market reaction to the adoption of ifrs for insurance firms in europe

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Market Reaction to the Adoption of IFRS for

Insurance Firms in Europe

Xiaoling Chen

A dissertation submitted to Cardiff Business School

in partial fulfilment of the requirements for the

degree of:

B.S. Accounting and Finance

Cardiff University

2014

Supervisor of Dissertation: Lecturer of Dissertation:

Wissam Abdallah Svetlana Mira

Xiaoling Chen C1153541 Dissertation BS3522

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Content

ABSTRACT ............................................................................................................................... 3

CHAPTER I INTRODUCTION ................................................................................................ 4

CHAPTER II BACKGROUND AND LITERATURE REVIEW ............................................. 5

2.1 IFRS Insurance Contract Development ............................................................................ 5

2.1.1 Phase I ........................................................................................................................ 6

2.1.2 Phase II ....................................................................................................................... 6

2.2 Related Literature Review ................................................................................................ 7

2.2.1 Accounting Information Quality ................................................................................ 7

2.2.2 Accounting Information Comparability ..................................................................... 9

2.2.3 Market Reaction ........................................................................................................ 9

2.2.4 Insurance Industry .................................................................................................... 11

CHAPTER III HYPOTHESES DEVELOPMENT .................................................................. 12

CHAPTER IV DATA, METHODOLOGY AND RESAERCH DESIGN .............................. 14

4.1 Methodology and Sample Selection ............................................................................... 14

4.2 Overall Market Reaction ................................................................................................ 15

4.3 Cross-Sectional Analysis ................................................................................................ 16

CHAPTER V RESULTS ......................................................................................................... 18

5.1 Overall Market Reaction ................................................................................................ 18

5.2 Cross-Sectional Analysis ................................................................................................ 19

CHAPTER VI CONCLUSION ................................................................................................ 23

REFERENCES ......................................................................................................................... 25

APPENDIX .............................................................................................................................. 33


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ABSTRACT

This study examines market reactions to events associated with the adoption of IFRS for

European insurance firms. First, we use event study to test the overall market reaction to

events. Then we conduct cross-sectional analysis to test whether firm characteristics

explain cross-sectional variation in the market reaction. Our findings show that there is

no evidence of significant market reaction to IFRS adoption for European insurance

firms. We also find that insurance firms that are audited by Big4 audit firms have more

positive reaction to IFRS adoption.

Keywords: IFRS; insurance; Europe; event study


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CHAPTER I INTRODUCTION

The introduction of International Financial Reporting Standards (IFRS) for companies around

the world is one of the most important financial reporting changes in accounting history. At

present, more than 100 countries have adopted IFRS or implied policy to converge domestic

accounting standards with the IFRS.

This study will examine market reactions to events associated with the adoption of IFRS with

a focus on European insurance firms. In recent decades, the International Accounting

Standard Board (IASB) has been working to improve financial reporting by issuing a high

quality standard for insurance contracts and expected to make it easier for users of financial

statements to understand how insurance contracts affect an insurer’s financial position. In

2005, all firms listed on stock exchanges of European member states were required to apply

IFRS when preparing their financial statements, within which the IFRS 4 Insurance Contract

is only an interim standard, addressing some of the urgent issues such as changes in

remeasuring insurance liabilities, future investment margins and asset classification. After

conducting a wide range of consultations, IASB published two exposure drafts for Insurance

Contract in 2010 and 2013 respectively. These are the events this study will examine.

However, the reaction of investors to the convergence of financial reporting regulation is not

consistent. For example, market participants may believe that IFRS would reduce information

asymmetry between the firm and investors and, thus improve accounting information quality.

In addition, investor might expect the information comparability to increase, hence lowering

the costs of comparing firms’ financial position. Therefore, if the firms’ financial information

is more transparent, market will be more liquid and cost of capital will be lower. In this case,

investors are expected to react positively to the events.

By contrast, investors are likely to react negatively to IFRS adoption because of the principle-

based characteristic of IFRS. Compared to rule-based regulation, principle-based standards

leave much for accounting professions in implementation. This may reduce quality and

comparability of the accounting information. Also, investors might believe that the increased

contract and monitoring costs from transition would reduce firms’ cash flow.

To test and explain the impact of the events associated with IFRS Insurance Contract adoption

we carry out two sets of empirical studies. First, we use event study to measure three-day


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price movements around the publication of Exposure Draft for all insurance firms in UK,

France, Germany and Switzerland. We find that there is no evidence of significant abnormal

returns on the event days. Then we conduct cross-sectional analysis to test whether firm

characteristics explain cross-sectional variation in the market reaction. The estimators indicate

that insurance firms that are audited by one of the Big4 auditors have more positive reaction

to IFRS adoption.

This study has contributions to this field. First, it provides empirical evidences to IASB. The

new IFRS Insurance Contract will be issued in early 2015 and expect to be effective in 2018.

Before the time, IASB performed an extensive consultation and collected feedback across all

major geographic regions with representatives of the insurance industry, actuaries, auditors

and insurance supervisors. Our study could help IASB understand how investors or firms

would response to this project and make further adjustment in standard setting process.

Second, this study extends research on impact of IFRS adoption. There were researches about

introduction of IFRS in Europe as a common-set of standard (e.g. Armstrong et al. 2010) and

researches about the impact of IFRS insurance contract in specific countries, such as Turkey

(e.g. Senyigit 2012) and Poland (e.g. Klimczak 2011). However, little is known about

adoption of IFRS for insurance firms in Europe. Also, our study examines the two exposure

draft separately and compares their results, which is quite timely given that the revised

exposure draft for insurance accounting standards was issued in July 2013.

The rest of the dissertation is organized as follows. Chapter II discusses the background of

IFRS Insurance Contract development and review literatures in this field. Chapter III presents

our hypotheses. Chapter IV describes our data, methodology and research design. Chapter V

presents our test results, and Chapter VI concludes.

CHAPTER II BACKGROUND AND LITERATURE REVIEW

2.1 IFRS Insurance Contract Development

According to IFRS 4, an insurance contract is a "contract under which one party (the insurer)

accepts significant insurance risk from another party (the policyholder) by agreeing to

compensate the policyholder if a specified uncertain future event (the insured event) adversely

affects the policyholder." In 1997, IASB’s predecessor, the IASC, carried out the initial work

on an Insurance project and published an issues paper in November 1999, and then the IASB


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(formed in March 2001) took over the project in 2001. In March 2002, the European

Parliament passed a resolution requiring all firms listed on stock exchanges of European

member states to apply IFRS when preparing their financial statements for fiscal years

beginning on or after January 1, 2005. Prior to 2005, most European firms applied domestic

accounting standards. IASB realised that it was not feasible to complete the comprehensive

project before 2005. In the meantime, IASB recognised that some guidance was necessary in

time since accounting for the insurance contract under IFRS was diverse and the insurance

contract was excluded from the scopes of existing IFRS. Therefore, the IASB decided to split

the project into two phases so that some urgent issues can be addressed before 2005.

2.1.1 Phase I

Phase I of the project was completed when IFRS 4 Insurance Contracts was issued in March

2004. IFRS 4 provided limited improvement in accounting by insurers and improved urgent

issues such as disclosures on amount, timing and uncertainty of future cash flows from

insurance contracts. Nonetheless, IFRS 4 was intended only as an interim standard which

allowed insurers to continue to use various accounting practices that had developed over the

years.

