ifrs and the complexity hurdle - university of illinois ifrs and the complexity hurdle nicolas...

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IFRS and the Complexity Hurdle

Nicolas Schrödl1

Christian Klein*

Chair of Accounting and Finance, University of Hohenheim, 70593 Stuttgart, Germany

Abstract

Regulators expect that the introduction of International Financial Reporting Standards

(IFRS) improves firms’ transparency and consequently benefits the capital market.

According to the literature and the enforcement panels’ reports, the standards’

introduction is associated with various operational hurdles, which are due to difficulties

in implementing and understanding the IFRS due to their complexity. Consequently, the

following question arises: Does the standards’ complexity partially impede the expected

benefits? We investigate this issue by examining the changes in market liquidity. Higher

transparency and lower information risk should increase market liquidity. We assume

that the more the regulations deviate from the former Generally Accepted Accounting

Principles (GAAP), the greater the degree of complexity that countries will experience.

The analysis of a worldwide sample finds that, owing to the introduction of IFRS, firms

from countries with little deviation have significantly higher market liquidity.

Keywords: IFRS, complexity, transparency, market liquidity, information risk

* E-mail address: [email protected].

Phone: +49 711 45922657.

Fax: +49 711 45922721.

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IFRS and the Complexity Hurdle

Abstract

Regulators expect that the introduction of International Financial Reporting Standards

(IFRS) improves firms’ transparency and consequently benefits the capital market.

According to the literature and the enforcement panels’ reports, the standards’

introduction is associated with various operational hurdles, which are due to difficulties

in implementing and understanding the IFRS due to their complexity. Consequently, the

following question arises: Does the standards’ complexity partially impede the expected

benefits? We investigate this issue by examining the changes in market liquidity. Higher

transparency and lower information risk should increase market liquidity. We assume

that the more the regulations deviate from the former Generally Accepted Accounting

Principles (GAAP), the greater the degree of complexity that countries will experience.

The analysis of a worldwide sample finds that, owing to the introduction of IFRS, firms

from countries with little deviation have significantly higher market liquidity.

Keywords: IFRS, complexity, transparency, market liquidity, information risk

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1. Introduction

Recently, the introduction of IFRS2 has become mandatory in different countries around

the world in order to provide financial statement users with information that is useful

for making economic decisions (e.g., IASB Framework; EC Regulation No.

1606/2002). Consequently, IFRS should lower information risks, optimize capital

allocation, and increase market liquidity. However, a closer look at the related literature

casts doubt on the expected capital market benefits. Early surveys and reports from

official institutions document the risks of and dissatisfaction with the increasing

complexity within international reporting standards. Overall, the studies underline the

difficulties firms and investors face when implementing the standards and analyzing the

financial statements, respectively (e.g., Fearnley and Hines, 2007; Palmrose, 2009;

CESR, 2007; FREP, 2009).

While there is extensive literature on complexity concerns, we are not aware of any

studies that provide evidence on IFRS complexity observing capital market reactions.

We therefore concentrate on different variables for market liquidity and examine

whether complexity within the standards represents a hurdle for the proper IFRS

adoption, which consequently reduces the expected capital market benefits. The

variables are Price Impact, Zero Returns, and Bid-Ask Spreads. We divided our sample

into voluntary and mandatory IFRS adopters to control for selection effects. Voluntary

adopters applied IFRS before the official mandatory adoption date, which was 1 January

2005 for our treatment firms.

The analysis faces various challenges. Since the IFRS mandate usually forces all firms

from one country to adopt IFRS at the same point in time, there are no firms with which

2 The International Financial Reporting Standards (IFRS), formerly called the International Accounting

Standards (IAS), are issued by the International Accounting Standards Board (IASB). The IAS were

issued by the IASB’s predecessor: the International Accounting Standards Committee (IASC). As the

IASB has adopted all standards issued by IASC, we will refer to these standards as the IFRS.

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to compare the findings. Consequently, the analysis demands a precise benchmark

definition. According to the finance literature, countries’ institutional features influence

the IFRS introduction’s effects (e.g., Daske et. al., 2008). We therefore chose treatment

and benchmark countries with similar enforcement regimes and similar reporting

incentives to be transparent. The selected benchmark countries did not mandate IFRS

introduction in 2005. We kept the worldwide sample constant between 2003 and 2008.

This sample selection method assures the comparability between the treatment and the

benchmark countries, guaranteeing for similar institutional characteristics.

Another challenge is measuring complexity. You and Zhang (2009) examine the US

GAAP’s complexity and use a word-count as a proxy for the complexity level. Li

(2008) and Miller (2008) also concentrate on the length of 10-K filings. Li (2008)

additionally uses the Fog Index. Filzen and Peterson (2010) observe the disclosure

length. Extant IFRS studies perform analyses based on surveys (e.g., Fearnley and

Hines, 2007). We decided to use a different approach that is more market orientated.

We therefore separated our treatment sample into two groups. One group consists of

countries with strong differences between the local GAAP and IFRS and the other one

of countries with weak differences. Compared to the weak differences group, we believe

that firms (preparers) from countries with strong differences are subject to greater

expenses to understand the standards and convert their accounting processes. As a

result, they experience a higher susceptibility to commit mistakes.

Investors (users) from countries with strong differences have to spend more in order to

absorb the additional information and to understand the changed accounting numbers as

well as (possibly) complexly transferred management assessments. They also face a

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higher risk of receiving financial statements with IFRS application errors. As a result,

they experience a high level of uncertainty and information risk.3

We conclude that countries with strong differences between the former local GAAP and

IFRS perceive the complexity in adopting IFRS to a higher degree than countries with

weaker differences. Consequently, we suppose that stronger capital market benefits

through IFRS introduction will materialize in countries with weaker differences

between the former local GAAP and IFRS.

Consistent with our expectation, we show that the complexity in the IFRS indeed

represents a hurdle and consequently reduces the expected capital market benefits. A

difference-in-differences as well as various regression analyses all document a better

development in market liquidity for the group with weak accounting discrepancies.

We find that the percentaged increase in market liquidity from the year before the IFRS

adoption to the adoption year was generally higher for the weak differences group. For

instance, mandatory adopters’ Bid-Ask Spreads indicate a percentaged increase in

market liquidity that is 14.92% higher than the strong differences group’s increase. The

improvement in Zero Returns is 19.19% stronger for voluntary and 9.56% stronger for

mandatory adopters.

The results from our regression analyses are also remarkable. The coefficients indicate

that the voluntary adopters’ Bid-Ask Spreads increased by 500 basis points between

2003 and 2008 for the group with strong accounting discrepancies while the group with

weak accounting discrepancies showed a decrease of 76 basis points, compared to a

benchmark that is unaffected by the IFRS adoption. This equates to an augmentation of

3 See Section 2.

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169.9% and a reduction of 25.9% for the group with strong and weak accounting

discrepancies, respectively. The coefficients for Price Impact and Zero Returns

underlined the better development in market liquidity for the group with weak

accounting discrepancies during this period.

Consistent with these results, both treatment samples’ market liquidity coefficients,

compared from the time before the IFRS adoption to the coefficients after the adoption,

underline the weak differences group’s advantages over the entire period. This evidence

stems from all our liquidity variables and remains so after various sensitivity analyses

and robustness checks. The results are, in general, statistically significant at the 1%

level.

This study contributes to the literature examining international financial reporting

standards’ complexity (e.g., Fearnley and Hines, 2007; You and Zhang, 2009) in that it

is the first to examine complexity during the certain period using a worldwide sample of

IFRS adopter firms. Moreover, it extends previous IFRS complexity studies since we

base our investigation on capital market data. Furthermore, we open a new methodology

to examine the topic by observing different levels of perceived complexity. The results

also contribute to studies examining the introduction of IFRS (e.g., Daske et al., 2008;

Holthausen, 2009; Armstrong et al., 2010) as it provides additional information on

prerequisites for positive capital market effects. The sample size enhances the

generalizability of our results – that the complexity in the IFRS represents a hurdle and

consequently reduces the capital market benefits expected from the IFRS introduction.

