627 -0.036 0.159 0.712 -0.317 -0.439 -0.815 0.183 0.567 -0.342 -0.556 -0.199 0.080 0.735 5. D5 -1.018 -0.312 -0.731 0.080 0.386 -0.314 0.024 -0.014 0.091 0.304 -0.252 0.084 -0.025 0.175 0.300 -0.528 -0.068 -0.257 0.080 0.330 6. D6 -1.220 -1.855 -6.403 0.420 0.192 -0.245 0.336 0.235 0.576 0.245 0.235 0.263 0.175 0.532 0.368 -0.410 -0.419 -1.997 0.396 0.268 7. D7 -0.605 -0.983 -0.203 0.078 0.155 -2.182 -3.698 -2.994 0.148 0.622 -0.033 -0.078 -0.052 0.238 0.127 -0.940 -1.586 -1.083 -0.892 0.301 8. D8 -0.630 -0.930 -0.852 0.286 0.604 -0.352 -0.465 -0.527 0.499 0.580 -0.214 -0.192 -0.629 0.658 0.416 -0.399 -0.529 -0.670 0.139 0.534 9. D9 -0.829 -0.695 -0.469 0.330 0.765 -0.680 0.034 -0.838 0.795 0.557 -0.353 0.027 -0.461 0.860 0.472 -0.621 -0.211 -0.590 0.117 0.598

10. D10 -0.607 -0.544 -0.135 0.093 0.335 -0.420 -0.353 -0.192 0.139 0.373 -0.270 -0.181 -0.378 0.189 0.448 -0.432 -0.359 -0.235 0.030 0.385 11. D11 -0.668 -0.318 -0.465 0.308 0.428 -0.454 -0.029 0.127 0.327 0.465 -0.396 0.013 1.656 0.553 0.472 -0.506 -0.112 0.439 0.330 0.455 12. D12 -0.878 -0.421 -0.502 0.191 0.102 -0.596 -0.118 -0.092 0.166 0.171 -0.232 2.045 -0.089 0.444 0.149 -0.569 0.502 -0.228 0.181 0.141 13. D13 -0.197 -0.290 -1.192 0.317 0.469 0.180 0.239 0.315 0.722 0.712 0.117 0.218 0.350 1.016 0.657 0.033 0.056 -0.176 0.550 0.613 14. D14 -0.610 -0.432 -0.503 0.540 0.567 -0.328 -0.109 -0.208 1.746 1.520 -0.176 0.042 0.050 1.668 0.359 -0.371 -0.166 -0.220 0.667 0.815 15. D15 -1.018 -1.724 -0.403 0.171 0.182 -0.720 -1.245 -0.998 0.215 0.348 -0.329 0.548 -1.429 0.380 0.386 -0.689 -0.807 -0.944 -0.149 0.305 16. D16 -0.957 -3.497 -0.678 0.396 0.445 -0.519 -1.941 -2.812 1.383 0.219 0.111 -0.364 -0.529 1.451 0.191 -0.455 -1.934 -1.340 -0.322 0.285 17. D17 -1.227 -1.493 -0.667 0.312 0.157 -0.816 -0.917 -0.639 0.619 0.143 -0.493 -0.510 -0.911 1.081 0.208 -0.845 -0.973 -0.739 0.251 0.169 18. D18 -0.014 -0.369 -0.141 0.120 0.151 0.263 -0.223 -0.086 0.211 0.009 0.196 -0.067 -0.319 0.228 0.183 0.148 -0.220 -0.182 0.088 0.115 19. D20 -0.579 -1.075 -0.296 0.083 0.104 -0.239 -0.757 0.032 0.088 0.090 -0.527 -0.767 -0.005 0.214 0.131 -0.448 -0.866 -0.090 0.110 0.108 20. D21 -0.208 -1.287 -0.295 0.288 0.416 -0.218 -0.812 -0.234 0.225 0.441 -0.235 -0.639 0.107 0.914 0.409 -0.220 -0.913 -0.140 0.323 0.422 21. D22 -0.473 -0.034 -0.489 0.325 0.314 -0.271 0.126 -0.005 0.674 0.514 -0.070 0.180 0.320 0.874 0.505 -0.271 0.091 -0.058 0.398 0.444 22. D23 -0.941 -0.428 -0.275 0.244 0.250 -0.729 -0.198 -0.632 0.567 0.321 -0.369 0.078 -0.183 0.468 0.352 -0.680 -0.183 -0.363 0.027 0.307 23. D26 -0.988 -0.736 -0.223 0.052 0.511 -0.566 -0.459 -0.881 0.092 0.556 -0.211 0.020 0.026 0.047 0.987 -0.588 -0.392 -0.359 -0.261 0.685 24. D27 -0.609 -0.235 -1.228 0.119 1.485 -0.390 -0.203 -0.654 0.257 1.897 -0.198 0.084 -0.050 0.501 1.703 -0.399 -0.118 -0.644 -0.011 1.695 25. D28 0.456 -3.278 0.122 1.747 0.331 0.118 -2.743 -0.094 2.077 0.697 -1.483 -1.723 -0.096 0.053 0.608 -0.303 -2.581 -0.023 0.569 0.545 26. D29 -0.898 -0.723 -1.094 0.132 0.192 -0.459 -0.083 -1.534 0.536 0.433 -0.459 -0.083 -1.529 1.293 0.433 -0.605 -0.296 -1.385 -0.036 0.353 27. D30 -0.229 -1.212 -0.207 0.175 0.027 0.032 -1.011 -1.003 0.348 0.114 0.135 -0.329 -1.324 0.144 0.212 -0.021 -0.851 -0.845 -0.228 0.118 28. D32 -0.492 -0.193 -0.199 0.064 0.618 -0.347 -0.056 -0.077 0.188 0.573 -0.214 0.009 0.120 0.318 0.529 -0.351 -0.080 -0.052 0.102 0.573 29. D33 -1.485 -1.416 -0.588 0.410 0.438 -1.090 -0.861 -1.358 0.446 0.353 -0.433 -0.159 -0.399 0.116 0.305 -1.003 -0.812 -0.782 -0.277 0.366 30. D34 -0.708 -1.948 -0.579 0.739 0.231 -0.290 -1.249 -0.963 1.144 0.199 -0.015 -0.474 -0.942 0.370 0.171 -0.338 -1.224 -0.828 0.049 0.200 31. D35 -0.356 -1.039 -0.262 1.176 0.470 -0.427 -0.666 -0.441 0.825 0.307 -0.094 -0.506 -0.847 1.393 0.287 -0.293 -0.737 -0.517 0.709 0.355 32. D36 -1.064 -1.019 -0.777 0.440 0.045 -0.650 -0.515 -0.189 0.322 0.067 -0.520 -0.371 -1.429 1.719 0.079 -0.745 -0.635 -0.798 0.656 0.064

Appendix 4

79

Y-1 Y-2 Y-3 Average 3 years X1 X2 X3 X4 X5 X1 X2 X3 X4 X5 X1 X2 X3 X4 X5 X1 X2 X3 X4 X5