2.1.2 Phase II

After the completion of the phase I, the IASB took up phase II of the project, which would

result in a new standard to replace the current IFRS 4. During the process, the Board has

performed an extensive consultation and collected feedback across all major geographic

regions with representatives of the insurance industry, actuaries, auditors and insurance

supervisors. For example, the IASB established the Insurance Working Group (IWG) to

analyse accounting issues relating to insurance contracts. The group brings together a wide

range of comments and includes senior financial executives who are involved in financial

reporting.

In July 2010 the Board issued the Exposure Draft (ED) Insurance Contracts with a four-month

comment period, ending on 30 November 2010. This is the first event we will examine. The

proposals in the ED would eliminate inconsistencies and weaknesses in existing practices. In

order to listen to the views and gain information about the proposed requirement from

interested parties, round-table meetings were held in Tokyo (Japan), London (United


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Kingdom) and Norwalk (United States) on December 2010. The IASB also conducted field

test for 15 insurance firms to test the proposals in the Exposure Draft in 2010. Through the

field test, the Board intended to understand how the proposed approach would operate in

practice and to identify where more detailed implementation guidance may be required.

The second event we will examine is IASB publishing the Revised Exposure Draft of

proposals for the accounting for Insurance Contract. Builds upon proposals published in 2010,

the revised exposure draft reflects feedback received during the extensive public consultation

period. The revised proposals introduce enhancements to the presentation and measurement of

insurance contracts as well as seek to minimise artificial accounting volatility. Hans

Hoogervorst (2013), Chairman of the IASB commented:

“We are approaching the end of this important project to bring consistency and

transparency to the accounting for Insurance contracts. The document published today

responds to concerns expressed about non-economic volatility resulting from our

previous proposals.”

Today, the IASB has been collecting feedbacks about the revised exposure draft. Then the

new IFRS Insurance Contract will be issued in early 2015 and expect to be effective in 2018.

2.2 Related Literature Review

Because the second phase of the IASB’s Insurance Project is under consideration, little is

known about how investors reacted to the IFRS adoption for insurance firms in Europe. This

study deduces investor judgment from assessing the equity market reaction to two important

events at the stage of IFRS adoption.

2.2.1 Accounting Information Quality

If the adoption of IFRS in insurance firms in Europe could improve the accounting quality, as

IASB expected, investors are likely to react positively to the movement toward IFRS adoption.

A single global set of accounting standards helps reduce information asymmetry. Also, the

principles-based nature of IFRS stimulates firms to report accounting information that better

reflects the economic substance and thus promotes greater transparency (Maines et al. 2003).

For example, Barth et al. (2008) uses three indicators, namely, earnings management, timely


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loss recognition and value relevance as the proxies for accounting quality. Firms with high

accounting quality exhibits less earnings management, more timely loss recognition, and

higher value relevance of earnings and equity book value to share price. Barth et al. (2008)

finds that firms applied International Accounting Standards (IAS), which compose a large

part of IFRS, experience an improvement in accounting quality between pre- and post-

adoption periods. Following the same proxies, Chua et al. (2012) finds an improvement to

accounting quality after Australian listed firms moved from Australian GAAP to IFRS.

Zeghal (2012) notices that the findings are more obvious for the firms in countries where the

distance between the pre-existing national GAAP and IFRS was significant. Horton et al.

(2013) confirm this argument and point out that the larger the difference between IFRS and

local GAAP the larger is the improvement in forecast accuracy. Chen et al. (2010) explain

that the reduced earnings management may due to the fact that IFRS limit management

opportunistic discretions by reducing available accounting alternatives. In addition, since

IFRS is easier to interpret and implement, it weakens the ambiguity and inconsistence of

domestic standards, which will decrease the probability that managers take advantage of

ambiguous domestic standards to manage earnings (Chen et al. 2010).

However, the findings on the effects of IFRS adoption on accounting quality are mixed in

previous studies. IFRS is a principles-based accounting standard that draws from the IASB’s

conceptual framework but lacks detailed implementation guidance, compared with rules-

based standards. As a results, the flexibility requires the accounting professional to exercise

judgment while leaves too much to interpretation and manipulation (Jermakowicz and

Mcguire 2002, Collins et al. 2012). Furthermore, IFRS may not adequately reflect regional

differences in economies, politics and culture that lead to existing differences in domestic

accounting standards. Empirically, Paananen and Lin (2008), comparing the characteristics of

accounting amounts in German companies, suggest a decrease in accounting quality during

the IFRS mandatory adoption period. They find that earnings and book value of equity are

becoming less value relevant over the last years. Similarly, employing Swedish publicly listed

firms from 2003 to 2006, Pannanen (2008) observes accounting quality decreased after IFRS

adoption in Sweden, especially for the committed adopters. Jeanjean and Stolowy (2008)

show after IFRS adoption, earnings management is not reduce in firms in Austria and UK and

even increases in France. From this point of view, investors would react negatively to IFRS

adoption in insurance firms.


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2.2.2 Accounting Information Comparability

Investors would react positively to IFRS adoption if they expect application of IFRS to result

in improved comparability of accounting information. IFRS is intended to enhance

international comparability，as comparability in financial statements is crucial for investors

to draw reasonable conclusions about the relative performance of firms (Uwadiae 2012). As a

consequence, there would be reduced cost of comparing firms’ financial information

internationally and greater consistency of financial reporting, enabling auditors and their

clients to deal with consistent accounting issues (Joos and Leung 2013). Barth (2008) claims

that the use of a common reporting language in business is an important step in making

financial reporting more comparable. Empirically, Yip and Young (2012) use three proxies

for information comparability: the similarity with which two firms translate economic events

into their financial statement, the degree of information transfer, and the similarity of the

information content of earning and of the book value of equity. Using data from 17 European

countries that adopted IFRS in 2005, they find a significant increase in the similarity facet of

cross-country comparability in the post-IFRS period. Besides, Brochet et al. (2011) measure

abnormal returns to insiders and analyst because both of them represent users who are likely

to get access to private information regarding the firm. The decrease of the abnormal return in

the UK following IFRS adoption indicates IFRS improve the comparability of financial

statements so that insiders and analysts are less likely to take advantage of private information.

They state that the increase in comparability can also arise in countries in which information

quality is already high and current domestic standards are already similar to IFRS.

In contrast, Liao et al. (2012) find that French firms’ earnings and book values are priced

differently than those of German firms in the years subsequent to mandatory IFRS reporting,

which suggests these summary accounting variables are not directly comparable between

these two large continental European countries. They explain that the accounting choices such

as depreciation expense, amortization expense, special items and other equity reserves, as well

as the patterns of earnings changes of French firms are different from the accounting choices

of German firms.

2.2.3 Market Reaction


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Contract and Monitoring Cost

The adoption of IFRS have economic consequences as changes in the rules used to calculate

accounting amounts alter the distribution of firms’ cash flows, or the wealth of parties who

use those numbers for contracting or decision making (Holthausen and Leftwich 1983).