We believe that this study is the first to provide comprehensive evidence of the IFRS

complexity’s capital market effects.

Our findings are therefore of special interest to the IASB, policy-makers, and countries

planning to implement IFRS.

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In Section 2 of this paper, we review the literature. We develop the hypotheses in

Section 3 and present the data selection in Section 4. The research design is described in

Section 5, while the empirical results are presented in Section 6. The conclusion with

suggestions for future research follows in Section 7.

2. Literature Review

Our review summarizes essential findings from studies on IFRS introduction.

Subsequently, we present exemplary studies to define the term “complexity” as

examined in the literature.

Literature on the IFRS Introduction

Various studies prove the context of reporting transparency and capital market benefits

(e.g., Welker, 1995; Botosan, 1997; La Porta et al., 1998; Healy et al., 1999; Lang and

Lundholm, 2000; Botosan and Plumlee, 2002).4 Hence, if IFRS introduction achieves

higher transparency, positive capital market effects will materialize. Daske et al. (2008)

present an overview of the capital market effects through IFRS introduction. These -

and other - authors identified specific company and country characteristics as

prerequisites for positive capital market effects.

Armstrong et al. (2010), for example, supplement this overview. They examine equity

return reactions to 16 events associated with IFRS introduction in Europe. They noticed

that firms with lower-quality pre-adoption information and higher pre-adoption

information asymmetry showed positive effects. Conversely, firms from code law

countries (associated with weak enforcement regimes) showed negative reactions.

Holthausen (2009) argues that benefits will not fully realize unless the underlying

institutional and economic factors also develop and become more similar. Furthermore,

4 For a comprehensive overview of the transparency literature see Barth and Schipper (2008).

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other studies have found evidence that the global adoption of a single set of accounting

standards does not have the potential to increase accounting information’s

comparability across countries that differ economically, politically, and culturally (e.g.,

Ball et al., 2003; Lang et al., 2006). Burgstahler et al. (2006) as well as Christensen et

al. (2007) underline incentives’ importance, while Ball and Shivakumar (2005)

emphasize the key role of regulation, and Leuz et al. (2003), Christensen et al. (2011),

as well as Burgstahler et al. (2006) underline the importance of enforcement.

In short, the literature demonstrated that the main characteristics for positive capital

market effects are an institutional environment that provides firms with strong

transparency incentives and a strict enforcement regime (e.g., Daske et al., 2008).

Literature on Complexity

The IFRS introduction’s objective relies on the principles understandability, relevance,

reliability, and comparability (IASB Framework). Simultaneously, these principles

document the requirement to transfer the standards’ contents in a low-complex and

informative manner. Nevertheless, the framework targets financial statement users with

“reasonable knowledge” and advises that “relevant information should not be excluded

solely because it may be too complex or difficult for some users to understand" (IASB,

2006). Authors have questioned the interpretation of “reasonable knowledge” and the

difficulty of making complex underlying economics understandable (Barth and

Schipper, 2008). Early surveys and reports from official institutions document the

difficulties faced when introducing IFRS.

On the one hand, these difficulties arise for companies (preparers) when implementing

IFRS. According to Larson and Street’s (2004) survey, companies perceive complexity

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as a key challenge when implementing IFRS. Jermakowicz and Gornik-Tomaszewski’s

(2006) survey confirms this result and identifies the lack of implementation guidance

and uniform interpretation as challenges. Fearnley and Hines (2007) also found

evidence from UK surveys that the IFRS are overly complex. Dunne et al. (2008) have

conducted surveys, which largely lead to the same conclusion. Palmrose (2009) claims

widespread discontent with complexity and deep disenchantment with the current state

of accounting affairs. The Financial Reporting Council (FRC) warns that IFRS are

becoming increasingly complex (FRC, 2009).

Official institutions like the Financial Reporting Enforcement Panel (FREP), the

Financial Reporting Review Panel (FRRP), and the Committee of European Securities

Regulators (CESR) detected various areas where companies had not complied with the

requirements of the relevant standard or legislation (e.g., FREP, 2009; FRRP, 2009;

CESR, 2007). In their annual report, the FREP (2009) still presented an error rate of

27%.

On the other hand, these difficulties arise for investors (users) when analyzing the

financial statements. Various studies generally confirm that analysts fail to access or

ignore certain complex information which could result in an incomplete use of available

information (e.g., Hirst and Hopkins, 1998; McEwen and Hunton, 1999; Hirshleifer,

2001; Bergstresser et al., 2006; Picconi, 2006).5 Brav and Heaton (2002) reveal that

investors’ uncertainty about information structures can lead to a pattern of underreaction

that varies with the level of uncertainty. Hong and Stein (1999) also find evidence of a

relation between underreaction and complexity. You and Zhang (2009) study the

5 We also mention studies on the US GAAP, as they provide insights on the general topic of analysts’

reaction to complexity. Moreover, it is worth it to consider concurring investigations and developments

in view of the IASB’s and FASB’s conjoint improvement projects and their path of convergence to

international accounting standards.

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immediate and delayed market response to SEC EDGAR 10-K filings. They use a

word-count as a proxy for the complexity level and find that investors’ underreaction is

stronger for firms with more complex 10-K filings. Li (2008) and Miller (2008) confirm

that longer 10-K filings increase the difficulty to understand the financial information.

Daske (2005) finds lower accuracy and higher dispersion among analysts’ forecasts for

German firms which adopted IAS between 1993 and 2002. Filzen and Peterson (2010)

find out that complex accounting is exploited by managers to meet or beat analysts’

expectations when expectations are close to actual earnings.

In conclusion, we have noticed a general concern regarding a high level of complexity

within the reporting standards. Our understanding of accounting complexity, in general,

is consistent with the SEC’s (2008) view. Accordingly, it affects both preparers and

investors and can “impede effective communication… between a company and its

stakeholders”, create “inefficiencies in the marketplace”… and ”suboptimal allocation

of capital”.

3. Hypothesis Development

Regulators expect that the introduction of IFRS benefits the capital market as the

standards are supposed to reveal more information and consequently improve firms’

transparency which will result in more efficient economic decision-making (e.g.,

EFRAG, 2011). Nevertheless, the high information requirements also increase the

complexity within the standards. The literature review demonstrates that both

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companies fail to implement IFRS properly6 due to the standards’ complexity and

analysts fail to absorb complex information.

We derive that complexity increases uncertainty for financial statement users in several

ways: Financial statement preparers’ mistakes in applying IFRS can transfer

information that impairs economic decisions. Furthermore, complexity creates

information asymmetry between managers and investors, as managers can communicate

the economic substance of a transaction in a way that is difficult to understand for

financial statement users (see also Filzen and Peterson, 2010; SEC, 2008). Overall, it is

a sophisticated task to absorb the transferred information. All issues increase

information risks. Thus, our first hypothesis is:

H1: The complexity within the standards reduces the expected capital market benefits

from IFRS since complexity increases uncertainty for investors.7

We observe two groups to examine the capital market effects. Compared to countries

with weak differences, we believe that firms (preparers) from countries with strong

differences between the former local GAAP and IFRS are subject to greater expenses to

understand the standards, convert their accounting processes, and come to an agreement

with auditors on sophisticated principles-based IFRS interpretations. As a result, they

experience a higher susceptibility to commit mistakes. Investors (users) from countries

with strong differences have higher expenses to absorb the additional information

provided in the disclosures and to understand the accounting numbers and (possibly)

complexly transferred management assessments. They also face a higher risk of

6 The enforcement panels’ (as presented in Section 2) annual reports note that IFRS have not been

implemented properly in almost all countries. Unfortunately, the enforcement panels’ measures and the

presentations of the results differ between the countries. Consequently, we are not able to conclude

how well the standards are implemented in each country. 7 There is, of course, a high extent of uncertainty in the year of the adoption. We cannot separate between

the influences of first year’s (natural) adoption uncertainty and general complexity-uncertainty.

Nevertheless, we keep these influences in mind when we interpret the results in Section 6.

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receiving financial statements with IFRS application errors. As a result, they experience

a high level of uncertainty and information risk.