No. Code times times times times times times times times times times times times times times times times times times times times 33. D38 -0.038 -1.308 -2.104 0.073 0.334 0.042 -0.197 -0.270 0.242 0.274 -0.446 -0.062 -0.090 0.300 0.364 -0.147 -0.522 -0.821 0.034 0.324 34. D39 -0.584 -2.321 -2.043 0.168 0.223 -2.011 -1.257 -3.762 0.380 0.601 -0.152 0.218 0.423 0.597 0.385 -0.916 -1.120 -1.794 -0.999 0.403 35. D40 -0.609 -1.047 -1.189 0.145 2.241 -0.345 -0.327 -0.211 0.222 2.333 0.812 -0.146 -0.170 0.198 2.056 -0.047 -0.506 -0.523 0.044 2.210 36. D41 -1.162 -1.905 -4.667 0.302 0.547 0.061 0.107 -0.016 0.566 0.273 0.087 0.117 -0.003 0.677 0.291 -0.338 -0.561 -1.562 0.321 0.370 37. D42 0.080 -0.340 -0.083 0.065 0.000 0.068 -0.303 -0.180 0.226 0.000 0.133 -0.221 -0.121 0.198 0.067 0.094 -0.288 -0.128 0.027 0.022 38. D43 -0.367 -0.787 -0.230 0.271 0.082 -0.305 -0.579 -0.340 0.792 0.312 0.063 -0.305 0.021 1.126 0.210 -0.203 -0.557 -0.183 0.352 0.201

Mean -0.634 -1.037 -0.799 0.313 0.415 -0.446 -0.608 -0.603 0.507 0.484 -0.189 -0.127 -0.236 0.602 0.432 -0.423 -0.591 -0.581 0.108 0.444 Lowest -1.485 -3.497 -6.403 0.052 0.000 -2.182 -3.698 -3.762 0.088 0.000 -1.483 -1.723 -1.529 0.047 0.067 -1.003 -2.581 -2.581 -0.999 0.022 Highest 0.456 -0.034 0.254 1.747 2.241 0.263 0.336 0.315 2.077 2.333 0.812 2.045 1.656 1.719 2.056 0.148 0.502 0.502 0.709 2.210

PN17 Companies 39. D44 -0.441 -0.076 -0.647 0.211 0.219 -0.029 0.163 0.192 1.504 0.407 -0.138 0.114 0.190 0.767 0.400 -0.203 0.067 -0.088 0.390 0.342 40. D45 -0.687 -0.873 -2.426 0.159 0.533 0.133 0.142 0.200 0.206 0.509 0.078 0.183 0.166 0.098 0.432 -0.159 -0.183 -0.687 0.152 0.491 41. D46 -0.869 -0.780 0.046 0.041 0.266 -0.833 -0.725 -0.040 0.085 0.240 -0.802 -0.686 -0.256 0.039 0.176 -0.835 -0.731 -0.083 0.013 0.228 42. D47 -0.706 -1.147 -0.544 0.167 0.414 -0.320 -0.764 -0.368 0.242 0.378 -0.261 -0.511 -0.446 0.219 0.356 -0.429 -0.807 -0.453 0.006 0.383 43. D48 -1.157 -3.244 -3.007 0.117 0.275 -0.376 -0.948 -0.199 0.385 0.287 -0.274 -0.641 -0.435 0.231 0.159 -0.603 -1.611 -1.213 0.050 0.240 44. D49 -0.882 -1.013 -0.536 0.028 0.103 -0.522 -0.562 0.583 0.040 0.186 -0.690 -0.287 -0.008 0.025 0.159 -0.698 -0.621 0.013 0.212 0.149 45. D51 0.025 -2.357 -0.195 0.823 0.090 0.030 -1.963 0.178 1.466 0.098 0.023 -2.340 0.106 3.664 0.082 0.026 -2.220 0.029 1.555 0.090 46. D54 -0.607 -0.252 -0.314 0.165 0.222 -0.169 -0.036 0.040 0.260 0.203 -0.240 0.018 0.091 0.411 0.264 -0.338 -0.090 -0.061 0.206 0.230 47. D55 0.163 -0.430 0.047 1.792 0.134 0.121 -0.436 0.037 1.645 0.132 0.121 -0.427 -0.450 0.953 0.104 0.135 -0.431 -0.122 0.927 0.123 48. D56 -0.766 -0.562 -1.939 0.041 0.418 0.049 0.220 0.046 0.136 0.323 0.136 0.253 0.204 0.217 0.369 -0.194 -0.030 -0.563 0.101 0.370 49. D58 -0.651 -1.189 -1.469 0.084 0.785 0.079 -0.352 -0.445 0.266 0.805 0.338 -0.098 -0.081 0.167 0.819 -0.078 -0.546 -0.665 -0.065 0.803 50. D59 -0.170 -1.914 0.369 0.999 1.474 -0.242 -2.552 0.156 1.177 1.875 0.048 -2.140 -0.591 0.547 1.338 -0.121 -2.202 -0.022 0.567 1.562 51. D60 0.519 -1.241 -0.244 0.509 0.311 0.518 -0.953 -1.933 1.108 0.320 0.754 -0.062 -0.226 1.003 0.299 0.597 -0.752 -0.801 -0.140 0.310 52. D61 -0.183 -0.445 -0.136 0.062 0.035 0.024 -0.327 -0.220 0.109 0.074 0.095 -0.166 -0.438 0.116 0.165 -0.021 -0.313 -0.265 -0.014 0.091

Mean -0.458 -1.109 -0.785 0.371 0.377 -0.110 -0.650 -0.127 0.616 0.417 -0.058 -0.485 -0.155 0.604 0.366 -0.209 -0.748 -0.356 0.283 0.387 Lowest -1.157 -3.244 -3.007 0.028 0.035 -0.833 -2.552 -1.933 0.040 0.074 -0.802 -2.340 -0.591 0.025 0.082 -0.835 -2.220 -1.213 -0.140 0.090 Highest 0.519 -0.076 0.369 1.792 1.474 0.518 0.220 0.583 1.645 1.875 0.754 0.253 0.204 3.664 1.338 0.597 0.067 0.029 1.555 1.562

APN17 Companies 53. D62 0.142 -0.754 0.167 0.038 0.114 0.002 -0.739 0.274 0.045 0.605 -0.038 -0.592 0.748 0.053 0.904 0.035 -0.695 0.396 0.122 0.541 54. D63 -0.258 -0.501 -0.588 0.074 0.733 -0.105 -0.170 0.043 0.268 0.781 -0.015 -0.086 0.086 0.140 0.815 -0.126 -0.252 -0.153 0.086 0.776 55. D64 -0.724 -0.333 -0.270 0.036 0.316 -0.583 0.172 -0.171 0.036 0.300 -0.118 -0.004 -0.255 0.086 0.233 -0.475 -0.055 -0.232 -0.016 0.283 56. D65 0.271 -0.385 -0.003 0.065 0.065 -0.075 -0.326 0.062 0.091 0.098 -0.738 -0.351 -0.016 0.077 0.083 -0.181 -0.354 0.015 0.068 0.082 57. D66 0.091 -2.341 0.066 1.039 1.278 0.140 -2.428 0.207 1.247 1.289 0.166 -2.000 -1.800 4.030 1.244 0.133 -2.256 -0.509 1.758 1.270 58. D67 0.451 -0.240 -0.440 0.212 0.612 0.273 -0.027 0.002 0.168 0.295 0.410 -0.070 -0.013 0.481 0.060 0.378 -0.112 -0.150 0.232 0.322 59. D68 0.174 -1.215 -0.035 0.302 0.034 0.178 -1.171 -0.476 0.519 0.074 0.082 -0.833 -0.031 0.736 0.113 0.144 -1.073 -0.181 0.187 0.073

Appendix 4

80

Y-1 Y-2 Y-3 Average 3 years X1 X2 X3 X4 X5 X1 X2 X3 X4 X5 X1 X2 X3 X4 X5 X1 X2 X3 X4 X5