Collins and Rozeff (1981) explore the economic reasons for the observed negative abnormal

performance of firms whose reported earnings and stockholders' equity were negatively

affected by the proposed elimination of full cost accounting in the oil and gas industry. They

suggest that explanations are driven by increased contracting and monitoring costs, which are

associated with firms’ contractual agreements, such as management compensation contracts

and lending agreements, and with firms’ political visibility. These costs place an upper bound

on the economic effect of accounting choice. Holthausen and Leftwich(1983)’s findings are

consistent with results that the increased contracting and monitoring costs and subsequent

reduced cash flow on the economic consequence of accounting standards choices.

Liquidity and Cost of Capital

If the quality and comparability of firms’ financial reporting increase after IFRS adoption, the

potential capital market consequences are lower costs of capital, increased liquidity, and

enhanced analyst and investor participation. It is expected that these capital market benefits

will lead to macroeconomic benefits such as enhanced employment, foreign direct investment

and GDP growth (Godsell and Welker 2012). Daske et al. (2008) provide early evidence on

the capital market effects of IFRS adoption reporting in 26 countries around the world. Daske

et al. (2008) find that adopters experience statistically significant increases in market liquidity

after mandatory IFRS, ranging from 3% to 6%, along with a decrease in firms’ cost of capital.

This might result from higher quality financial reporting and better disclosure that reduce

adverse selection problems in share markets and lower estimation risk. Li (2010) finds

evidence that, on average, the IFRS adoption in EU in 2005 significantly reduces the cost of

equity for adopters by 47 basis points and behind the reduction are increased disclosure and

improved information comparability.

However, some other studies find limited or no capital market benefits for adopters. Atwood

et al. (2011) find that after IFRS adoption earnings that are persistent and associated with

future cash flow are no more than earnings reported under local GAAP.


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Foreign Investment

IFRS adoption will encourage international trade in goods and foreign portfolio investment

decisions. It may help IFRS users from other countries to understand financial information,

thus reducing information asymmetries between users of financial statements in different

countries (Márquez-Ramos 2011). Amiram (2012) finds that foreign equity portfolio

investments (FPI) increase in countries that adopt IFRS. More importantly, this relation is

driven by foreign investors from countries that also use IFRS. Tan et al. (2011) separately

examine how accounting convergence affects both foreign and local financial analysts. They

find that IFRS adoption attracts foreign analysts, particularly those who are located in a

country that adopts IFRS at the same time as the firm’s country and those with prior IFRS

experience. This result can be explained by the fact that the common use of IFRS enables the

investment environment more familiar to investors so that they are willing to invest in

familiar market. Another argument is that IFRS reporting makes it less costly for investors to

compare firms across markets and countries. Thus, even if the quality of corporate reporting

does not improve, it is possible that the financial information provided becomes more useful

to investors (Daske et al. 2008). Moreover, the IFRS familiarity effect interacts with other

familiarity factors, including shared geographical region, shared spoken language and culture

to promote investments. The increased foreign investment in a country’s firms could again

enhance the liquidity of the capital markets and extend firms’ investor base, which in turn

improves risk sharing and lowers cost of capital.

2.2.4 Insurance Industry

Some industry specific characteristics of insurance firms might affect investor’s reaction to

the introduction of IFRS. It is widely accepted that the IFRS will create a serious challenge

for the European insurance industry. One of the most significant challenges in IFRS is the

movement toward fair value accounting, also known as mark-to-market accounting. Instead of

traditional historic cost accounting, fair value discloses firm’s current market value of assets.

However, given that the activities of the insurance industry are long term in nature and

insurance firms tend to diversify risk over time, the fair value accounting causes increased

volatility for insurance firms. Hence, investors are likely to require higher return to

compensate the volatility, which lead to higher cost of capital. Actually, the volatility is not

always reflects underlying economic reality. The Fitch Ratings (2004) suggests that it is vital

to make the distinction between volatility resulting from economic mismatch and from


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accounting mismatch. If the cost of capital increases, manager would face the pressure to hold

lower level of capital. Hence, their risk absorption capabilities are reduced as well. Dickinson

and Liedtke (2004) in a survey on 40 leading insurance companies reveal that none of the 40

insurance companies in the survey currently uses an internal accounting system based on full

fair value, nor would they voluntarily choose to do so. As this is an approach they did not

fully adopt, European insurers have to rebuild their accounting system when apply IFRS,

which will lose some compatibility with their historic accounting data (Mariga 2007).

However, Post et al. (2007) contrast that concerns about the effects of IFRS are overstated.

He states that what IFRS changes is the information investors receive about the insurance

business’s performance, but not the underlying economic performance of an insurer.

Therefore, they conclude that IFRS adoption has minor impact on the cost of capital. The

main area of IFRS impact on the European insurance industry is only on insurance type and

product design. Under IFRS, to pass a significant portion of investment and insurance risk to

policyholders, insurer may choose to increase premium or change product designs

substantially. Also, Senyigit (2012) finds there is no difference in Turkish insurance firms’

equity after the new standard is implemented since January 1 2008, although he admits the

project will have significant influence on insurance industry when the second stage is

completed. Klimczak (2011) finds consistent evidence from Poland. The event study in the

research shows that there is no evidence of abnormal returns either before, on, or after the

adoption of the IFRS. He suggests that the low market reaction may be explained by the

existence of an efficient market with widespread interim reporting requirements. In the

efficient market, the pre-adoption accounting information quality is high and investors are

able to access information easily and process this information efficiently, which can serve as a

substitute for more informative accounting regulations.

CHAPTER III HYPOTHESES DEVELOPMENT

Although it is possible that the new accounting requirements brought by the IFRS will cause

increased volatility in the insurance firms, proponents argue that the adoption of IFRS in

European insurance firms will reduce information asymmetry and improve familiarity to

investors as it may lead to higher accounting information quality and comparability, and thus

higher capital market liquidity and lower cost of capital. Therefore, the first hypothesis is

stated as following:


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H1: There is a positive overall market reaction to IFRS adoption in insurance firms in

Europe, all other things being equal.

A number of prior researches indicate that generally the large firms appear to show higher

levels of comparability and accounting information quality pre IFRS period (Cascino and

Joachim 2012) because they seem to attract more attention from analysts and they have more

press releases and public information available from sources other than financial statements

(Choi et al. 2013). Furthermore, large firms are more likely to operate at the international

level and to be compared with their peers. Hence, they may achieve high level of consistence

in accounting techniques choices (Joos and Leung 2013). By contrast, small firms are

assumed to have greater information asymmetry before adoption. If investors expect IFRS

adoption in European insurance firms to lead to convergence benefits, they would react more

positively to the events for small firms. Consequently, the second hypothesis is:

H2: Small insurance firms (as measured by size) will react more positively to the

announcement compared to large insurance firms.

It is assumed that the dominant auditors, Big 4 (PricewaterhouseCoopers, Deloitte, Ernst &

Young, and KPMG), provide higher auditing quality. Insurance firms audited by Big 4 would

have higher accounting information quality and comparability and hence their information

asymmetry is lower before the adoption of IFRS. Furthermore, the Big 4 auditors would

support their clients with better professional knowledge to facilitate transition, hence firms

audited by Big 4 are expected to benefit more from IFRS adoption. Therefore, the third

hypothesis is formulated as following:

H3: Insurance firms that are audited by Big 4 will react more positively to the adoption

of IFRS.

