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 ˆ and ˆ
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(
ˆ
T
Tt
mmt
mmt
T
Tt
iit
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
1
ˆ
T
Tt
iti R
L

;


1
0 11
1
ˆ
T
Tt
mtm R
L

;
2
11
2
)ˆˆ(
2
1
ˆ
1
0
mt
T
Tt
iiit 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

Page 18 of 40
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

Page 19 of 40
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.

Page 20 of 40
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

Page 21 of 40
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)

Page 22 of 40
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.

Page 23 of 40
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

Page 24 of 40
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.

Page 25 of 40
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1–25

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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].

Page 29 of 40
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

Page 32 of 40
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.

Page 33 of 40
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

Page 34 of 40
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

Page 35 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

Page 36 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

Page 37 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

Page 38 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

Page 39 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

Page 40 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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