No. Code times times times times times times times times times times times times times times times times times times times times 60. D69 -0.148 -0.471 -1.080 0.085 0.427 0.542 0.056 0.184 0.158 0.420 0.532 0.044 0.097 0.310 0.479 0.309 -0.124 -0.266 0.193 0.442 61. D70 -0.188 -6.797 -1.550 1.482 0.218 -0.026 -1.604 -0.299 1.720 0.132 -0.470 -1.968 -0.128 0.429 0.073 -0.228 -3.456 -0.659 0.537 0.141 62. D71 0.001 -1.549 -1.587 1.404 2.138 0.003 -0.376 -0.069 1.648 1.771 -0.033 -0.302 -0.804 1.204 2.479 -0.010 -0.742 -0.820 0.847 2.130 63. D72 -0.037 -1.377 -0.602 0.316 1.642 0.018 -1.114 -0.060 0.228 0.721 0.080 -0.703 -1.385 0.179 1.020 0.020 -1.065 -0.683 0.145 1.128 64. D73 -0.493 -5.911 -0.451 3.321 0.556 -0.351 -5.438 -0.304 2.538 0.267 -0.937 -5.105 -0.219 1.531 0.903 -0.593 -5.485 -0.325 1.516 0.576 65. D74 -0.714 -0.525 -0.235 0.104 0.521 -0.531 -0.339 -0.208 0.173 0.605 -0.573 -0.218 0.075 0.290 0.421 -0.606 -0.360 -0.123 0.062 0.516 66. D75 -0.020 -0.719 -0.264 0.480 0.688 0.036 -0.536 0.092 1.195 0.757 -0.036 -0.538 -0.094 1.142 0.647 -0.007 -0.598 -0.089 0.571 0.697 67. D76 -0.265 -0.788 -0.477 0.226 0.915 -0.106 -0.565 -0.429 0.498 0.635 0.231 -0.368 0.136 1.617 0.717 -0.047 -0.574 -0.257 0.471 0.756 68. D77 -0.024 -0.212 0.013 0.110 0.029 -0.036 -0.125 0.008 0.072 0.024 -0.027 -0.048 0.008 0.147 0.011 -0.029 -0.128 0.010 0.088 0.021 69. D78 -0.372 -1.100 0.155 0.168 0.523 -0.400 -1.041 0.085 0.185 0.503 -0.396 -0.908 -0.189 0.269 0.520 -0.389 -1.016 0.017 0.174 0.515 70. D79 -1.711 -1.601 -2.370 0.127 0.463 -0.292 -0.249 0.056 0.035 0.500 -0.213 -0.226 0.135 0.105 0.589 -0.739 -0.692 -0.726 0.096 0.517 71. D80 -0.179 -5.262 0.369 1.730 1.924 -0.289 -5.801 -0.617 1.702 1.696 -0.151 -4.802 -0.294 3.230 1.736 -0.207 -5.288 -0.181 1.448 1.786 72. D81 0.293 0.224 0.314 0.099 0.740 -0.076 0.206 0.282 0.233 0.790 0.022 0.216 0.321 0.274 0.804 0.080 0.215 0.305 0.218 0.778 73. D82 0.025 -1.409 0.345 1.344 1.416 0.000 -2.638 0.017 1.195 0.457 0.148 -3.598 -0.516 2.170 0.185 0.058 -2.549 -0.051 1.177 0.686 74. D83 -0.851 0.265 -7.436 0.168 0.362 0.323 0.081 -0.350 0.397 0.554 0.195 -0.522 0.320 0.695 0.618 -0.111 -0.059 -2.489 0.171 0.511 75. D84 -0.108 -0.350 -0.664 0.028 0.933 0.120 -0.016 0.069 0.132 0.734 0.120 0.008 -0.239 0.182 0.710 0.044 -0.119 -0.278 0.093 0.792 76. D85 -0.526 -1.740 -0.349 0.087 0.632 -0.319 -1.251 -0.101 0.204 0.588 -0.181 -1.003 -0.277 0.464 0.484 -0.342 -1.332 -0.242 0.150 0.568 77. D86 -0.622 -2.405 1.019 0.138 0.734 -0.758 -1.505 -1.868 0.212 0.286 -0.387 -0.620 -0.470 0.277 0.144 -0.589 -1.510 -0.439 -0.484 0.388 78. D87 -0.761 -2.606 -1.006 0.381 0.372 -0.241 -1.552 -0.910 0.701 0.484 0.000 -0.841 -0.742 2.864 0.540 -0.334 -1.666 -0.886 0.778 0.465 79. D88 -0.274 -0.144 -0.563 0.238 0.707 -0.192 0.123 0.109 0.390 0.782 -0.004 0.224 0.214 0.202 0.872 -0.157 0.068 -0.080 0.183 0.787 80. D89 0.066 -1.134 0.009 0.831 0.086 0.123 -1.256 -0.097 2.643 0.749 -0.016 -1.413 -0.210 1.684 0.756 0.058 -1.268 -0.099 0.806 0.530 81. D90 0.071 -0.161 0.050 0.018 0.027 0.069 -0.046 0.000 0.054 0.006 0.069 -0.046 0.000 0.191 0.006 0.070 -0.084 0.016 0.070 0.013 82. D91 -0.050 -0.815 -0.795 0.178 0.266 -0.242 -0.367 0.007 0.126 0.286 0.274 -0.323 -0.041 0.179 0.280 -0.006 -0.502 -0.276 0.121 0.277 83. D92 -0.353 0.124 -0.764 0.093 0.185 0.178 0.077 0.122 0.468 0.256 0.186 0.067 0.056 0.601 0.192 0.004 0.089 -0.195 0.272 0.211 84. D93 -0.825 -0.361 -0.224 0.042 0.251 -0.533 -0.169 -0.078 0.245 0.283 -0.475 -0.068 -0.337 0.370 0.268 -0.611 -0.199 -0.213 0.111 0.267 85. D94 -0.243 -1.421 0.101 0.639 1.327 -0.238 -1.438 0.053 0.664 1.276 -0.235 -1.377 -0.110 1.289 1.223 -0.239 -1.412 0.015 0.661 1.275 86. D95 -0.533 -3.592 -0.431 0.961 0.513 -0.257 -2.982 -0.627 1.439 0.512 -0.003 -2.319 -0.078 3.086 0.520 -0.264 -2.964 -0.378 1.140 0.515 87. D96 -0.053 -1.073 -0.020 0.311 0.195 -0.066 -0.822 0.000 0.656 0.402 -0.025 -0.833 -0.330 0.621 0.323 -0.048 -0.909 -0.116 0.311 0.307 88. D97 -0.344 -1.034 -0.633 0.744 1.981 -0.149 -1.266 0.192 2.864 1.736 -0.235 -0.960 -0.898 1.439 1.785 -0.243 -1.087 -0.446 0.792 1.834 89. D98 -0.385 -0.810 -0.836 0.254 0.844 -0.057 -0.305 -0.998 0.196 0.736 0.295 0.094 0.151 0.809 0.784 -0.049 -0.341 -0.561 0.022 0.788

Mean -0.256 -1.366 -0.569 0.483 0.669 -0.106 -0.999 -0.157 0.685 0.605 -0.067 -0.875 -0.193 0.904 0.636 -0.143 -1.080 -0.306 0.410 0.637 Lowest -1.711 -6.797 -7.436 0.018 0.027 -0.758 -5.801 -1.868 0.035 0.006 -0.937 -5.105 -1.800 0.053 0.006 -0.739 -5.485 -2.489 -0.484 0.013 Highest 0.451 0.265 1.019 3.321 2.138 0.542 0.206 0.282 2.864 1.771 0.532 0.224 0.748 4.030 2.479 0.378 0.215 0.396 1.758 2.130