We conclude that countries with strong differences between the former local GAAP and

IFRS perceive the complexity in adopting IFRS to a higher extent than countries with

weaker differences. Consequently, our second hypothesis is:

H2: Stronger capital market benefits through IFRS introduction will materialize in

countries with weaker differences between the former local GAAP and IFRS.8

To measure the two clusters’ capital market effects, we focus on market liquidity.

Market liquidity comprehensively represents change due to IFRS introduction, since

higher transparency and lower information risk should optimize capital allocation and

increase trading and liquidity. We also found proof in the international finance literature

that market liquidity is an appropriate measure to capture both clusters’ reactions (e.g.,

Daske et al., 2008; Hail and Leuz, 2007).

4. Data Selection

We define two samples, a treatment and a benchmark sample, in order to observe the

capital market effects. As the countries’ institutional features influence IFRS

introduction’s effects, we need treatment and benchmark sample countries whose

features are similar and can be kept constant over the sample period. Primarily, these

features are legal enforcement and transparency incentives for companies.9 As a result,

we define the following criteria:

8 We empirically test the second hypothesis and make a conclusion from its results on the first hypothesis.

9 See Section 2.

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Criteria for the Sample Definition

(1) Enforcement: we chose the Rule of Law (Kaufmann et al., 2009) as criterion for

our sample definition.10

Higher values represent countries with stricter

enforcement regimes. We divided our possible sample countries into two

groups: countries with relatively weak and countries with relatively strong

enforcement regimes. The cut-off point is the median score.

(2) Reporting incentives to be transparent: we chose Bellver and Kaufmann’s

(2005) institutional transparency index and the average transparency scores for

the years 2003 to 2008 (http://www.transparency.org/policy_research/

surveys_indices/cpi/2009/cpi_2009_table). We also chose the second variable to

assure the index reliability.11

Higher values indicate stronger reporting

incentives for firms. We divided possible sample countries into two groups:

countries with strong incentives and countries with weak incentives. The cut-off

point is the median score.12

(3) IFRS convergence: countries converge towards IFRS differently, for example,

by adapting the local GAAP to IFRS. We therefore ignore all countries with an

official convergence strategy, with local regulators officially announcing a

gradual move towards IFRS over a predetermined time-frame. Daske et al.

(2008) provide an overview of countries with an IFRS convergence strategy.

10

There are also other scores for accounting enforcement, but they did not have sufficient data available

(e.g. Brown et al., 2009). 11

Bellver and Kaufmann’s (2005) study was a draft; it was not published. Nevertheless, in the light of

the authors’ reputations, we see no material risk concerning data reliability.

The second variable simply serves to support Bellver and Kaufmann’s (2005) index. All countries

chosen for our sample according to the transparency index also belong to the strong incentives group

according to the second variable. Both variables are closely related, as low corruption levels represent

strong governance infrastructure regarding transparency incentives. 12

Other variables, such as Leuz et al.’s (2003) earnings management score cannot be used, because data

are not available for all the sample countries.

14

We divided all possible treatment and benchmark sample countries into two groups

according to the enforcement and transparency criteria. The cut-off point is the median

value of all countries’ variables which is 0.82, 1.05, and 5.03 for Rule of Law

(Enforcement), Institutional Transparency, and CPI Index (both Transparency

Incentives), respectively. We excluded countries with values below the medians and

used the groups with strong institutional structures. Ultimately, the IFRS adoption

countries whose enforcement regimes are relatively strict and whose institutional

environments provide strong incentives for company transparency are: Denmark,

Finland, Germany, Ireland, Netherlands, Norway, Portugal, Sweden, Switzerland,

and the UK. The benchmark countries with strong institutional features are Canada,

Chile, Israel, Japan, New Zealand, and the U.S. This sample selection method

ensures the comparability between treatment countries and benchmark countries,

guaranteeing similar institutional characteristics. We believe that this approach is more

precise compared to a comprehensive global benchmark.13

We chose listed firms from these treatment countries and benchmark countries,

provided that their market capitalization was EUR 10 million or more. The market

capitalization criterion is important because some countries did not have data for

smaller firms. We selected a randomly drawn sample of 350 firms from each country if

more data were available. This approach disallows strong effects from any particular

country that might be due to country-specific regulatory changes. The examined sample

consists of 49% treatment and 51% benchmark observations. Furthermore, we allocated

firms from our treatment countries (mandatory adoption) that had not yet adopted IFRS

to the benchmark sample.14

Voluntary IFRS adopters from the benchmark countries

13

Especially owing to Daske et al.’s (2008) results, we could define the specific benchmark group.

Nevertheless, we also exceeded the benchmark sample as part of the sensitivity analysis in Section 4.2. 14

Including company year observations from the treatment countries to the benchmark sample brings the

risk that observations might be concentrated in specific countries during certain periods. We omit these

observations when conducting the sensitivity analysis (Section 4.2.).

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were dropped as well as country years if IFRS was mandated at a later point in time.15

We illustrate the sample selection process and the descriptive statistics for IFRS adopter

countries and benchmark countries in Table 1.16

Since we have selected treatment (IFRS adopters) and benchmark (non-IFRS adopters)

countries with similar institutional features, we can focus on one variable within the

treatment countries: the complexity they experienced with the IFRS application. As

concluded from Section 3, countries with stronger differences between IFRS and the

local GAAP experience complexity to a higher degree when applying IFRS.

To measure the differences between the local GAAP and the IFRS, we use Bae et al.’s

(2008) summary score, which is based on 21 key accounting dimensions. Higher

positive scores represent larger differences with the local GAAP.17

In addition, we

utilize Daske et al.’s (2008) modified score. They orthogonalized Bae et al.’s (2008)

score for accounting discrepancies with respect to fundamental country characteristics –

such as countries’ legal origins and their gross domestic product (GDP) per capita – as a

response to the concern that the variables are highly correlated and that they are

outcomes of more fundamental qualities of countries’ institutional frameworks.

Applying their score on our sample led to the same results.

Furthermore, we tested Bae et al.’s (2008) variable using the mean of Ding et al.’s

(2009) absence and divergence results per country. We divided the treatment group at

15

Israel partially mandated IFRS in 2008 and New Zealand in 2007. See http://www.iasplus.com. 16

Alternatively, we tried to form a benchmark based on company level matching and assembled a

propensity score. However, there were insufficient data for a robust score. We therefore omitted this

approach. 17

An alternative method would be to choose only countries with an official convergence strategy as one

can assume that these countries have low differences between IFRS and local GAAP. However, this

method does not account for the GAAP differences at the beginning of the convergence strategy. For

instance, Daske et al. (2008) identify a country with an official convergence strategy and strong

differences between IFRS and the local GAAP (see Table 6).

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their median into groups with weak and strong differences and, again, obtained the same

results.

Bae et al.’s (2008) score is available for the period before the IFRS adoption and not for

each year of our sample period. Thus, we face the risk of a change in the GAAP

differences not recorded in our investigation. However, through our choice of sample

criteria, we have addressed this risk. Important changes in the GAAP differences score

are not expected, as we have excluded all countries with an IFRS convergence strategy.

Based on the treatment countries’ median (10.5), we form two clusters:

1) Strong differences (over the median): Germany, Denmark, Finland, Portugal,

and Switzerland.

2) Weak differences (below the median): Ireland, The Netherlands, Norway,

Sweden, and the UK.

We use an ANOVA to test the significance of the groups’ differences. The results

indicate that the group with strong differences and that of weak differences differ

significantly (the p-value is always below 1.0%).

<insert table 1 about here>

One concern about the data is that the selected treatment countries represent all four

legal origins (La Porta et al., 1998) and, hence, might contain cross-border

heterogeneity. La Porta et al. (1998) generally classify a country based on the origin of

the initial laws it adopted. Consequently, the observations cannot account for current

revisions or changes in the laws. At the time La Porta et al. (1998) examined the legal

rules 1993-1994, the European Community was already attempting to harmonize

European laws (e.g., Andenas and Kenyon-Slade, 1993; Werlauff, 1993). Eight of our

ten selected treatment countries have joined the European Union (former: European

17

Community) largely before 2003, the start of our investigation period. Therefore, we

expect the selected countries to be legally harmonized in accordance with our

examination’s requirements. The two remaining treatment countries that have not joined

the European Union are Switzerland and Norway. We therefore conduct different

sensitivity analyses (as described in Section 6) and exclude these countries’ data from

the investigations.