Compared with life insurance firms, the adoption of the IFRS will have more benefits to non-

life insurance firms. The Board expressed the preliminary view that a single model is

appropriate for both life and non-life insurance contracts. However, some respondents,

particularly some from the US, Bermuda and the Lloyds market, claim that there are

significant and fundamental differences between them (IASB 2008). In general, non-life

insurance firms are exposed to a greater extent of uncertainty than life insurance firms. For

instance, for life insurance the probability of insured event occurring is certain and the

amount of loss if insured event occurs is fixed and determinable, which is the face value of

policy. In contrast, non-life insurance firms may receive none or many claims for insured


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event occurring and the amount of loss is unknown. Therefore, due to significant uncertainty

and volatility, market participants may expect to access more information about non-life

insurers. If investors believe IFRS could improve accounting information quality, they would

have more positive reaction to the adoption for non-life insurers. On the other hand, life

insurers may have disadvantages under IFRS. With long term contracts and reliance in some

situations on future investment returns to gain profits, life insurance firms find it difficult to

achieve a closer matching of their assets of liabilities positions at all times if fair value

accounting is applied under IFRS (Fitch Rating 2004). Thus, the forth hypothesis is stated as

following:

H4: Non-life insurance firms will react more positively than life insurance firms to the

IFRS adoption.

CHAPTER IV DATA, METHODOLOGY AND RESAERCH DESIGN

4.1 Methodology and Sample Selection

We infer investor perceptions relating to IFRS adoption by examining European firms’ equity

return reactions to our two events. We first provide evidence on the overall European

insurance market reaction to these events and then focus our tests on determining whether

particular firm characteristics explain cross-sectional variation in insurance firms’ reaction in

a pattern consistent with our predictions.

We use event study methodology. According to MacKinlay (1997), the use of event-study

methodology requires an assumption of market efficiency hypotheses, which allows

researchers to measure the share price movement of IFRS adoption. Malkiel and Fama (1970)

defined efficient market as “a market in which prices always fully reflect available

information”. In the classical efficiency market hypotheses, he describes three level of

efficiency: weak form efficiency, semi-strong form efficiency and strong form efficiency.

Weak form efficiency: Share prices fully reflect all the information implied by all prior

price movements.

Semi-strong form efficiency: Share prices fully reflect all publicly available information

relevant to the value of the shares.

Strong form efficiency: Share prices fully reflect all knowable information i.e. investors

or groups have monopolistic access to any information relevant to the value of the

shares. (Malkiel and Fama 1970)


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In 1991, Fama revisited the efficient market hypotheses and proposes modern market

efficiency. He general defined “prices reflect information to the point where the marginal

benefits of acting on information (profits to be made) do not exceed the marginal costs.” He

emphasised the test is a joint test of market efficient efficiency and the equilibrium expected

return model.

The initial task of conducting the event study is to identify the event. As noted in the IFRS

insurance contract development, the IASB published an exposure draft of improvements to

the accounting for insurance contracts on 30 July 2010 and released the revised exposure draft

on 20 June 2013. Thus, 30 July 2010 is used as the first event date and 20 June 2013 is

defined as the second event day. We will examine the abnormal returns using a three day

event window i.e. [-1, 1]. The normal return will be estimated using a market model and an

estimation window, i.e. a period over which the parameters are estimated, of [-90,-30]. The

choice of the length of estimation window is supported by Scholtens and Dam’s (2007) study.

They conduct an event study to assess the impact of adoption of the Equator Principles for

banks on financial return. They use an estimation window of 60 days, ranging from 90 days

prior to the event till 30 days prior to the event.

Figure 1 Event Study

The sample comprises both life and non-life insurance firms for which event returns are

available for both 2 events in United Kingdom, Germany, France and Switzerland, which

produces a sample of 45 firms. We obtain daily price data between 2010 and 2013 from

Datastream. Table 1 provides a breakdown of the sample by country.

INSERT TABLE 1 ABOUT HERE

4.2 Overall Market Reaction

We use market model to estimate expected returns on event days. In market model, it assumes

for asset i in period t

Rit=αi+ßiRmt+εit where E(εit =0);var(εit)=σ2 and t=[-90,-30] Equation (1)

-1 1 -90 -30

Estimation window

Event window


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The normal returns is thus

Equation (2)

Where ̂ and ̂ are OLS estimates from Equation (1) and t= [-1, 1]

According to MacKinlay (1997), under general conditions ordinary least squares (OLS) is a

consistent estimation procedure for the market model parameters. Hence, the parameters can

be formulated as following:

1

0

1

0

1

2

1

ˆ

)ˆ)(ˆ(

ˆT

Ttmmt

mmt

T

Ttiit

i

R

RR

Equation (3)

miii ˆˆˆˆ Equation (4)

Where Rit and Rmt are the returns in period t for asset i and the market respectively; L1=T1-T0

i.e. -30-(-90) here.

1

0 11

1ˆ

T

Ttiti R

L

;

1

0 11

1ˆ

T

Ttmtm R

L

;

2

11

2 )ˆˆ(2

1ˆ

1

0

mt

T

Ttiiit RR

Li

Equation (5)

Based on the linear relationship between equity return and market return, applying market

returns on event days, we will obtain expected return or normal return for each firm.

Abnormal returns (ARit) are defined as the return for asset i in period t minus normal return

(NRit). ARit=Rit- NRit. The abnormal return observations must be aggregated in order to draw

overall inferences for the events. Cumulated abnormal returns (CARs) are calculated by

cumulating all the abnormal returns for the event window. We will provide a t-test of whether

there is a significant market reaction to the event days by testing whether CARs is

significantly different from zero.

4.3 Cross-Sectional Analysis

We base our inferences on tests of whether firm characteristics explain cross-sectional

variation in the market reaction to IFRS Insurance Contract adoption events. In order to

obtain the inferences, we estimate the following equation:

mtiiit RNR ˆˆ


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CMARi,t=β0+ β1InforQualFactori,t + β2Turnoveri,t + β3CloseHeldi,t

+β4Big4i,t +β5Codei,t +β6Non-Life + β7Yeari,t +εi,t Equation (6)

Where i denotes firm and t denotes event time

The InforQualFactor proxy reflects firm’s pre-adoption information quality, which is derived

from two variables. One variable is ADR, which is an indicator variable that equals 1 if a firm

cross-lists in the U.S. using American Depository Receipts (ADR) during the event year, and

0 other wise. The other one is Size, which is an indicator variable that equals 1 if the firm’s

prior end of year market value of equity is greater than the sample median and 0 otherwise.

We expect ADR firms to have higher accounting information quality before the adoption of

IFRS because these firms are subject to U.S. accounting reporting requirements as well and

are usually larger and attract more attention from analysts (Armstrong et al. 2010). In addition,

large firms are expected to have higher pre-adoption information quality. As a result, if

investors believe the IFRS adoption could improve accounting information quality to a greater

extent for European insurance firms with lower pre-adoption information quality, we expect

β1 is negative.