Appendix 5

81

Variables of the sample companies for Y-1, Y-2, Y-3 and 3-year average Y-1 Y-2 Y-3 Average 3 years

Z-Score Stock

returns MV BMV Z-Score Stock

returns MV BMV Z-Score Stock

returns MV BMV Z-Score Stock

returns MV BMV No. Code Z (%) (log) (times) Z (%) (log) (times) Z (%) (log) (times) Z (%) (log) (times) PN4 Companies

1. D1 -1.421 -7.820 4.828 0.949 -1.692 -7.660 4.987 0.815 -1.280 6.720 4.802 3.026 -1.464 -2.920 4.872 1.597 2. D2 -0.316 3.500 4.398 -0.061 -0.002 4.820 4.439 0.285 1.583 -11.480 4.787 0.441 0.421 -1.053 4.541 0.222 3. D3 -1.281 -4.020 4.530 0.307 -0.679 -9.610 4.594 0.507 1.316 2.530 4.657 0.630 -0.215 -3.700 4.594 0.481 4. D4 0.335 -2.260 4.537 0.186 -0.167 -9.360 4.773 0.029 -0.820 6.770 4.737 0.376 -0.217 -1.617 4.682 0.197 5. D5 -1.596 18.530 4.207 0.137 0.091 -9.340 4.207 2.696 0.281 -1.930 4.437 1.867 -0.408 2.420 4.284 1.566 6. D6 -8.865 9.570 5.042 -0.277 1.147 -12.360 5.194 2.179 1.573 0.710 5.216 1.963 -2.048 -0.693 5.151 1.288 7. D7 -1.559 3.130 4.566 -3.315 -8.103 -8.140 4.816 -1.148 0.203 -1.380 4.987 0.057 -3.153 -2.130 4.789 -1.469 8. D8 -1.522 8.470 4.304 -0.390 -0.265 -6.140 4.463 0.291 0.039 -2.010 4.412 0.750 -0.583 0.107 4.393 0.217 9. D9 -0.898 0.780 4.471 0.096 -0.131 -5.140 4.865 0.253 0.545 -0.850 4.892 0.605 -0.161 -1.737 4.743 0.318

10. D10 -0.858 1.470 4.334 -0.840 -0.453 -9.990 4.497 0.037 -0.192 1.070 4.640 0.411 -0.501 -2.483 4.490 -0.131 11. D11 -0.715 1.210 4.843 -0.373 0.436 -2.250 4.866 0.115 2.298 -6.600 5.060 0.125 0.673 -2.547 4.923 -0.044 12. D12 -1.509 -4.280 4.665 0.882 -0.469 0.250 4.602 2.140 2.318 -7.370 5.035 0.986 0.113 -3.800 4.768 1.336 13. D13 -0.893 -10.270 4.455 0.362 2.170 -0.360 4.731 0.956 2.358 -6.430 4.770 0.787 1.212 -5.687 4.652 0.702 14. D14 -0.437 -4.460 4.176 -3.294 2.621 -10.550 4.633 0.007 1.943 0.820 4.683 0.628 1.375 -4.730 4.498 -0.886 15. D15 -2.793 -8.970 4.446 -0.016 -2.400 0.060 4.543 0.681 -0.443 -8.850 4.791 1.294 -1.879 -5.920 4.593 0.653 16. D16 -4.292 12.290 4.751 -0.603 -3.670 -6.030 5.402 0.049 0.861 -6.750 5.445 0.778 -2.367 -0.163 5.199 0.075 17. D17 -2.919 1.450 4.563 -0.181 -1.610 -4.720 4.842 0.194 -0.624 -1.790 5.063 0.289 -1.718 -1.687 4.823 0.100 18. D18 -0.253 -1.210 4.333 2.082 0.174 -9.390 4.580 1.597 0.222 7.200 4.628 1.589 0.048 -1.133 4.514 1.756 19. D20 -1.763 3.270 4.738 -0.888 -0.787 -1.510 4.755 0.480 -0.954 -7.050 5.195 0.080 -1.168 -1.763 4.896 -0.109 20. D21 -1.086 3.210 5.054 -0.153 -0.597 1.560 4.977 0.540 0.557 -4.210 5.712 0.095 -0.375 0.187 5.248 0.161 21. D22 -0.357 -3.610 4.911 1.215 1.038 -3.910 5.062 1.065 1.809 -1.880 5.021 1.312 0.830 -3.133 4.998 1.197 22. D23 -1.150 -3.410 4.425 -0.216 -0.672 -6.350 4.841 0.198 0.346 -2.520 4.799 0.727 -0.492 -4.093 4.689 0.236 23. D26 -1.384 -4.870 4.652 -3.619 -1.257 -6.460 4.854 -1.417 0.869 -0.860 4.564 1.734 -0.591 -4.063 4.690 -1.101 24. D27 -0.468 3.060 3.999 0.463 0.907 -9.180 4.499 0.976 2.040 -3.640 4.601 1.912 0.826 -3.253 4.366 1.117 25. D28 -0.623 -4.010 5.440 -0.955 0.055 -6.540 5.847 -0.545 -2.640 0.190 5.069 -8.107 -1.069 -3.453 5.452 -3.203 26. D29 -2.390 -7.470 4.857 -1.462 -1.107 1.590 5.436 0.351 -0.344 -12.450 5.818 0.145 -1.280 -6.110 5.370 -0.322 27. D30 -1.447 9.470 4.954 -0.353 -1.520 -7.150 5.214 0.104 -1.163 1.430 4.884 2.419 -1.377 1.250 5.017 0.724 28. D32 -0.201 -2.800 5.061 0.417 0.281 -2.320 5.543 0.664 0.763 -6.050 5.763 0.628 0.281 -3.723 5.456 0.570

Appendix 5

82

Y-1 Y-2 Y-3 Average 3 years

Z-Score Stock

returns MV BMV Z-Score Stock

returns MV BMV Z-Score Stock

returns MV BMV Z-Score Stock

returns MV BMV No. Code Z (%) (log) (times) Z (%) (log) (times) Z (%) (log) (times) Z (%) (log) (times) 29. D33 -2.641 -0.320 5.574 -0.901 -2.510 -11.440 5.606 -0.479 -0.570 15.700 4.979 0.767 -1.907 1.313 5.386 -0.204 30. D34 -2.266 0.000 5.558 -0.269 -1.159 -3.210 5.754 0.176 -0.890 4.500 5.426 1.727 -1.438 0.430 5.579 0.545 31. D35 -0.011 5.900 5.181 0.032 -0.402 1.840 5.312 0.210 0.232 -11.290 5.568 0.227 -0.060 -1.183 5.354 0.156 32. D36 -2.374 6.280 4.407 -0.244 -0.965 -2.450 4.246 1.222 -0.523 0.590 4.959 0.339 -1.288 1.473 4.537 0.439 33. D38 -3.043 -16.130 4.937 -3.230 0.091 6.350 5.481 0.473 0.065 -4.000 5.488 0.849 -0.962 -4.593 5.302 -0.636 34. D39 -4.558 4.420 4.691 -2.025 -6.049 -14.820 5.018 -0.224 1.471 1.430 5.213 0.658 -3.045 -2.990 4.974 -0.530 35. D40 -0.459 -10.250 4.375 -1.454 1.672 3.320 4.593 0.668 2.751 -3.890 4.659 1.053 1.321 -3.607 4.542 0.089 36. D41 -6.884 -2.800 4.309 -1.273 0.991 -10.360 4.548 0.765 1.168 0.410 4.606 0.680 -1.575 -4.250 4.487 0.057 37. D42 -0.278 -1.780 4.522 0.481 -0.190 -6.610 5.074 0.371 0.056 -1.660 5.011 1.162 -0.137 -3.350 4.869 0.671 38. D43 -1.032 -6.550 4.447 0.176 -0.120 -10.280 4.857 0.246 1.115 -3.040 5.010 0.330 -0.012 -6.623 4.772 0.251