5. Research Design

To measure the two clusters’ capital market effects, we focused on market liquidity.18

Market liquidity represents change due to IFRS introduction, as trading is expected to

increase due to lower information risks. We also found proof in the international finance

literature that market liquidity is an appropriate measure to capture both clusters’

reactions (e.g., Daske et al. ,2008; Hail and Leuz, 2007). We examine market liquidity

according to the following variables:

Zero Returns is the proportion of trading days with zero daily stock returns of all the

potential trading days in a given year. Illiquidity or Price Impact is a variation of the

Amihud (2002) illiquidity measure, i.e. the annual average of daily absolute stock

returns divided by the trading volume. This measure provides the price impact of each

EUR traded on the stock price. As verified by Amihud’s (2002) study, the price impact

or the return increases with illiquidity.19

Bid-Ask Spreads are the annual average of daily

quoted spreads measured at the end of each trading day by calculating the difference

between the bid price and the asking price divided by the mid-point.

18

See Section 3. 19

See also, for example, Amihud and Mendelson (1986).

18

As our investigation starts in 2003 and covers the entire period until 2008. We start in

2003 to ensure that we have sufficient data before the IFRS adoption to compare the

IFRS adopters’ variables with. We furthermore want to capture voluntary IFRS adopters

to control for self-selection effects. When we intended to extend the sample period back

to 2001 we experienced that most of the early IFRS adopters data stems from the strong

differences group.

Our sample covers four years of mandatory IFRS adoption and consequently truncates

distorting adoption effects in the first year of the mandate. It ends in 2008 due to data

availability at the time of our analysis.

The measurement period is defined from month -5 to month +7 relative to the firm’s

fiscal year-end (e.g., Hail and Leuz, 2007). Consequently, we ensure that information

from interim reports and annual reports are priced in our data.

We obtained the financial, price, and trading volume data from Bloomberg. In the event

that fiscal year-end or reporting standard data were not available, we compared the

company information from Datastream and Reuters.

To define the control variables, we followed the literature (e.g., Chordia et al., 2000;

Leuz and Verrecchia, 2000) and controlled for firm size, share turnover, and return

variability.20

We logarithmized the control variables and lagged them by one year (e.g.,

Stoll, 1978; Glosten and Milgrom, 1985). Table 2 illustrates descriptive statistics on the

variables. Another control variable is the market benchmark. It is computed as the

dependent variable’s annual mean from the benchmark sample (Daske et al., 2008).21

We therefore controlled for unobserved time-invariant company characteristics.

Furthermore, we included an indicator variable for every year, country, and industry to

20

The variables are explained in Table 2.

21 Daske et al. (2008) used US GAAP reporting, US Listing, and New Markets Listing as additional

control variables. These variables had little empirical validity as they generally did not lead to

statistically significant results. To concentrate on the main determinants, we omitted these control

variables.

19

deal with industry, year, and country fixed effects. We followed Campbell (1996)

categorizing the firms into the following industries: Petroleum, Finance/Real Estate,

Consumer durables, Basic, Food/Tobacco, Construction, Capital Goods, Transportation,

Utilities, Textiles/Trade, Service, and Leisure. We excluded values outside the 1% and

99% percentile, except for variables with natural lower and upper limits. Throughout the

tests and analyses, we ensured that the data fulfilled the necessary statistical premises.22


We conducted univariate and multiple regression analyses. In the univariate analysis,

we calculated the dependent variables’ mean values in the preadoption year and in the

IFRS adoption year, holding the sample constant over the two years. The examined

groups are voluntary and mandatory IFRS adopters with weak and strong accounting

discrepancies between IFRS and the local GAAP. We differentiated between mandatory

and voluntary IFRS adopters to examine whether the capital market reactions were

distorted by self-selection effects.

It is possible that voluntary adopters’ capital market effects cannot be attributed to IFRS

alone, as they might adopt the new standards in advance to signal superior company

characteristics. In that case, the results were only attributable to IFRS after the first

adoption years.

However, the early adoption can also be part of a new commitment to transparency.

These companies are not forced to adopt IFRS and are therefore probably more

committed to overcome complexity and implement the standards properly.

22

We excluded the year control variables from our calculation for Price Impact in respect of the statistical

premises.

20

Voluntary adopter companies first adopted IFRS before the adoption became mandatory

in their country, which was in 200523

for our treatment sample.24

Mandatory adopters

applied IFRS for the first time at fiscal year-ends on or after 31 December 2005.

We compared the means and examined two-sided t-tests to assess statistical

significance.

In our regression analysis, we calculated the ordinary least squares (OLS) coefficient

estimates throughout the firm-years. Indicator variables separated our IFRS adopters

into voluntary and mandatory adopters with weak and strong accounting discrepancies

between IFRS and the local GAAP. By using indicator variables for IFRS adopters, we

could also distinguish different periods to present the firms’ development over the

years. We assume that the influence of the natural complexity of the accounting change

(implementation costs) should diminish over the years.25

Another indicator variable labeled companies from our treatment countries between

2003 and 2004 that did not adopt IFRS during this period. We therefore could compare

the liquidity values before and after the IFRS introduction for the two groups having

strong and weak accounting discrepancies between the local GAAP and IFRS.

The variables are presented in the following regression model:

23

See Daske et al. (2008), Table 6.

24 Some firms belonged to an index that prescribed IFRS application prior to 2005. These firms comply

with the voluntary adopters criteria as the mentioned stock segments presume superior firm

characteristics and commitment to innovation and transparency. We therefore did not add these firms

to the mandatory adopter groups. 25

See Section 3, hypothesis H1.

21

itijtjit

itit

itit

ititit

itit

ititit

Controls

WDMandatoryWDMandatoryWD

VoluntaryWDVoluntaryWDVoluntary

WDAdoptionIFRSBeforeSDMandatory

SDMandatorySDVoluntarySDVoluntary

SDVoluntarySDAdoptionIFRSBeforeDepVar

)20082007(

)20062005()20082007(

)20062005()20042003(

)20082007()20062005(

)20082007()20062005(

)20042003(

1211

1098

76

54

3210

itDepVar represents the firms’ (i) dependent variables for each year (t): Zero Returns,

Price Impact, and Bid-Ask Spreads. SD and WD indicate our sample groups with strong

and weak accounting discrepancies, respectively. Dummy variables that take the value

of one or zero represent different IFRS adopter types. ijControls comprises the control

variables and fixed effects for every firm, country, and year. We examined two-sided t-

tests to assess the statistical significance and undertook various sensitivity analyses and

robustness checks.

6. Empirical Results

6.1 Results Based on Univariate Analysis

Table 3 illustrates the results from our univariate analysis. Statistical significance is

indicated at the 1%, 5%, and 10% level with ***, **, and *, respectively. The absolute

change in market liquidity in the year of the IFRS adoption (column (b)-(a)) is equated.

For three of the six variables, the improvement in market liquidity is higher for the

group with weak accounting discrepancies, compared to the group with strong

accounting discrepancies. These variables are voluntary adopters’ Bid-Ask Spreads as

well as mandatory adopters’ Bid-Ask Spreads and Price Impact. For instance,

mandatory adopters with weak accounting discrepancies decreased Bid-Ask Spreads by

57 basis points. Mandatory adopters with strong accounting discrepancies decreased

Bid-Ask Spreads by 17 basis points. This is an improvement of 22.18% for the group

22

with weak and 7.26% for the group with strong discrepancies. With regard to the

relative change in market liquidity (column (b)-(a) in %), five of the six variables show

a stronger improvement in market liquidity for the group with weak accounting

discrepancies. The one exception is the Price Impact variable for voluntary adopters.