The equation (6) also contains two proxies for pre-adoption information asymmetry among

investors or between the firm and investors. The first is Turnover, which is an indicator

variable that equals 1 if the firm’s ratio of average number of daily shares traded to average

total number of shares outstanding for the year is greater than the sample median and 0

otherwise. The second proxy is CloseHeld, which is the percentage of shares held by insiders,

as provided by Worldscope through Thomson One Banker. We use data of Turnover and

CloseHeld in 2012 for observations of events in 2013. The reason is practical as the number

of common shares outstanding and the percentage of shares held by insiders in 2013 have not

yet available now. We expect that firms with larger turnover and lower insider ownership will

have less informational asymmetry. If investors expect the IFRS adoption to decrease

information asymmetry, then they will react more favourably to the events for firms with

greater pre-adoption information asymmetry. Therefore, we expect β2 is negative and β3 is

positive.

Additionally, equation (6) has two proxies for enforcement and implementation of accounting

standards. The first is Code, which is an indicator variable that equals 1 if a firm is domiciled

in a code law country and 0 otherwise (All of the sample countries except the U.K. are

classified as code law countries). Because investors consider financial reporting standards are


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less stringently enforced in code law countries (Ball et al. 2003), firms in code law countries

may have greater flexibility in the application of IFRS. Therefore, we expect β5 is negative.

Another proxy is Big4, which is an indicator variable that equals 1 if the firm’s auditor during

the fiscal year is one of the four largest accounting firms and 0 otherwise. It is found that Big4

audit firms provide higher audit quality and better support to facilitate IFRS transition. Hence,

we expectβ4 is positive.

Additionally, the proxy for the type of insurance firms is Non-Life, which is an indicator

variable that equals 1 if the firm is a non-life insurance firm, and otherwise 0. Due to the long-

term nature of life insurance firms, investors may believe the benefits to IFRS adoption are

higher for non-life insurance firms. If this is the case, we expect β6 is positive.

Finally, in order to test the potentially confounding effects of news occurring in the event year,

the equation includes Year, an indicator variable that equals 1 if the observations if locate in

year 2010, and otherwise 0.

Before performing a multivariate analysis, we first run a descriptive statistics. Then we run

the OLS regression described above and use R2 and F-test to evaluate the models.

CHAPTER V RESULTS

5.1 Overall Market Reaction


For the overall market reaction, we have information from 90 observations, ranging from -

0.15301 to 0.11531, with a mean of 0.00099 and standard deviation of 0.03428. There are

both 45 observations for each event. For the event in 2010, the CMAR ranges from -0.15301

to 0.11531, with a mean of 0.00021 and standard deviation of 0.03756. For the event in 2013,

the CMAR ranges from -0.07135 to 0.06779, with a mean of 0.00177 and standard deviation

of 0.03107. The information reveals that the investors react positively to both events and they

have greater reaction to event in 2013 than to the event in 2010 as the mean of CMAR is

larger than that in 2010. Additionally, the skewness is negative, indicating a clustering of

scores at the high end (right-hand side of a graph). The kurtosis values are positive, indicating

that the distribution is rather peaked (clustered in the centre), with long thin tails. Between


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them, the kurtosis value for event in 2013 is much closer to 0, which means the distribution is

more perfectly normal.

A one-sample t test is conducted to test whether the mean of CMAR differs from 0. Neither

the CMAR for event in 2010 (t=0.038, p=0.970) nor the CMAR for event in 2013 (t=0.382,

p=0.705) is significantly different from zero. The CMAR of observations for both events is

not significantly different from zero (t=0.274, p=0.785). Based on the statistic results, we find

that there is no significant market reaction to IFRS adoption for European insurance firm.

Therefore, the hypothesis 1 is rejected. This finding is consistent with Post et al.’s (2007)

argument that concerns about the effects of IFRS are overstated because what IFRS changes

is the information investors receive about the insurance business’s performance, but not the

underlying economic performance of an insurer. This is also supported by empirical evidence.

For example, Senyigit (2012) finds there is no difference on Turkish insurance firms’ equity

after the new standard is implemented. Also, Klimczak (2011) find there is no evidence of

abnormal returns either before, on, or after the adoption of the IFRS in Poland.

5.2 Cross-Sectional Analysis


Table 3 presents descriptive statistics for the variables used in Equation (6). Deleting

observations that have missing data for some variable, the remaining observations drop to 67.

The table reveals that 68.7 percent of the sample firms are non-life insurance firms and 83.6

percent of the firms are audited by one of the Big 4 auditing firm. An average of 46.3 percent

of firms’ outstanding shares is held by insiders. It also reveals that only 6 percent of firms

have ADR listings.

Before interpreting the output of regression, we check the assumptions of the regression. We

have not violated the multicollinearity as in collinearity statistics the smallest tolerance value

among each independent variable is 0.507, which is not less than 0.10 and the largest VIF

value is 1.924, which is well below the cut-off of 10. This is supported by the Pearson

correlation coefficient that no correlation between variables exceeds 0.7. In terms of outliers,

normality, linearity, homoscedasticity and independence of residuals, one of the ways that

these assumptions can be checked is visual detection from the Normal Probability Plot (P-P)

of the Regression Standardised Residual and the Scatterplot shown below.


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Figure 2

Figure 3

In the Normal P-P Plot, the points lie in a reasonably straight diagonal line from bottom left to

top right, suggesting no major deviations from normality. In the Scatterplot of the


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standardised residuals, the residuals are roughly rectangularly distributed, with most of the

scores concentrated in the centre. Outliers are checked by inspecting the Mahalanobis

distances. The maximum Mahalanobis distance value in the output is 22.325, which is lower

than the critical chi-square value (26.12) for 8 independent variables. In addition, in the

Casewise diagnostics, we have one case fall outside ranges. However, the maximum value for

Cook’s Distance is 0.226 (lower than 1), suggesting the case have no major problems on the

results for our model as a whole.


We take Levene’s test to test the homogeneity of variances. Table 4 reveals that most of the

significant values for Levene’s test are greater than 0.05, suggesting we have not violated the

assumption of homogeneity of variance.


Table 5 presents Pearson correlations between the variables. Consistent with our expectations,

it reveals that CMAR is significantly positively correlated with Life, Big4 and Closeheld, and

significantly negatively correlated with Turnover and Size. However, the correlations between

CMAR and Code and ADR are opposite to our expectation. In our sample, only UK is not

code law country. Although the IFRS could be more stringently enforced than in code law

countries, there are very little differences between UK GAAP (FRS/UITF/SSAP) and IFRS

(Collings 2009). For some year, the Accounting Standards Board (ASB) in the UK is working

with the IASB to converge UK GAAP with IFRS because it has always been the goals that

the UK will finally fully adopt IFRS. Thus, the IASB intended to achieve convergence of UK

standards with IFRS as quickly as possible and to minimise the burden of changes (PwC

2005). Therefore, insurance firms in UK may not benefit a lot from IFRS adoption so that

Code is positively correlated with CMAR. Furthermore, in an absence of enforcement,

accounting standards might not be appropriately applied. For example, Ball et al. (2003) find

that although Hong Kong, Malaysia, Singapore, and Thailand adopt accounting standards that

are largely related to those of common law countries, the information quality of the firms in

these countries is no better than that of code law countries. In terms of ADR, only two of our

sample firms cross-list in the U.S. using ADR during the event year and both of them are UK

firms. Hence, insufficient observations may prevent ADR to present negative correlation with

CMAR. Furthermore, Siegel (2009) critics that foreign firms are not subject to the same level

of regulatory scrutiny as applied to domestic U.S. firms. In a similar vein, Lang et al. (2006)


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show that even firms that cross-list in the U.S. are subject to U.S.GAAP, they have greater

earnings management.