Mean -1.742 -0.297 4.672 -0.490 -0.666 -5.101 4.909 0.461 0.483 -1.787 4.984 0.667 -0.642 -2.395 4.855 0.213 Lowest -8.865 -16.130 3.999 -3.619 -8.103 -14.820 4.207 -1.417 -2.640 -12.450 4.412 -8.107 -3.153 -6.623 4.284 -3.203 Highest 0.335 18.530 5.574 2.082 2.621 6.350 5.847 2.696 2.751 15.700 5.818 3.026 1.375 2.420 5.579 1.756

PN17 Companies 39. D44 -0.734 -5.310 5.661 0.228 2.237 -7.700 6.461 0.162 1.333 0.300 6.060 0.329 0.945 -4.237 6.061 0.240 40. D45 -3.296 -15.870 4.778 -0.317 1.190 -7.420 4.905 2.203 0.956 1.140 4.535 4.890 -0.383 -7.383 4.739 2.259 41. D46 -1.296 -0.590 3.840 0.113 -1.272 0.230 4.158 0.382 -1.529 -0.330 3.822 1.533 -1.366 -0.230 3.940 0.676 42. D47 -1.816 -7.970 4.491 1.003 -0.832 -1.730 4.675 1.451 -0.642 -3.430 4.644 2.294 -1.097 -4.377 4.603 1.583 43. D48 -7.015 -4.470 4.334 -1.663 -0.851 -4.530 4.845 1.035 -0.961 -7.340 4.614 2.243 -2.942 -5.447 4.597 0.539 44. D49 -2.300 -1.990 4.733 -3.622 -0.275 -3.130 4.858 1.526 -0.801 2.420 4.800 0.987 -1.125 -0.900 4.797 -0.370 45. D51 -1.613 17.320 4.991 1.274 -0.191 -0.830 5.212 0.952 1.535 -5.380 5.545 0.388 -0.090 3.703 5.249 0.871 46. D54 -0.786 -9.960 4.567 0.940 0.298 -6.520 4.764 1.105 0.544 -7.110 4.956 0.743 0.019 -7.863 4.762 0.929 47. D55 1.705 3.790 4.736 2.407 1.498 -6.040 4.736 2.359 0.301 -0.990 4.483 4.224 1.168 -1.080 4.651 2.997 48. D56 -2.808 -6.570 4.210 -1.699 0.773 -7.630 4.717 2.516 1.179 -3.250 4.869 1.834 -0.285 -5.817 4.599 0.883 49. D58 -2.440 -5.440 4.128 -4.144 0.352 -11.000 4.615 0.200 1.145 -0.400 4.460 1.343 -0.314 -5.613 4.401 -0.867 50. D59 0.757 3.150 4.699 0.246 0.414 -1.160 4.771 0.101 -0.798 3.990 4.477 0.181 0.124 1.993 4.649 0.176 51. D60 -0.147 -4.650 4.351 1.250 -0.941 -5.260 4.706 0.700 1.769 3.660 4.880 1.127 0.227 -2.083 4.646 1.026 52. D61 -0.667 0.030 4.566 3.782 -0.340 2.870 4.804 2.894 -0.227 -6.070 4.804 3.926 -0.411 -1.057 4.725 3.534

Mean -1.604 -2.752 4.578 -0.015 0.147 -4.275 4.873 1.256 0.272 -1.628 4.782 1.860 -0.395 -2.885 4.744 1.034 Lowest -7.015 -15.870 3.840 -4.144 -1.272 -11.000 4.158 0.101 -1.529 -7.340 3.822 0.181 -2.942 -7.863 3.940 -0.867 Highest 1.705 17.320 5.661 3.782 2.237 2.870 6.461 2.894 1.769 3.990 6.060 4.890 1.168 3.703 6.061 3.534