Regarding the difference-in-differences results for the relative changes that are

statistically significant, all three variables show a stronger improvement for the group

with weak discrepancies. For example, the advantage in Zero Returns is 19.19% for

voluntary adopters and 9.56% for mandatory adopters. The Bid-Ask Spreads’ difference

for mandatory adopters is 14.92%.

For both, absolute and relative changes, only half of the results are statistically

significant. Nevertheless, these findings are overall supportive of advantages in market

liquidity for the group with weak accounting discrepancies.


6.2 Results Based on Regression Analysis

We present the results of the ordinary least squares coefficient estimates in Table 4. The

t-statistics in parentheses indicate statistical significance.

IFRS Adopters’ Development over the Years

The observation from 2003 to 2008 demonstrates that all the liquidity coefficients

developed better for the group with weak accounting discrepancies. Their market

liquidity either increased more or decreased less than the strong differences group’s

coefficients compared to the benchmark group, which is unaffected by the IFRS

adoption.

For instance, voluntary adopters’ Bid-Ask Spreads increased by 500 basis points

between 2003 and 2008 for the group with strong accounting discrepancies, compared

23

to the benchmark’s mean of 2.94%. This equates to an augmentation of 169.9%.26

At

the same time, the variables for the group with weak accounting discrepancies

decreased by 76 basis points which equates a reduction of 25.9%.

The mandatory adopter groups with strong and weak accounting discrepancies

increased by 526 (187.7%) and 131 (44.6 %) basis points, respectively, and, hence,

underlined the better development in market liquidity for the group with weak

accounting discrepancies.

The coefficients for Price Impact and Zero Returns show the same trends.27

Furthermore, they are generally statistically significant at the 1% level. The conclusion

stays unchanged when we observe the examined periods 2003 to 2004, to 2006, and to

2008, separately.

IFRS Adopters’ Development compared to the Time before IFRS

In addition, we compare the treatment samples’ market liquidity coefficients before the

IFRS adoption (see Table 4, (a)) to the coefficients after the adoption. The results

confirm the former conclusions. For example, the strong differences group’s Bid-Ask

Spreads before IFRS were 3.66%.28

The Bid-Ask Spreads for the voluntary IFRS

adopters developed from 3.31% to 4.0% and 7.94% over the measured periods 2003 to

26

We tabulate the Bid-Ask Spreads variables as

685.0189.0119.0)0294.0ln( e =0.0794, 0.0794-

0.0294=0.0500 for voluntary adopters with strong accounting discrepancies, and as 130.0230.0200.0)0294.0ln( e =0.0218, 0.0218–0.0294=-0.0076 for voluntary adopters with weak

accounting discrepancies. The value 0.02942 represents the benchmark’s mean for the Bid-Ask Spread

variable. The other values are the Bid-Ask Spreads coefficients presented in Table 4. The mandatory

adopters with strong and weak accounting discrepancies are calculated as 770.0255.0)0294.0ln( e =0.0820;

0.0820-0.0294=0.0526 and 416.0047.0)0294.0ln( e =0.0425; 0.0425-0.0294=0.0131, respectively.

27 The benchmark’s means for Price Impact and the Proportion of Zero Returns to tabulate the variables

are 1.26 and 7.67%, respectively. 28

Calculated as 218.0)0294.0ln( e =0.0366.

24

2004, 2005 to 2006, and 2007 to 2008, respectively.29

This increase of 428 basis points

is a deterioration of 117.1% compared to the coefficient before IFRS.

The weak differences group’s Bid-Ask Spreads before IFRS were 2.64%. The Bid-Ask

Spreads for the voluntary IFRS adopters developed from 2.41% to 1.91% and 2.18%

over the measured periods 2003 to 2004, to 2006, and to 2008, respectively. This

decrease of 46 basis points is an improvement of 17.5% compared to the coefficient

before IFRS.

Mandatory adopters with weak differences also developed better than the strong

differences group. Over the entire period, the weak differences group’s Bid-Ask Spreads

increased by 161 basis points (deterioration of 61.1%) while the strong differences

group’s increased by 454 basis points (deterioration of 124.1%).

The Price Impact coefficients for the weak differences group’s voluntary and

mandatory adopters also showed wide advantages over the entire period.

The weak (strong) differences group’s Proportion of Zero Returns showed an

improvement of 3.5% (2.4%) for voluntary adopters.30

Mandatory adopters’

Proportion of Zero Returns decreased from 7.45% to 7.34% (improvement of 11 basis

points or 1.5%) for the group with weak differences and increased from 7.56% to 7.60%

(deterioration of 4 basis points or 0.5%) for the group with strong differences.


In Section 3, we developed the hypothesis that the expected positive capital market

effects through the IFRS introduction’s higher transparency cannot prevail if analysts

fail to interpret financial statements correctly and perceive uncertainty and information

risks. We identified the IFRS’ complexity as a possible cause of uncertainty. A variable

29

Calculated as 119.0)0294.0ln( e =0.0331,

189.0119.0)0294.0ln( e =0.0400, 685.0189.0119.0)0294.0ln( e =0.0794.

30 From 7.45% to 7.19% and from 7.56% to 7.38%, respectively. 7.45% (7.56%) is the Proportion of Zero

Returns before IFRS for the weak (strong) differences group.

25

that accounts for discrepancies between the local GAAP and the IFRS measures the

degree of complexity that IFRS adopters and financial statement users experience. The

outcomes from our univariate and regression analyses clearly demonstrate the higher

market liquidity of firms from countries with weaker differences between the local

GAAP and IFRS and support our hypothesis of the complexity hurdle. Furthermore, the

effects did not diminish over the years as one could expect, given that there is an

additional natural uncertainty in the year of the adoption.31

No significant deviations appeared between the voluntary and mandatory IFRS adopter

groups. Both groups experience the complexity hurdle. The coefficient estimates as well

as the control variables are mainly significant at the 1% level.

We then examined our results by performing various sensitivity analyses. We excluded

observations from specific countries to control for other country characteristics that

could affect firm liquidity. When we remove Portugal,32

we find that the average

enforcement and transparency scores are higher for the strong differences group.

Nevertheless previous papers’ results about IFRS being more beneficial for firms in

countries with strong enforcement and transparency, the advantages for the weak

differences group persist. This result articulately strengthens the relation between

market liquidity and the country of origin. We find the same results when we also

exclude Sweden.33

In addition, we varied the benchmark definitions, tested our regressions’ robustness by

applying a random effect, and omitted the observations for the voluntary adopter years

2003 and 2004 to start the investigation at the time of the IFRS mandate. The main

31

See Section 3, hypothesis H1. 32

We chose Portugal since it belongs to the strong differences group and demonstrates relatively weak

transparency and enforcement values. 33

We chose Sweden since it belongs to the weak differences group and demonstrates relatively strong

transparency and enforcement values.

26

assertion - that IFRS adopters with weak accounting differences between the local

GAAP and IFRS experience a better development in market liquidity than IFRS

adopters with strong accounting differences - was usually confirmed. However,

statistical significance varies.

Furthermore, we excluded the benchmark observations and calculated a regression only

between the strong and weak differences treatment groups. We therefore demonstrated

that the differences in the coefficients are statistically significant, not only against the

benchmark countries but also between the two adopter groups. We illustrate some of the

sensitivity analyses in Table 5.


In contrast to our results, Daske et al. (2008) concluded that firms from countries with

strong accounting discrepancies between IFRS and the local GAAP profit more from

the IFRS adoption compared to firms with weak discrepancies. Their cross-sectional

analysis ends in 2005. During the first years of our regression analysis, comparisons in

market liquidity between the time before and after IFRS adoption also partially resulted

in advantages for the group with strong accounting differences. However, over the

entire period, the advantages clearly prevailed for the group with weak accounting

discrepancies. We assume Daske et al.’s (2008) different conclusions are due to the

different observation period.

Although our study’s focus is on complexity, the coefficients from the regression

analysis present an interesting development, which is worth it to be mentioned.