Table 6 presents the regression analysis. R square measures how well the model fits the data

by indicating how much the variance in the CMAR is explained by the model. In this case,

our model explains 20.9 percent of the variance in CMAR for event 1, 24.6 percent of the

CMAR in event 2 and 14.9 percent of the CMAR for observations combined both events,

which are quite respectable. F-test evaluates the overall suitability of the model. Overall, the

model applied cannot statistically significantly predict the outcome variables for event 1

(F=1.059, p=0.415), event 2 (F=1.072, p=0.412), or combined observations (F=1.264,

p=0.280). Unstandardized beta provides values indicating the change of the dependent for

every unit change for each independent variable. For instance, the largest beta coefficient is

0.023 for Big4, indicating for every unit increase in the Big4, the predicted value of the

CMAR would increase by 0.023 unit.

For event 1, table 6 reveals that only the coefficient on Big4, β4, is significantly different from

zero, as predicted (t=1.9, p=0.068). This indicates that investors react more positively to IFRS

adoption for insurance firms that are audited by one of the Big4 auditors because they expect

these firms to have greater enforcement during transition. However, table 5 also reveals that

the coefficient on Size, β1, is negative, opposite to our expectation. As the p-value 0.986 is

large, we would not reject the null.

Table 6 also reveals that for event 2, the coefficient on Non-Life, β6, is negative and

significantly different from zero, which is inconsistent with our expectation. This indicates

that market participants reacted more positively to the IFRS adoption in 2013 for life

insurance firms than for non-life insurance firms. Besides, the coefficient on Turnover, β2,

which is positive, is also different to our expectation. This indicates firms with larger turnover

benefit more from the event in 2013. However, the coefficient is not significantly different

from zero (t=-0.298, p=0.768).

For the analysis combined observations from both event 1 and event 2, the coefficient on Big4,

β4, is significantly different from zero (t=2.214, p=0.031), consistent with results for event 1.


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This indicates that investors react more positively to IFRS adoption for insurance firms that

are audited by one of the Big 4 auditors.

Based on the cross-sectional analysis, we accept the hypothesis 3 that insurance firms that are

audited by Big 4 will react more positively to the adoption of IFRS. We reject the hypothesis

2 that smaller insurance firms will react more positively to the announcement compared to

larger insurance firms, and reject hypothesis 4 that non-life insurance firms will react more

positively than life insurance firms to the events.

CHAPTER VI CONCLUSION

This study examines market reactions to events associated with the adoption of IFRS for

European insurance firms. First, we use event study to test the overall market reaction to

events. Then we conduct cross-sectional analysis to test whether firm characteristics explain

cross-sectional variation in the market reaction.

First, we hypothesise there is a positive market reaction to IFRS adoption for European

insurance firms if the IFRS adoption reduces information asymmetry and improves

accounting quality and comparability. Second, we hypothesise smaller insurance firms will

react more positively to the introduction of IFRS as they may have great information

asymmetry pre-adoption. Third, we hypothesise insurance firms that are audited by Big 4

accounting firms will react more positively to the adoption of IFRS since Big 4 may provide

more stringent enforcement to support IFRS transition. Finally, non-life insurance firms face

more uncertainty in their insurance contracts and IFRS could offer investors more information

about insurers’ financial position. Hence, we hypothesise that non-life insurance firms will

react more positively than life insurance firms to IFRS adoption.

Our findings show that there is no evidence of significant market reaction to IFRS adoption

for European insurance firms. We also find that insurance firms that are audited by Big4 audit

firms have more positive reaction.

Of course, there are limitations to our study that we caution the readers to be aware of in

interpreting our main results. First, our 60 days’ estimation window in the event study may

not provide appropriate expected returns. Other news that is concurrently occurring during the

period may have influence in the returns for our sample firms. Further studies can extend the


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days in estimating expected returns such as one year before or one year after the event.

Second, we use eight indicators as proxies for cumulated abnormal returns while only one of

them present significant impact. Further research could examine other aspects of abnormal

returns.


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TABLE 1

Sample Composition by Country

Country No. of Life Insurance

Firms

No. of Non-life

Insurance Firms

Total No. of Firms

United Kingdom 15 9 24

France 4 1 5

Germany 6 3 9

Switzerland 6 1 7

Total 31 14 45

This table presents the sample composition by country. The sample includes all life and non-life insurance firms

in UK, France, Germany and Switzerland with returns available for both 2 events.

TABLE 2

Summary statistics for the abnormal returns

No. of Obs. Mean Median Standard

Deviation

Min Max Skewness Kurtosis

Overall

CMAR

90 0.00099 0.00277 0.03428 -0.15301 0.11531 -1.068 5.951

CMAR

in 2010

45 0.00021 0.00279 0.03756 -0.15301 0.11531 -1.345 8.421

CMAR

in 2013

45 0.00177 0.00273 0.03107 -0.07135 0.06779 -0.558 0.806

This table provides summary statistics for the abnormal returns on the event days [-1, 1]. CMAR is the firm’s

cumulative abnormal returns on event days. Estimation window is [-90,-30].


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TABLE 3

Descriptive Statistics

Variable Mean Standard Deviation N

CMAR 0.004 0.0302 67

Non-Life 0.687 0.4674 67

Big4 0.836 0.3732 67

CloseHeld 0.463 0.5024 67

Code 0.433 0.4992 67

Turnover 0.522 0.5033 67

ADR 0.060 0.2387 67

Size 0.570 0.499 67

Year2010 0.537 0.5024 67

This table provides descriptive statistics for the variables used in the cross-sectional analyses. CMAR is the

firm’s cumulative abnormal returns on event days. Non-Life is an indicator variable that equals 1 if the firm is a

non-life insurance firm, and otherwise 0. Big4 is an indicator variable that equals 1 if the firm’s auditor during

the fiscal year is one of the four largest accounting firms and 0 otherwise. CloseHeld is the percentage of shares

held by insiders. Code is an indicator variable that equals 1 if a firms is domiciled in a code law country and 0

otherwise. Turnover is an indicator variable that equals 1 if the firm’s mean daily percentage shares traded

during the year is greater than the sample median and 0 otherwise. ADR is an indicator variable that equals to 1 if

a firm cross-lists in the U.S. and 0 otherwise. Size is an indicator variable that equals 1 if the firm’s prior end of

year market value of equity is greater than the sample median and 0 otherwise. Year is an indicator variable that

equals 1 if the observations if locate in year 2010, and otherwise 0.

TABLE 4

Test of homogeneity

Variables Levene Statistic Sig.

Non-Life 1.089 0.300

Big4 0.038 0.846

Closeheld 0.658 0.420

Code 1.338 0.251

Turnover 0.261 0.611

ADR 0.539 0.465

Size 3.314 0.072

Year 2010 0.287 0.594

This table provides Levene’s test for the homogeneity of variances.