Appendix 5

83

Y-1 Y-2 Y-3 Average 3 years

Z-Score Stock

returns MV BMV Z-Score Stock

returns MV BMV Z-Score Stock

returns MV BMV Z-Score Stock

returns MV BMV No. Code Z (%) (log) (times) Z (%) (log) (times) Z (%) (log) (times) Z (%) (log) (times) APN17 Companies 53. D62 -0.293 0.430 5.300 1.156 0.186 -5.540 5.380 0.891 1.075 1.750 5.486 0.737 0.323 -1.120 5.389 0.928 54. D63 -0.540 6.070 4.288 0.250 0.818 -11.030 4.834 0.653 0.940 -2.280 4.603 1.428 0.406 -2.413 4.575 0.777 55. D64 -0.976 -8.120 3.786 2.739 -0.246 -3.920 3.786 5.831 -0.057 -2.600 4.166 3.326 -0.426 -4.880 3.912 3.966 56. D65 0.013 0.750 4.539 5.099 -0.149 -6.040 4.752 3.418 -0.945 -1.800 4.714 1.061 -0.360 -2.363 4.668 3.193 57. D66 0.133 2.480 4.426 0.462 0.456 -1.280 4.479 0.469 1.640 -0.170 4.775 0.198 0.743 0.343 4.560 0.376 58. D67 0.594 -8.310 4.971 2.367 0.712 3.280 4.982 3.178 0.869 -4.950 5.061 2.371 0.725 -3.327 5.005 2.639 59. D68 -0.741 5.800 4.867 9.462 -0.876 -0.400 5.119 5.408 0.066 -4.780 5.252 4.983 -0.517 0.207 5.079 6.618 60. D69 -1.187 -10.530 4.078 0.538 1.361 -9.990 4.347 2.198 1.462 -1.740 4.635 1.103 0.545 -7.420 4.353 1.280 61. D70 -6.835 0.810 4.657 -0.239 -0.077 -5.540 5.048 0.079 -2.064 -2.320 4.418 0.303 -2.992 -2.350 4.707 0.047 62. D71 0.408 0.420 4.153 0.666 2.976 3.160 4.822 0.458 2.543 -4.270 4.576 0.712 1.976 -0.230 4.517 0.612 63. D72 -0.059 -7.570 4.646 1.888 -0.207 2.230 4.780 0.889 -0.809 -6.740 4.909 1.121 -0.358 -4.027 4.778 1.299 64. D73 -2.978 -4.140 4.421 0.319 -3.288 1.470 4.317 0.566 -3.826 -0.330 4.582 0.035 -3.364 -1.000 4.440 0.307 65. D74 -0.849 5.710 3.861 0.387 -0.299 -5.010 4.111 0.623 -0.003 -0.330 4.304 0.540 -0.384 0.123 4.092 0.517 66. D75 0.164 7.390 4.155 2.310 1.543 -4.710 4.512 1.207 1.121 -3.570 4.529 1.122 0.943 -0.297 4.399 1.546 67. D76 -0.390 -5.540 4.402 1.026 0.033 -6.820 4.738 0.836 2.333 -5.990 5.096 0.588 0.659 -6.117 4.746 0.816 68. D77 -0.085 1.600 5.359 0.257 -0.056 1.070 5.147 0.993 0.091 -3.530 5.424 0.812 -0.017 -0.287 5.310 0.687 69. D78 -0.626 -2.340 4.292 0.523 -0.668 -5.400 4.377 0.482 -0.705 -0.750 4.563 0.430 -0.666 -2.830 4.411 0.479 70. D79 -5.093 -4.550 4.085 -3.556 0.051 -2.180 3.505 2.725 0.390 7.410 3.997 0.636 -1.551 0.227 3.862 -0.065 71. D80 -1.418 -2.270 4.181 0.202 -3.309 3.570 4.267 0.115 -0.282 -8.640 4.575 0.220 -1.669 -2.447 4.341 0.179 72. D81 1.669 -1.550 5.139 3.417 1.435 -2.900 5.399 1.667 1.637 -2.340 5.271 1.324 1.580 -2.263 5.270 2.136 73. D82 1.720 3.700 4.317 0.507 -0.970 2.950 4.394 0.230 -1.612 -2.390 4.735 0.124 -0.287 1.420 4.482 0.287 74. D83 -7.493 -5.770 4.360 -2.571 1.005 -9.170 4.739 1.517 1.306 -6.320 5.127 1.034 -1.727 -7.087 4.742 -0.007 75. D84 -0.161 10.750 3.538 5.100 1.039 -2.530 4.307 2.222 0.782 -5.760 4.343 2.164 0.553 0.820 4.063 3.162 76. D85 -1.896 -5.750 4.027 0.747 -0.879 -2.760 4.467 0.944 -0.514 -6.580 4.872 0.540 -1.096 -5.030 4.455 0.744 77. D86 -1.136 5.710 3.929 0.687 -3.632 0.950 4.406 0.495 -1.056 -5.290 4.583 0.850 -1.942 0.457 4.306 0.677 78. D87 -3.621 3.260 4.269 -0.032 -1.517 -2.290 4.481 0.627 1.820 -7.810 5.129 0.268 -1.106 -2.280 4.626 0.288 79. D88 -0.036 -2.590 4.331 1.709 1.213 -5.030 4.508 1.844 1.508 0.150 4.191 3.751 0.895 -2.490 4.343 2.435 80. D89 -0.143 -0.740 4.776 0.433 2.162 -4.630 5.296 0.142 0.801 -7.650 5.185 0.047 0.940 -4.340 5.086 0.207 81. D90 0.005 10.830 4.459 1.583 0.083 -0.550 4.879 2.112 0.220 -9.880 5.426 0.599 0.103 0.133 4.921 1.432 82. D91 -1.216 -2.430 4.282 0.375 -0.190 -5.430 4.201 0.485 0.369 -3.480 4.329 0.504 -0.346 -3.780 4.271 0.455

Appendix 5

84

Y-1 Y-2 Y-3 Average 3 years

Z-Score Stock

returns MV BMV Z-Score Stock

returns MV BMV Z-Score Stock

returns MV BMV Z-Score Stock

returns MV BMV No. Code Z (%) (log) (times) Z (%) (log) (times) Z (%) (log) (times) Z (%) (log) (times) 83. D92 -0.715 9.490 5.121 8.119 1.101 -8.620 5.834 1.459 1.103 -3.390 5.886 1.309 0.496 -0.840 5.613 3.629 84. D93 -1.116 -6.920 3.905 2.773 -0.252 -0.040 4.636 1.020 -0.241 -6.790 4.804 0.890 -0.537 -4.583 4.448 1.561 85. D94 0.403 12.400 4.417 0.071 0.317 3.480 4.434 0.119 0.790 -1.290 4.722 0.090 0.503 4.863 4.524 0.093 86. D95 -3.081 -3.150 4.619 0.294 -1.915 -4.130 4.809 0.330 1.206 -2.780 5.054 0.306 -1.263 -3.353 4.827 0.310 87. D96 -0.639 0.700 4.672 0.389 0.170 -2.600 5.062 0.263 -0.244 -2.710 5.033 0.363 -0.237 -1.537 4.923 0.339 88. D97 0.715 -8.510 4.417 1.008 3.377 -7.480 4.936 0.137 1.131 -1.660 4.810 0.318 1.741 -5.883 4.721 0.488 89. D98 -0.934 -7.070 4.411 0.573 -0.429 1.140 4.348 1.635 2.133 -3.860 4.657 1.569 0.257 -3.263 4.472 1.259

Mean -1.039 -0.258 4.418 1.379 0.029 -2.776 4.660 1.305 0.405 -3.391 4.806 1.021 -0.202 -2.142 4.628 1.235 Lowest -7.493 -10.530 3.538 -3.556 -3.632 -11.030 3.505 0.079 -3.826 -9.880 3.997 0.035 -3.364 -7.420 3.862 -0.065 Highest 1.720 12.400 5.359 9.462 3.377 3.570 5.834 5.831 2.543 7.410 5.886 4.983 1.976 4.863 5.613 6.618

Appendix 6

85

Average return, MV and BMV of the sample companies for Y-1 Distress Y-1

Z-score Average return

Market value

Book-to-market

No. Code Z (%) (log) times 1. D82 1.720 3.700 4.317 0.507 2. D55 1.705 3.790 4.736 2.407 3. D81 1.669 -1.550 5.139 3.417 4. D59 0.757 3.150 4.699 0.246 5. D97 0.715 -8.510 4.417 1.008 6. D67 0.594 -8.310 4.971 2.367 7. D71 0.408 0.420 4.153 0.666 8. D94 0.403 12.400 4.417 0.071 9. D4 0.335 -2.260 4.537 0.186

10. D75 0.164 7.390 4.155 2.310 11. D66 0.133 2.480 4.426 0.462 12. D65 0.013 0.750 4.539 5.099 13. D90 0.005 10.830 4.459 1.583

Mean 0.663 1.868 4.536 1.564 Lowest 0.005 -8.510 4.153 0.071 Highest 1.720 12.400 5.139 5.099

Most distressed Y-1

Z-score Average return

Market value

Book-to-market

No. Code Z (%) (log) times 1. D35 -0.011 5.900 5.181 0.032 2. D88 -0.036 -2.590 4.331 1.709 3. D72 -0.059 -7.570 4.646 1.888 4. D77 -0.085 1.600 5.359 0.257 5. D89 -0.143 -0.740 4.776 0.433 6. D60 -0.147 -4.650 4.351 1.250 7. D84 -0.161 10.750 3.538 5.100 8. D32 -0.201 -2.800 5.061 0.417 9. D18 -0.253 -1.210 4.333 2.082

10. D42 -0.278 -1.780 4.522 0.481 11. D62 -0.293 0.430 5.300 1.156 12. D2 -0.316 3.500 4.398 -0.061 13. D22 -0.357 -3.610 4.911 1.215 14. D76 -0.390 -5.540 4.402 1.026 15. D14 -0.437 -4.460 4.176 -3.294 16. D40 -0.459 -10.250 4.375 -1.454 17. D27 -0.468 3.060 3.999 0.463 18. D63 -0.540 6.070 4.288 0.250 19. D28 -0.623 -4.010 5.440 -0.955 20. D78 -0.626 -2.340 4.292 0.523 21. D96 -0.639 0.700 4.672 0.389 22. D61 -0.667 0.030 4.566 3.782 23. D11 -0.715 1.210 4.843 -0.373