Compared to the benchmark, which is unaffected by the IFRS introduction, the weak

differences group’s coefficients for Bid-Ask Spreads and Price Impact generally show

higher market liquidity as a consequence of the IFRS introduction only until 2006. IFRS

adopters’ progress decreased between 2006 and 2008, resulting in even lower liquidity

27

values than those of the benchmark firms. This result was not expected at all, as

adoption and analysing difficulties were supposed to be reduced after the early IFRS

adoption years.34

We deduce that companies’ and investors’ difficulties in implementing IFRS and

analyzing the financial statements, respectively, increased over the years.35

Consequently, the information risk due to complexity increased.

This evidence questions IFRS’ long-term benefits in general as well as previous studies’

early results of capital market benefits. The latter were possibly influenced by

introduction effects rather than the IFRS adoption itself. These effects can emerge

through misinterpretations or investors’ expectations and enthusiasm. This field is open

to future research.

7. Conclusion

In this paper, we examined the IFRS introduction’s effects in the context of the

standards’ complexity. We formed two groups of countries that experienced the

complexity differently. The outcomes from our univariate and regression analyses

clearly demonstrate that the complexity in IFRS represents a hurdle and consequently

reduces the capital market benefits expected from the IFRS introduction. Countries that

experienced the complexity to a lesser extent, showed a stronger percentaged increase in

market liquidity in the year of the adoption and a better development in market liquidity

over the years – calculated against an unaffected benchmark or against the liquidity

coefficients before the adoption.

34

Performing our sensitivity analysis, we also find various scenarios for a decrease of the Zero Returns

coefficients between 2006 and 2008. Nevertheless, the weak differences group always maintains its

advance against the benchmark. 35

This interpretation is consistent with FREP’s (2009) report of an increase in the adoption error rate

from 26% to 27% and various studies’ view that IFRS are becoming increasingly complex (see Section

2).

28

Hurdles for capital market benefits can arise as complexity of IFRS increases

uncertainty and information risk for financial statement users.36

Our results underline the importance of current efforts to make the IFRS more

understandable. The latest FREP (2011) report that presented an IFRS-application error

rate of 27% confirms the significance. Future research could explore how the efforts

achieve success – how firms and analysts will process simplifications and how the

capital markets react.

A long-term observation on the capital market concerning IFRS adopters is another

fertile field for future research.

36

See Section 3.

29

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Table 1. Sample Selection

Panel A: Selection Process

Country Enforcement

Rule of Law

Incentives

Institutional

Transparenc.

Incentives

CPI Index

Man

dat.

IFRS

Conv

erge

nce

GAAP

Diff.

(1)

GAPP

Diff.

(2)

1/0

strong/weak

1/0

strong/weak

1/0

strong/weak

1/0

yes/

no

1/0

yes/

no

Argentina -0.58 0 0.81 0 2.78 0 0 0 14 n.a.

Australia 1.77 1 2.2 1 8.71 1 1 1 4 -0.4

Austria 1.84 1 0.9 0 8.20 1 1 0 12 2.5

Belgium 1.44 1 1.02 0 7.30 1 1 0 13 1.4

Bermuda 1.04 1 n.a. n.a n.a. n.a 0 0 n.a. n.a.

Brazil -0.35 0 1 0 3.69 0 0 0 11 n.a.

Canada 1.77 1 2.4 1 8.65 1 0 0 5 n.a.

Chile 1.22 1 2.38 1 7.19 1 0 0 13 n.a.

China -0.41 0 0.34 0 3.44 0 0 0 9 n.a.

Colombia -0.74 0 0.64 0 3.79 0 0 0 n.a n.a.

Czech Rep. 0.77 0 1.02 0 4.43 0 1 0 14 0.6

Denmark 1.9 1 1.89 1 9.44 1 1 0 11 0.1

Egypt -0.04 0 -0.47 0 3.14 0 0 0 9 n.a.

Finland 1.9 1 1.7 1 9.45 1 1 0 15 4.4

Germany 1.71 1 1.47 1 7.89 1 1 0 11 1.5

Greece 0.76 0 0.21 0 4.33 0 1 0 17 6.1

Hong Kong 1.35 1 1 0 8.18 1 1 1 3 -1.5

Hungary 0.81 0 0.86 0 5.03 0 1 0 13 -0.3

India 0.13 0 0.72 0 3.10 0 0 0 8 n.a.

Indonesia -0.81 0 0.35 0 2.26 0 0 0 4 n.a.

Ireland 1.63 1 1.67 1 7.49 1 1 0 1 -3.3

Israel 0.84 1 1.47 1 6.39 1 0 0 6 n.a.

Italy 0.6 0 1.31 1 4.94 0 1 0 12 0.7

Japan 1.33 1 1.48 1 7.30 1 0 0 9 n.a.

Korea (S) 0.8 0 1.36 1 4.95 0 0 0 6 n.a.

Luxembourg 1.89 1 0.7 0 8.51 1 1 0 18 6.0

Malaysia 0.49 0 0.63 0 4.99 0 0 0 8 n.a.

Mexico -0.45 0 1.6 1 3.50 0 0 0 1 n.a.

Morocco -0.03 0 -0.22 0 3.36 0 0 0 n.a. n.a.

36

Table 1. (continued)

Panel A: Selection Process

Country Enforcement

Rule of Law

Incentives

Institutional

Transparenc.

Incentives

CPI Index

Man

dat.

IFRS

Conv

erge

nce

GAAP

Diff.

(1)

GAPP

Diff.

(2)

1/0

strong/weak

1/0

strong/weak

1/0

strong/weak

1/0

yes/

no

1/0

yes/

no

Netherlands 1.74 1 1.75 1 8.84 1 1 0 4 -7.6

New Zealand 1.84 1 1.88 1 9.49 1 0 0 3 n.a.

Norway 1.93 1 1.44 1 8.64 1 1 0 7 -3.8

Pakistan -0.87 0 0.23 0 2.35 0 0 0 4 n.a.

Peru -0.66 0 0.73 0 3.60 0 0 0 1 n.a.

Philippines -0.51 0 1.41 1 2.49 0 1 1 10 1.1

Poland 0.45 0 1.09 1 4.00 0 1 0 12 -0.9

Portugal 1.11 1 1.47 1 6.34 1 1 0 13 2.2

Russia -0.92 0 0.09 0 2.46 0 0 0 16 n.a.

Singapore 1.66 1 1.85 1 9.31 1 1 1 0 -4.5

South Africa 0.12 0 0.38 0 4.70 0 1 1 0 -3.1

Spain 1.19 1 1.05 0 6.78 1 1 0 16 4.9

Sri Lanka 0.04 0 0.43 0 3.30 0 0 0 n.a. n.a.

Sweden 1.86 1 1.91 1 9.25 1 1 0 10 -0.7

Switzerland 1.94 1 1.41 1 8.95 1 1 0 12 2.2

Taiwan 0.82 0 1.23 1 5.71 1 0 0 6 n.a.

Thailand 0.11 0 0.72 0 3.46 0 0 0 4 n.a.

Turkey 0.02 0 0.86 0 3.74 0 0 0 14 n.a.

UK 1.7 1 2.36 1 8.38 1 1 0 1 -3.4

USA 1.55 1 2.78 1 7.45 1 0 0 4 n.a.

Venezuela -1.26 0 -0.43 0 2.20 0 1 0 5 -4.9

Median 0.82 1.05 5.03 10.5* -0.3*

* only based on the treatment countries (bold)

37


Panel B: Descriptive Statistics on the Selected Countries

Country

Number of

Firms

Number of

Observations

Before IFRS

Adoption

Voluntary

Adopters

Mandatory

Adopters

Treatment Countries

Strong Differences

Germany 350 3,917 503 2,803 611

Denmark 163 2,058 419 1,094 545

Finland 120 1,878 336 1,282 260

Portugal 69 728 132 498 98

Switzerland 301 2,764 448 2,073 243

Total 1,003 11,345 1,838 7,750 1,757

Weak Differences

Ireland 188 667 150 218 299

Netherlands 138 1,487 65 1,138 284

Norway 187 2,158 330 1,142 686

Sweden 299 3,964 645 2,730 589

UK 350 3,406 334 1,458 1,614

Total 1,162 11,682 1,524 6,686 3,472

Total 2,165 23,027 3,362 14,436 5,229

Benchmark Countries

Canada 350 3,497 n.a. n.a. n.a.