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TABLE 5

Pearson Correlations

CMAR Non-Life Big4 Closeheld Code Turnover ADR Size

Non-Life 0.080

Big4 0.246 -0.126

Closeheld 0.117 0.304 -0.316

Code 0.212 0.266 0.062 0.216

Turnover -0.155 -0.195 0.141 -0.611 -0.250

ADR 0.048 -0.373 0.112 -0.234 -0.220 0.241

Size -0.026 -0.266 0.182 -0.337 0.216 0.491 0.220

Year2010 -0.045 0.018 -0.007 0.081 0.025 0.012 -0.019 -0.086

This table provides Pearson correlations for the variables used in the cross-sectional analyses.


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TABLE 6

Cross-Sectional Analysis

Variable Event 1 Event 2 Combined

Coefficient

(t-statistic)

[p-value]

Coefficient

(t-statistic)

[p-value]

Coefficient

(t-statistic)

[p-value]

Constant

-0.024

(-1.026)

[0.314]

-0.013

(-0.542)

[0.593]

-0.021

(-1.404)

[0.166]

Non-Life

0.011

(0.846)

[0.405]

-0.024

(-1.826) ∗

[0.081]

0.003

(0.343)

[0.733]

Big4

0.028

(1.900) ∗

[0.068]

0.020

(1.123)

[0.273]

0.023

(2.214) ∗

[0.031]

Closeheld

4.632E-005

(0.152)

[0.881]

0.000

(0.678)

[0.504]

0.008

(0.796)

[0.429]

Code

0.010

(0.718)

[0.478]

0.008

(0.600)

[0.554]

0.011

(1.230)

[0.224]

Turnover

0.010

(-0.668)

[0.510]

0.005

(0.298)

[0.768]

-0.004

(-0.326)

[0.746]

ADR

0.004

(0.173)

[0.864]

0.030

(1.246)

[0.225]

0.017

(0.982)

[0.330]

Size

0.000

(0.018)

[0.986]

-0.006

(-0.472)

[0.642]

-0.004

(-0.403)

[0.689]

Year - -

-0.004

(-0.500)

[0.619]

No. of Observations 45 45 90

Firms 45 45 45

R2 0.209 0.246 0.149

F statistic 1.059 0.016 1.264

F p-value 0.415 0.412 0.280

This table provides results from cross-sectional analyses examining the market reaction for two events associated

with IFRS Insurance Contract adoption in Europe. The estimation is an OLS regression of the following form:

CMARi,t=β0+ β1InforQualFactori,t + β2Turnoveri,t + β3CloseHeldi,t

+β4Big4i,t +β5Codei,t +β6Non-Life + β7Yeari,t +εi,t


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CMAR is the firm’s cumulative abnormal returns on event days. Non-Life is an indicator variable that equals 1 if

the firm is a non-life insurance firm, and otherwise 0. Big4 is an indicator variable that equals 1 if the firm’s

auditor during the fiscal year is one of the four largest accounting firms and 0 otherwise. CloseHeld is the

percentage of shares held by insiders. Code is an indicator variable that equals 1 if a firms is domiciled in a code

law country and 0 otherwise. Turnover is an indicator variable that equals 1 if the firm’s mean daily percentage

shares traded during the year is greater than the sample median and 0 otherwise. ADR is an indicator variable

that equals to 1 if a firm cross-lists in the U.S. and 0 otherwise. Size is an indicator variable that equals 1 if the

firm’s prior end of year market value of equity is greater than the sample median and 0 otherwise. Year is an

indicator variable that equals 1 if the observations if locate in year 2010, and otherwise 0.

∗ indicates significantly different from zero at the 10% level.


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APPENDIX

SPSS regression analysis for event in 2010


Mean Std. Deviation N

AR .002337065712

874

.031016349487

125 36

Life .31 .467 36

Big4 .83 .378 36

Closeheld 32.2225 28.74263 36

code .44 .504 36

Turnover .528 .5063 36

ADR .06 .232 36

Size .53 .506 36

Correlations

AR Life Big4 Closeheld code Turnover ADR Size

Pearson Correlation

AR 1.000 .151 .338 .096 .189 -.166 .042 .031

Life .151 1.000 .135 -.202 -.229 .144 .366 .265

Big4 .338 .135 1.000 -.286 .100 .174 .108 .174

Closeheld .096 -.202 -.286 1.000 .244 -.776 -.225 -.412

code .189 -.229 .100 .244 1.000 -.162 -.217 .286

Turnover -.166 .144 .174 -.776 -.162 1.000 .229 .554

ADR .042 .366 .108 -.225 -.217 .229 1.000 .229

Size .031 .265 .174 -.412 .286 .554 .229 1.000

Sig. (1-tailed)

AR . .189 .022 .289 .135 .167 .405 .430

Life .189 . .216 .119 .089 .201 .014 .059

Big4 .022 .216 . .045 .281 .155 .264 .155

Closeheld .289 .119 .045 . .076 .000 .093 .006

code .135 .089 .281 .076 . .173 .102 .045

Turnover .167 .201 .155 .000 .173 . .089 .000

ADR .405 .014 .264 .093 .102 .089 . .089

Size .430 .059 .155 .006 .045 .000 .089 .

N

AR 36 36 36 36 36 36 36 36

Life 36 36 36 36 36 36 36 36

Big4 36 36 36 36 36 36 36 36

Closeheld 36 36 36 36 36 36 36 36

code 36 36 36 36 36 36 36 36

Turnover 36 36 36 36 36 36 36 36

ADR 36 36 36 36 36 36 36 36

Size 36 36 36 36 36 36 36 36


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Variables Entered/Removeda

Model Variables

Entered

Variables

Removed

Method

1

Size, Big4,

ADR, code,

Life, Closeheld,

Turnoverb

. Enter

a. Dependent Variable: AR

b. All requested variables entered.

Model Summary

Model R R Square Adjusted R

Square

Std. Error of the

Estimate

1 .457a .209 .012 .030835677544

271

a. Predictors: (Constant), Size, Big4, ADR, code, Life, Closeheld,

Turnover

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1

Regression .007 7 .001 1.059 .415b

Residual .027 28 .001

Total .034 35


b. Predictors: (Constant), Size, Big4, ADR, code, Life, Closeheld, Turnover

Coefficientsa

Model Unstandardized Coefficients Standardized

Coefficients

t Sig.

B Std. Error Beta

1

(Constant) -.024 .023 -1.026 .314

Life .011 .013 .165 .846 .405

Big4 .028 .015 .343 1.900 .068

Closeheld 4.632E-005 .000 .043 .152 .881

code .010 .013 .155 .718 .478

Turnover -.012 .018 -.201 -.668 .510

ADR .004 .025 .033 .173 .864

Size .000 .015 .004 .018 .986



of 40

SPSS t-test for event in 2010

One-Sample Statistics

N Mean Std. Deviation Std. Error Mean

AR 45 .000214167627

957

.037562123792

681

.005599430811

980

One-Sample Test

Test Value = 0

t df Sig. (2-tailed) Mean

Difference

95% Confidence Interval of the

Difference

Lower Upper

AR .038 44 .970 .000214167627

957

-

.011070743665

846

.011499078921

761

SPSS regression analysis for event in 2013



AR .005043742760

703

.029685534530

988 31

Life .32 .475 31

Big4 .839 .3739 31

Closeheld 28.4632 30.80415 31

code .42 .502 31

Turnover .52 .508 31

ADR .06 .250 31

Size .61 .495 31


Model Variables

Entered

Variables

Removed

Method

1

Size, code,

Big4, ADR, Life,

Turnover,

Closeheldb

. Enter




of 40

Correlations

AR Life Big4 Closeheld code Turnover ADR Size

Pearson

Correlation

AR 1.000 -.358 .132 .148 .243 -.140 .053 -.105

Life -.358 1.000 .115 -.248 -.307 .254 .381 .265

Big4 .132 .115 1.000 -.473 .017 .102 .115 .192

Closeheld .148 -.248 -.473 1.000 .418 -.601 -.241 -.256

code .243 -.307 .017 .418 1.000 -.354 -.223 .139

Turnover -.140 .254 .102 -.601 -.354 1.000 .254 .423

ADR .053 .381 .115 -.241 -.223 .254 1.000 .209

Size -.105 .265 .192 -.256 .139 .423 .209 1.000

Sig. (1-tailed)