Appendix 6

86

Most distressed Y-1 Z-score Average

return Market value

Book-to-market

No. Code Z (%) (log) times 24. D92 -0.715 9.490 5.121 8.119 25. D44 -0.734 -5.310 5.661 0.228 26. D68 -0.741 5.800 4.867 9.462 27. D54 -0.786 -9.960 4.567 0.940 28. D74 -0.849 5.710 3.861 0.387 29. D10 -0.858 1.470 4.334 -0.840 30. D13 -0.893 -10.270 4.455 0.362 31. D9 -0.898 0.780 4.471 0.096 32. D98 -0.934 -7.070 4.411 0.573 33. D64 -0.976 -8.120 3.786 2.739 34. D43 -1.032 -6.550 4.447 0.176 35. D21 -1.086 3.210 5.054 -0.153 36. D93 -1.116 -6.920 3.905 2.773 37. D86 -1.136 5.710 3.929 0.687 38. D23 -1.150 -3.410 4.425 -0.216 39. D69 -1.187 -10.530 4.078 0.538 40. D91 -1.216 -2.430 4.282 0.375 41. D3 -1.281 -4.020 4.530 0.307 42. D46 -1.296 -0.590 3.840 0.113 43. D26 -1.384 -4.870 4.652 -3.619 44. D80 -1.418 -2.270 4.181 0.202 45. D1 -1.421 -7.820 4.828 0.949 46. D30 -1.447 9.470 4.954 -0.353 47. D12 -1.509 -4.280 4.665 0.882 48. D8 -1.522 8.470 4.304 -0.390 49. D7 -1.559 3.130 4.566 -3.315 50. D5 -1.596 18.530 4.207 0.137 51. D51 -1.613 17.320 4.991 1.274 52. D20 -1.763 3.270 4.738 -0.888 53. D47 -1.816 -7.970 4.491 1.003 54. D85 -1.896 -5.750 4.027 0.747 55. D34 -2.266 0.000 5.558 -0.269 56. D49 -2.300 -1.990 4.733 -3.622 57. D36 -2.374 6.280 4.407 -0.244 58. D29 -2.390 -7.470 4.857 -1.462 59. D58 -2.440 -5.440 4.128 -4.144 60. D33 -2.641 -0.320 5.574 -0.901 61. D15 -2.793 -8.970 4.446 -0.016 62. D56 -2.808 -6.570 4.210 -1.699 63. D17 -2.919 1.450 4.563 -0.181 64. D73 -2.978 -4.140 4.421 0.319 65. D38 -3.043 -16.130 4.937 -3.230 66. D95 -3.081 -3.150 4.619 0.294 67. D45 -3.296 -15.870 4.778 -0.317 68. D87 -3.621 3.260 4.269 -0.032 69. D16 -4.292 12.290 4.751 -0.603

Appendix 6

87

Most distressed Y-1 Z-score Average

return Market value

Book-to-market

No. Code Z (%) (log) times 70. D39 -4.558 4.420 4.691 -2.025 71. D79 -5.093 -4.550 4.085 -3.556 72. D70 -6.835 0.810 4.657 -0.239 73. D41 -6.884 -2.800 4.309 -1.273 74. D48 -7.015 -4.470 4.334 -1.663 75. D83 -7.493 -5.770 4.360 -2.571 76. D6 -8.865 9.570 5.042 -0.277

Mean -1.786 -1.101 4.554 0.157 Lowest -8.865 -16.130 3.538 -4.144

Highest -0.011 18.530 5.661 9.462

Appendix 7

88

Average return, MV and BMV of the sample companies for Y-2 Distress Y-2

Z-score Average return

Market value

Book-to-market

No. Code Z (%) (log) times 1. D97 3.377 -7.480 4.936 0.137 2. D71 2.976 3.160 4.822 0.458 3. D14 2.621 -10.550 4.633 0.007 4. D44 2.237 -7.700 6.461 0.162 5. D13 2.170 -0.360 4.731 0.956 6. D89 2.162 -4.630 5.296 0.142 7. D40 1.672 3.320 4.593 0.668 8. D75 1.543 -4.710 4.512 1.207 9. D55 1.498 -6.040 4.736 2.359

10. D81 1.435 -2.900 5.399 1.667 11. D69 1.361 -9.990 4.347 2.198 12. D88 1.213 -5.030 4.508 1.844 13. D45 1.190 -7.420 4.905 2.203 14. D6 1.147 -12.360 5.194 2.179 15. D92 1.101 -8.620 5.834 1.459 16. D84 1.039 -2.530 4.307 2.222 17. D22 1.038 -3.910 5.062 1.065 18. D83 1.005 -9.170 4.739 1.517 19. D41 0.991 -10.360 4.548 0.765 20. D27 0.907 -9.180 4.499 0.976 21. D63 0.818 -11.030 4.834 0.653 22. D56 0.773 -7.630 4.717 2.516 23. D67 0.712 3.280 4.982 3.178 24. D66 0.456 -1.280 4.479 0.469 25. D11 0.436 -2.250 4.866 0.115 26. D59 0.414 -1.160 4.771 0.101 27. D58 0.352 -11.000 4.615 0.200 28. D94 0.317 3.480 4.434 0.119 29. D54 0.298 -6.520 4.764 1.105 30. D32 0.281 -2.320 5.543 0.664 31. D62 0.186 -5.540 5.380 0.891 32. D18 0.174 -9.390 4.580 1.597 33. D96 0.170 -2.600 5.062 0.263 34. D5 0.091 -9.340 4.207 2.696 35. D38 0.091 6.350 5.481 0.473 36. D90 0.083 -0.550 4.879 2.112 37. D28 0.055 -6.540 5.847 -0.545 38. D79 0.051 -2.180 3.505 2.725 39. D76 0.033 -6.820 4.738 0.836

Mean 0.986 -4.859 4.865 1.137 Lowest 0.033 -12.360 3.505 -0.545 Highest 3.377 6.350 6.461 3.178

Appendix 7

89

Most distress Y-2 Z-score Average

return Market value

Book-to-market

No. Code Z (%) (log) times 1. D2 -0.002 4.820 4.439 0.285 2. D77 -0.056 1.070 5.147 0.993 3. D70 -0.077 -5.540 5.048 0.079 4. D43 -0.120 -10.280 4.857 0.246 5. D9 -0.131 -5.140 4.865 0.253 6. D65 -0.149 -6.040 4.752 3.418 7. D4 -0.167 -9.360 4.773 0.029 8. D42 -0.190 -6.610 5.074 0.371 9. D91 -0.190 -5.430 4.201 0.485