Chile 164 1,901 n.a. n.a. n.a.

Israel 350 1,859 n.a. n.a. n.a.

Japan 350 4,277 n.a. n.a. n.a.

New Zealand 94 492 n.a. n.a. n.a.

U.S. 350 4,933 n.a. n.a. n.a.

Total 1,658 16,959 n.a. n.a. n.a.

Panel A illustrates the potential sample countries that were chosen according to various criteria.

Rule of Law (Kaufmann et al. 2009) represents the countries’ enforcement. Higher values stand

for countries with stricter enforcement regimes. The values in the column summarize the sample

period’s average Rule of Law values. The values in the first and second Incentives columns

represent firms’ reporting incentives according to Bellver and Kaufmann 2005 and average

transparency scores from 2003 to 2008 (http://www.transparency.org/

policy_research/surveys_indices/cpi/2009/cpi_2009_table), respectively. Higher values indicate

stronger incentives. We divided our possible sample countries into two groups: countries with

strong (1) and weak (0) enforcement and transparency incentives. The cut-off point is the

median value. Mandatory IFRS Adoption information derives from Daske et al. 2008. The

38

Convergence column indicates that none of the sample countries follows an official IFRS

convergence strategy during the sample period (Daske et al. 2008). The convergence strategy is

defined as local regulators’ official announcement of a gradual move towards IFRS over a

predetermined time-frame.

GAAP Difference 1 and 2 measure the differences between the local GAAP and IFRS according

to Bae et al. 2008 and a modified version from Daske et al. 2008, respectively. Higher positive

scores represent stronger differences with the local GAAP. We separate the treatment group at

the median into groups with weak and strong differences.

Panel B illustrates the selected countries’ descriptive statistics. We defined “Voluntary

Adopters” as firms that adopted IFRS before the adoption became mandatory. “Before IFRS

Adoption” values stemmed from the same period. “Mandatory Adopters” applied IFRS for the

first time on the fiscal year-ends on or after 31 December 2005. We selected a randomly-drawn

sample of up to 350 firms from each country to avoid distortion due to specific countries having

more data available. Our sample consists of 49% treatment (14,436 + 5,229) and 51%

benchmark (16,959 + 3,362) observations.

39

Table 2. Descriptive Statistics on the Variables

N Mean Median Std.

Dev. P1 P25 P75 P99

Dependent Variables

Zero

Returns 15,355 10.89% 1.59% 19.66% 0% 0% 11.55% 88.84%

Price

Impact 14,698 6.83 0.07 46.61 0.00 0.01 0.72 149.11

Bid-Ask

Spreads 13,989 3.71% 1.47% 8.63% 0.06% 0.54% 3.80% 35.86%

Control Variables

Market

Value 15,355 2,131.04 216.32 8,900.94 2.18 45.85 1,027.18 36,584.1

Share

Turnover 15,355 1.74 0.54 12.44 0.01 0.21 1.21 9.46

Return

Variability 15,355 0.027 0.023 0.019 0.008 0.016 0.033 0.091

Table 2 illustrates the variables’ descriptive statistics before scanning the dataset for possible

outliers. We used three dependent variables: (1) Zero Returns is the proportion of trading days

with zero daily stock returns out of all potential trading days in a given year. (2) Price Impact is

a variation of the Amihud 2002 illiquidity measure, i.e. the annual average of daily absolute

stock returns divided by the trading volume (we multiplied the coefficient by 100,000 for

expositional purposes). (3) Bid-Ask Spreads are the annual average of daily quoted spreads

measured at the end of every trading day by calculating the difference between the bid price and

the ask price divided by the mid-point.

We defined the following control variables: Market Value is the stock price (in EUR) multiplied

by the number of shares outstanding. We calculated Share Turnover as the annual EUR trading

volume divided by the market value of outstanding equity. Return Variability is the annual

standard deviation of daily stock returns.

40

Table 3. Univariate Analysis

Zero Returns

Voluntary Adopters Mandatory Adopters

PRE

(a)

POST

(b)

(b)-(a) (b)-(a)

in %

PRE

(a)

POST

(b)

(b)-(a) (b)-(a)

in %

Weak

Diff. (i)

10.07%

N=238

7.24%

N=238

-2.83%

*

-32.98%

*

6.95%

N=242

5.80%

N=242 -1.15% -18.11%

Strong

Diff. (ii)

22.87%

N=267

19.92%

N=267

-2.95%

** -13.80%

22.45%

N=140

20.61%

N=140 -1.84% -8.54%

(i) – (ii) -12.80%

***

-12.68%

***

0.12% -19.19%

***

-15.50%

***

-14.82%

***

0.69% -9.56%

*

Bid-Ask Spreads


PRE

(a)

POST

(b)

(b)-(a) (b)-(a)

in %

PRE

(a)

POST

(b)

(b)-(a) (b)-(a)

in %

Weak

Diff. (i)

1.91%

N=217

1.30%

N=217

-0.61%

***

-38.47%

***

2.89%

N=205

2.31%

N=205

-0.57%

**

-22.18%

*

Strong

Diff. (ii)

2.37%

N=247

1.77%

N=247

-0.60%

***

-29.26%

***

2.39%

N=132

2.23%

N=132

-0.17% -7.26%

(i) – (ii) -0.46%

***

-0.47%

***

-0.01% -8.9% 0.50%* 0.08% -0.40%

**

-14.92%

***

Price Impact


PRE

(a)

POST

(b)

(b)-(a) (b)-(a)

in %

PRE

(a)

POST

(b)

(b)-(a) (b)-(a)

in %

Weak

Diff. (i)

0.267

N=232

0.185

N=232

-0.082

**

-36.73%

*

0.351

N=222

0.262

N=222

-0.089 -29.38%

Strong

Diff. (ii)

0.554

N=199

0.339

N=199

-0.215

***

-49.00%

**

0.492

N=93

0.475

N=93

-0.016 -3.40%

(i) – (ii) -0.287

***

-0.154

***

0.133

**

12.27% -0.141 -0.213

**

-0.073 -25.98%

41


The table illustrates the dependent variables’ mean values in the preadoption year (a) and in the

IFRS adoption year (b) for voluntary and mandatory IFRS adopters with weak and strong

accounting discrepancies between the IFRS and the local GAAP. See Table 1 for the sample

selection process. We defined voluntary adopters as firms that adopted IFRS before the

adoption became mandatory. Mandatory adopters applied IFRS for the first time on the fiscal

year-ends on or after 31 December 2005. Moreover, we indicate the absolute ((b) – (a)) and

relative change ((b)-(a) in %) through the IFRS adoption for the different groups as well as the

number of observations. We mark statistical significance at the 1%, 5% and 10% levels with

***, **, and *, respectively, based on two-sided t-tests. We used three dependent variables: (1)

Zero Returns is the proportion of trading days with zero daily stock returns out of all potential

trading days in a given year. (2) Price Impact is a variation of the Amihud 2002 illiquidity

measure, i.e. the annual average of daily absolute stock returns divided by the trading volume

(we multiplied the coefficient by 100,000 for expositional purposes). (3) Bid-Ask Spreads are

the annual average of daily quoted spreads measured at the end of each trading day by

calculating the difference between the bid price and the asking price divided by the mid-point.

We obtained the financial, price, and trading volume data from Bloomberg. In the event that

fiscal year-end or reporting standard data were not available, we compared the company

information from Datastream and Reuters.