AR . .024 .239 .213 .094 .226 .388 .287

Life .024 . .269 .089 .047 .084 .017 .075

Big4 .239 .269 . .004 .463 .293 .269 .151

Closeheld .213 .089 .004 . .010 .000 .096 .082

code .094 .047 .463 .010 . .025 .114 .229

Turnover .226 .084 .293 .000 .025 . .084 .009

ADR .388 .017 .269 .096 .114 .084 . .130

Size .287 .075 .151 .082 .229 .009 .130 .

N

AR 31 31 31 31 31 31 31 31

Life 31 31 31 31 31 31 31 31

Big4 31 31 31 31 31 31 31 31

Closeheld 31 31 31 31 31 31 31 31

code 31 31 31 31 31 31 31 31

Turnover 31 31 31 31 31 31 31 31

ADR 31 31 31 31 31 31 31 31

Size 31 31 31 31 31 31 31 31

Model Summary


Square

Std. Error of the

Estimate

1 .496a .246 .016 .029440337966

918

a. Predictors: (Constant), Size, code, Big4, ADR, Life, Turnover,

Closeheld

ANOVAa


1

Regression .007 7 .001 1.072 .412b

Residual .020 23 .001

Total .026 30


b. Predictors: (Constant), Size, code, Big4, ADR, Life, Turnover, Closeheld


of 40

Coefficientsa


Coefficients

t Sig.

B Std. Error Beta

1

(Constant) -.013 .024 -.542 .593

Life -.024 .013 -.383 -1.826 .081

Big4 .020 .018 .248 1.123 .273

Closeheld .000 .000 .192 .678 .504

code .008 .014 .139 .600 .554

Turnover .005 .015 .077 .298 .768

ADR .030 .024 .250 1.246 .225

Size -.006 .013 -.106 -.472 .642


SPSS t-test for event in 2013



AR 45 .001767455973

549

.031069386166

555

.004631550632

507

One-Sample Test

Test Value = 0

t df Sig. (2-tailed) Mean Difference 95% Confidence Interval of the Difference

Lower Upper

AR .382 44 .705 .001767455973549 -.007566820990599 .011101732937697

SPSS regression analysis for combined observations



AR .003589408824

556

.030208729757

559 67

Life .687 .4674 67

Big4 .836 .3732 67

Closeheld .463 .5024 67

code .433 .4992 67

Turnover .522 .5033 67

ADR .060 .2387 67

Size .57 .499 67

Year2010 .537 .5024 67


of 40

Correlations

AR Life Big4 Closeheld code Turnover ADR Size Year2010

Pearson

Correlation

AR 1.000 .080 .246 .117 .212 -.155 .048 -.026 -.045

Life .080 1.000 -.126 .304 .266 -.195 -.373 -.266 .018

Big4 .246 -.126 1.000 -.316 .062 .141 .112 .182 -.007

Closeheld .117 .304 -.316 1.000 .216 -.611 -.234 -.337 .081

code .212 .266 .062 .216 1.000 -.250 -.220 .216 .025

Turnover -.155 -.195 .141 -.611 -.250 1.000 .241 .491 .012

ADR .048 -.373 .112 -.234 -.220 .241 1.000 .220 -.019

Size -.026 -.266 .182 -.337 .216 .491 .220 1.000 -.086

Year2010 -.045 .018 -.007 .081 .025 .012 -.019 -.086 1.000

Sig. (1-tailed)

AR . .261 .022 .172 .043 .106 .350 .418 .359

Life .261 . .155 .006 .015 .057 .001 .015 .442

Big4 .022 .155 . .005 .309 .128 .184 .070 .477

Closeheld .172 .006 .005 . .039 .000 .028 .003 .258

code .043 .015 .309 .039 . .021 .037 .040 .420

Turnover .106 .057 .128 .000 .021 . .025 .000 .463

ADR .350 .001 .184 .028 .037 .025 . .037 .440

Size .418 .015 .070 .003 .040 .000 .037 . .245

Year2010 .359 .442 .477 .258 .420 .463 .440 .245 .

N

AR 67 67 67 67 67 67 67 67 67

Life 67 67 67 67 67 67 67 67 67

Big4 67 67 67 67 67 67 67 67 67

Closeheld 67 67 67 67 67 67 67 67 67

code 67 67 67 67 67 67 67 67 67

Turnover 67 67 67 67 67 67 67 67 67

ADR 67 67 67 67 67 67 67 67 67

Size 67 67 67 67 67 67 67 67 67

Year2010 67 67 67 67 67 67 67 67 67


Model Variables

Entered

Variables

Removed

Method

1

Year2010, Big4,

code, ADR,

Turnover, Life,

Size,

Closeheldb

. Enter




of 40

Model Summaryb


Square

Std. Error of the

Estimate

1 .385a .149 .031 .029736007146

603

a. Predictors: (Constant), Year2010, Big4, code, ADR, Turnover, Life,

Size, Closeheld

b. Dependent Variable: AR

ANOVAa


1

Regression .009 8 .001 1.264 .280b

Residual .051 58 .001

Total .060 66


b. Predictors: (Constant), Year2010, Big4, code, ADR, Turnover, Life, Size, Closeheld

Coefficientsa


Coefficients

t Sig.

B Std. Error Beta

1

(Constant) -.021 .015 -1.404 .166

Life .003 .009 .049 .343 .733

Big4 .023 .011 .287 2.214 .031

Closeheld .008 .010 .132 .796 .429

code .011 .009 .183 1.230 .224

Turnover -.004 .011 -.058 -.326 .746

ADR .017 .017 .132 .982 .330

Size -.004 .010 -.066 -.403 .689

Year2010 -.004 .007 -.062 -.500 .619


Residuals Statisticsa

Minimum Maximum Mean Std. Deviation N

Predicted Value -

.018378201872110 .023998375982046 .003589408824556 .011641161503528 67

Residual -

.090241283178329 .098159499466419 .000000000000000 .027875629363550 67

Std. Predicted

Value -1.887 1.753 .000 1.000 67

Std. Residual -3.035 3.301 .000 .937 67


of 40


SPSS t-test for combined observations



AR 90 .000990811800

753

.034283674740

608

.003613816624

690

One-Sample Test

Test Value = 0

t df Sig. (2-tailed) Mean

Difference

95% Confidence Interval of the

Difference

Lower Upper

AR .274 89 .785 .000990811800

753

-

.006189764856

426

.008171388457

932

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