10. D51 -0.191 -0.830 5.212 0.952 11. D72 -0.207 2.230 4.780 0.889 12. D64 -0.246 -3.920 3.786 5.831 13. D93 -0.252 -0.040 4.636 1.020 14. D8 -0.265 -6.140 4.463 0.291 15. D49 -0.275 -3.130 4.858 1.526 16. D74 -0.299 -5.010 4.111 0.623 17. D61 -0.340 2.870 4.804 2.894 18. D35 -0.402 1.840 5.312 0.210 19. D98 -0.429 1.140 4.348 1.635 20. D10 -0.453 -9.990 4.497 0.037 21. D12 -0.469 0.250 4.602 2.140 22. D21 -0.597 1.560 4.977 0.540 23. D78 -0.668 -5.400 4.377 0.482 24. D23 -0.672 -6.350 4.841 0.198 25. D3 -0.679 -9.610 4.594 0.507 26. D20 -0.787 -1.510 4.755 0.480 27. D47 -0.832 -1.730 4.675 1.451 28. D48 -0.851 -4.530 4.845 1.035 29. D68 -0.876 -0.400 5.119 5.408 30. D85 -0.879 -2.760 4.467 0.944 31. D60 -0.941 -5.260 4.706 0.700 32. D36 -0.965 -2.450 4.246 1.222 33. D82 -0.970 2.950 4.394 0.230 34. D29 -1.107 1.590 5.436 0.351 35. D34 -1.159 -3.210 5.754 0.176 36. D26 -1.257 -6.460 4.854 -1.417 37. D46 -1.272 0.230 4.158 0.382 38. D87 -1.517 -2.290 4.481 0.627 39. D30 -1.520 -7.150 5.214 0.104 40. D17 -1.610 -4.720 4.842 0.194 41. D1 -1.692 -7.660 4.987 0.815 42. D95 -1.915 -4.130 4.809 0.330 43. D15 -2.400 0.060 4.543 0.681 44. D33 -2.510 -11.440 5.606 -0.479 45. D73 -3.288 1.470 4.317 0.566 46. D80 -3.309 3.570 4.267 0.115

Appendix 7

90

Most distress Y-2 Z-score Average

return Market value

Book-to-market

No. Code Z (%) (log) times 47. D86 -3.632 0.950 4.406 0.495 48. D16 -3.670 -6.030 5.402 0.049 49. D39 -6.049 -14.820 5.018 -0.224 50. D7 -8.103 -8.140 4.816 -1.148

Mean -1.213 -3.338 4.749 0.780 Lowest -8.103 -14.820 3.786 -1.417 Highest -0.002 4.820 5.754 5.831

Appendix 8

91

Average return, MV and BMV of the sample companies for Y-3 Distress Y-3

Z-score Average return

Market value

Book-to-market

No. Code Z (%) (log) times 1. D40 2.751 -3.890 4.659 1.053 2. D71 2.543 -4.270 4.576 0.712 3. D13 2.358 -6.430 4.770 0.787 4. D76 2.333 -5.990 5.096 0.588 5. D12 2.318 -7.370 5.035 0.986 6. D11 2.298 -6.600 5.060 0.125 7. D98 2.133 -3.860 4.657 1.569 8. D27 2.040 -3.640 4.601 1.912 9. D14 1.943 0.820 4.683 0.628

10. D87 1.820 -7.810 5.129 0.268 11. D22 1.809 -1.880 5.021 1.312 12. D60 1.769 3.660 4.880 1.127 13. D66 1.640 -0.170 4.775 0.198 14. D81 1.637 -2.340 5.271 1.324 15. D2 1.583 -11.480 4.787 0.441 16. D6 1.573 0.710 5.216 1.963 17. D51 1.535 -5.380 5.545 0.388 18. D88 1.508 0.150 4.191 3.751 19. D39 1.471 1.430 5.213 0.658 20. D69 1.462 -1.740 4.635 1.103 21. D44 1.333 0.300 6.060 0.329 22. D3 1.316 2.530 4.657 0.630 23. D83 1.306 -6.320 5.127 1.034 24. D95 1.206 -2.780 5.054 0.306 25. D56 1.179 -3.250 4.869 1.834 26. D41 1.168 0.410 4.606 0.680 27. D58 1.145 -0.400 4.460 1.343 28. D97 1.131 -1.660 4.810 0.318 29. D75 1.121 -3.570 4.529 1.122 30. D43 1.115 -3.040 5.010 0.330 31. D92 1.103 -3.390 5.886 1.309 32. D62 1.075 1.750 5.486 0.737 33. D45 0.956 1.140 4.535 4.890 34. D63 0.940 -2.280 4.603 1.428 35. D26 0.869 -0.860 4.564 1.734 36. D67 0.869 -4.950 5.061 2.371 37. D16 0.861 -6.750 5.445 0.778 38. D89 0.801 -7.650 5.185 0.047 39. D94 0.790 -1.290 4.722 0.090 40. D84 0.782 -5.760 4.343 2.164 41. D32 0.763 -6.050 5.763 0.628 42. D21 0.557 -4.210 5.712 0.095 43. D9 0.545 -0.850 4.892 0.605 44. D54 0.544 -7.110 4.956 0.743

Appendix 8

92

Distress Y-3 Z-score Average

return Market value

Book-to-market

No. Code Z (%) (log) times 45. D79 0.390 7.410 3.997 0.636 46. D91 0.369 -3.480 4.329 0.504 47. D23 0.346 -2.520 4.799 0.727 48. D55 0.301 -0.990 4.483 4.224 49. D5 0.281 -1.930 4.437 1.867 50. D35 0.232 -11.290 5.568 0.227 51. D18 0.222 7.200 4.628 1.589 52. D90 0.220 -9.880 5.426 0.599 53. D7 0.203 -1.380 4.987 0.057 54. D77 0.091 -3.530 5.424 0.812 55. D68 0.066 -4.780 5.252 4.983 56. D38 0.065 -4.000 5.488 0.849 57. D42 0.056 -1.660 5.011 1.162 58. D8 0.039 -2.010 4.412 0.750

Mean 1.119 -2.844 4.938 1.128 Lowest 0.039 -11.480 3.997 0.047 Highest 2.751 7.410 6.060 4.983

Most distress Y-3

Z-score Average return

Market value

Book-to-market

No. Code Z (%) (log) times 1. D74 -0.003 -0.330 4.304 0.540 2. D64 -0.057 -2.600 4.166 3.326 3. D10 -0.192 1.070 4.640 0.411 4. D61 -0.227 -6.070 4.804 3.926 5. D93 -0.241 -6.790 4.804 0.890 6. D96 -0.244 -2.710 5.033 0.363 7. D80 -0.282 -8.640 4.575 0.220 8. D29 -0.344 -12.450 5.818 0.145 9. D15 -0.443 -8.850 4.791 1.294

10. D85 -0.514 -6.580 4.872 0.540 11. D36 -0.523 0.590 4.959 0.339 12. D33 -0.570 15.700 4.979 0.767 13. D17 -0.624 -1.790 5.063 0.289 14. D47 -0.642 -3.430 4.644 2.294 15. D78 -0.705 -0.750 4.563 0.430 16. D59 -0.798 3.990 4.477 0.181 17. D49 -0.801 2.420 4.800 0.987 18. D72 -0.809 -6.740 4.909 1.121 19. D4 -0.820 6.770 4.737 0.376 20. D34 -0.890 4.500 5.426 1.727 21. D65 -0.945 -1.800 4.714 1.061 22. D20 -0.954 -7.050 5.195 0.080 23. D48 -0.961 -7.340 4.614 2.243 24. D86 -1.056 -5.290 4.583 0.850

Appendix 8

93

Most distress Y-3 Z-score Average

return Market value

Book-to-market

No. Code Z (%) (log) times 25. D30 -1.163 1.430 4.884 2.419 26. D1 -1.280 6.720 4.802 3.026 27. D46 -1.529 -0.330 3.822 1.533 28. D82 -1.612 -2.390 4.735 0.124 29. D70 -2.064 -2.320 4.418 0.303 30. D28 -2.640 0.190 5.069 -8.107 31. D73 -3.826 -0.330 4.582 0.035

Mean -0.896 -1.652 4.767 0.766 Lowest -3.826 -12.450 3.822 -8.107

Highest -0.003 15.700 5.818 3.926