42

Table 4. Regression Analysis

Bid-Ask Spreads (log)

Proportion of Zero

Returns Price Impact (log)

Independent

Variables

IFRS Adopters Strong Weak Strong Weak Strong Weak

(a) Before IFRS

Adoption

0.218***

(5.621)

-0.108***

(-2.717)

-0.111***

(-14.660)

-0.221***

(-29.111)

0.526***

(6.596)

-0.441***

(-5.794)

(b) Voluntary

2003-2004

0.119***

(3.186)

-0.200***

(-4.781)

-0.119***

(-16.783)

-0.180***

(-22.066)

0.512***

(6.737)

-0.392***

(-4.820)

(c) Voluntary

2005-2006

0.189***

(5.583)

-0.230***

(-6.751)

-0.088***

(-13.718)

-0.148***

(-22.665)

0.649***

(9.217)

-0.230***

(-3.449)

(d) Voluntary

2007-2008

0.685***

(18.757)

0.130***

(3.571)

-0.089***

(-14.008)

-0.154***

(-23.796)

1.896***

(27.233)

0.868***

(13.140)

(e) Mandatory

2005-2006

0.255***

(4.842)

-0.047

(-1.011)

-0.032***

(-3.111)

-0.163***

(-18.301)

0.518***

(3.907)

-0.122

(-1.258)

(f) Mandatory

2007-2008

0.770***

(16.370)

0.416**

(9.961)

-0.038***

(-4.420)

-0.172***

(-23.192)

1.958***

(18.883)

0.989***

(12.587)

Control

Variables

Market Value t-1

(log)

-0.372***

(-98.943)

-0.033***

(-46.516)

-0.860***

(-105.303)

Share Turnover

t-1 (log)

-0.284***

(-54.327)

-0.039***

(-37.174)

-0.924***

(-69.147)

Return

Variability t-1

(log)

0.242***

(15.818)

-0.009***

(-2.876)

-0.337***

(-9.558)

Market

Benchmark

0.162***

(4.752)

0.521***

(6.007)

0.359***

(7.997)

R square 0.75 0.49 0.77

Number of

observations

13,381 14,588 12,682

Number of

unique firms

3,064 3,188 2,952

43


The table illustrates the regression analyses’ results regarding the different market liquidity

variables between 2003 and 2008. Zero Returns is the proportion of trading days with zero daily

stock returns of all the potential trading days in a given year. Price Impact is a variation of the

Amihud 2002 illiquidity measure, i.e. the annual average of daily absolute stock returns divided

by the trading volume. This measure gives the price impact of each EUR traded on the stock

price. As verified by Amihud’s 2002 study, the price impact or the return increases with

illiquidity. Bid-Ask Spreads are the annual average of daily quoted spreads measured at the end

of each trading day by calculating the difference between the bid price and the asking price

divided by the mid-point. We defined voluntary adopters ((b) to (d)) as firms that first adopted

IFRS before the adoption became mandatory. Mandatory ((e) to (f)) adopters applied IFRS for

the first time on the fiscal year-ends on or after 31 December 2005. We present the summarized

results of various years to calculate with two-year periods. Within the voluntary and mandatory

adopter groups, we distinguish between IFRS adopters with weak and strong accounting

discrepancies between IFRS and the local GAAP. See Table 1 for the sample selection process.

Before IFRS Adoption (a) represents firms from our treatment countries between 2003 and 2004

that did not adopt IFRS during this period.

We defined the following control variables: Market Value is the stock price (in EUR) multiplied

by the number of shares outstanding. We calculated Share Turnover as the annual EUR trading

volume divided by the market value of outstanding equity. Return Variability is the annual

standard deviation of daily stock returns. We lagged these variables by one year. Market

Benchmark is defined as the annual mean of the dependent variable from observations in the

benchmark countries. Where indicated, we used the natural log of the raw values for the

variables. We also included fixed effects, as described in Section 5.

Statistical significance is indicated at the 1%, 5%, and 10% levels with ***, **, and *,

respectively (t-statistics in parentheses). We obtained the financial, price, and trading volume

data from Bloomberg. In the event that fiscal year-end or reporting standard data were not

available, we compared the company information from Datastream and Reuters

44

Table 5. Sensitivity Analysis

Panel A: Sensitivity Analysis without Observations from Portugal


Proportion of Zero


Independent

Variables


(a) Before IFRS

Adoption

-0.099*

(-1.624)

-0.460***

(-7.533)

-0.092***

(-13.244)

-0.201***

(-29.155)

0.430***

(4.147)

-0.542***

(-5.564)

(b) Voluntary

2003-2004

-0.282***

(-4.818)

-0.712***

(-11.224)

-0.125***

(-20.144)

-0.179***

(-25.003)

0.274***

(2.774)

-0.680***

(-6.783)

(c) Voluntary

2005-2006

-0.424***

(-7.679)

-0.855***

(-15.751)

-0.110***

(-19.383)

-0.165***

(-28.565)

0.416***

(4.490)

-0.452***

(-5.206)

(d) Voluntary

2007-2008

0.706***

(12.815)

0.131***

(2.448)

-0.098***

(-17.255)

-0.162***

(-27.885)

1.602***

(17.460)

0.586***

(6.797)

(e) Mandatory

2005-2006

-0.281***

(-3.622)

-0.695***

(-10.228)

-0.082***

(-9.731)

-0.177***

(-23.964)

0.329**

(2.269)

-0.413***

(-3.728)

(f) Mandatory

2007-2008

0.854***

(12.815)

0.476***

(7.963)

-0.043***

(-5.736)

-0.158***

(-24.323)

1.783***

(15.029)

0.773***

(8.038)

Control

Variables

Market Value t-1

(log)

-0.310***

(-69.340)

-0.025***

(-45.590)

-0.754***

(-103.607)

Share Turnover

t-1 (log)

-0.277***

(-41.761)

-0.033***

(-38.268)

-0.903***

(-73.610)

Return

Variability t-1

(log)

-0.081***

(-8.348)

-0.024***

(-20.480)

-0.321***

(-19.436)

Market

Benchmark

0.746***

(20.189)

1.248

(1.262)

0.495***

(11.803)

R square 0.563 0.429 0.71

Number of

observations

13,142 14,316 12,465

Number of

unique firms

3,002 3,129 2,887

45


Panel B: Sensitivity Analysis without Benchmark Observations


Proportion of Zero


Independent

Variables


(a) Before IFRS

Adoption

0.453***

(11.654)

n.a. 0.100***

(12.360)

n.a. 0.254***

(2.789)

n.a.

(b) Voluntary

2003-2004

0.333***

(8.997)

n.a. 0.066***

(9.192)

n.a. 0.292***

(3.395)

n.a.

(c) Voluntary

2005-2006

0.422***

(12.558)

n.a. 0.057***

(8.760)

n.a. 0.442***

(5.636)

n.a.

(d) Voluntary

2007-2008

0.484***

(15.065)

n.a. 0.058***

(9.096)

n.a. 1.699***

(21.628)

n.a.

(e) Mandatory

2005-2006

0.504***

(9.963)

n.a. 0.090***

(9.748)

n.a. 0.302**

(2.132)

n.a.

(f) Mandatory

2007-2008

0.530***

(12.843)

n.a. 0.114***

(13.985)

n.a. 1.708***

(15.087)

n.a.

Control

Variables

Market Value t-1

(log)

-0.352***

(-89.266)

-0.024***

(-30.255)

-0.894***

(-85.984)

Share Turnover

t-1 (log)

-0.274***

(-46.374)

-0.040***

(-33.185)

-0.970***

(-59.553)

Return

Variability t-1

(log)

-0.074***

(-8.016)

-0.030***

(-18.157)

-0.572***

(-13.458)

Market

Benchmark

-0.157***

(-5.014)

0.867

(0.806)

0.594***

(10.796)

R square 0.705 0.382 0.745

Number of

observations

7,725 8,315 6,972

Number of

unique firms

1,793 1,803 1,694

46


This table illustrates some of the sensitivity analyses’ results regarding the market liquidity

variables. The methodology and the variables are as described in Table 4 though we do not

report all the coefficients.

The presented sensitivity analyses stem from the same sample as in Table 4 but without

observations from Portugal (Panel A). When we remove Portugal, the average enforcement and

transparency scores are higher for the strong differences group. Furthermore, we excluded the

benchmark observations from the sample and calculated a regression only between the strong

and weak differences treatment groups (Panel B). Consequently, we demonstrated that the

differences in the coefficients are statistically significant, not only against the benchmark

countries but also between the two adopter groups.

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