Upload
vuthien
View
232
Download
1
Embed Size (px)
Citation preview
1
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.
2
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
3
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.
4
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
5
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.
6
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.
7
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).
8
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
9
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.
10
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
11
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.
12
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.
13
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.).
15
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).
16
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
<insert table 2 about here>
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.
<insert table 3 about here>
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.
<insert table 4 about here>
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.
<insert table 5 about here>
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
References
Amihud, Y. (2002). Illiquidity and Stock Returns. Journal of Financial Markets, 5, 31-
56.
Amihud, Y., & Mendelson, H. (1986). Asset pricing and the bid–ask spread. Journal of
Financial Economics, 17, 223-249.
Andenas, M., & Kenyon-Slade, S. (1993). E.C. Financial Market Regulation and
Company Law. London: Sweet and Maxwell.
Armstrong, C., Barth, M., Jagolinzer, A., & Riedl, E. (2010). Market Reaction to the
IFRS adoption in Europe. The Accounting Review, 85, 31-62.
Bae, K. -H., Tan, H., & Welker, M. (2008). International GAAP Differences: The
Impact on Foreign Analysts. The Accounting Review, 83, 593–628.
Ball, R., Robin, A., & Wu, J. (2003). Incentives Versus Standards: Properties of
Accounting Income in Four East Asian Countries. Journal of Accounting &
Economics, 36, 235–270.
Ball, R., & Shivakumar, L. (2005). Earnings Quality in U.K. Private Firms. Journal of
Accounting & Economics 39, 83–128.
Barth, M., & Schipper, K. (2008). Financial Reporting Transparency. Journal of
Accounting Auditing and Finance, 23, 173-190.
Bellver, A., & Kaufmann, D. (2005). Transparenting Transparency Initial Empirics and
Policy Applications. Washington, DC: The World Bank. Available at
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=808664.
Bergstresser, D., Desai, M., & Rauh, J. (2006). Earnings manipulation, pension
assumptions, and managerial investment decisions. The Quarterly Journal of
Economics, 121, 157-195.
Botosan, C. (1997). Disclosure Level and the Cost of Equity Capital. The Accounting
Review, 72, 323-349.
30
Botosan C., & Plumlee, M. (2002). A Re-examination of Disclosure Level and the
Expected Cost of Equity Capital. Journal of Accounting Research, 40, 21-40.
Brav, A., & Heaton, J. (2002). Competing theories of financial anomalies. Review of
Financial Studies, 15, 575–606.
Brown, P., Preiato, J., & Tarca, A. (2009). Mandatory IFRS and Properties of Analysts
Forecasts: How Much Does Enforcement Matter? Australian School of Business:
Research Paper No. 2009 ACCT 01. Available at
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1499625.
Burgstahler, D., Hail, L., & Leuz, C. (2006). The Importance of Reporting Incentives:
Earnings Management in European Private and Public Firms. The Accounting
Review, 81, 983–1016.
Campbell, J. (1996). Understanding Risk and Return. Journal of Political Economy,
104, 298–345.
CESR: Committee of European Securities Regulators (2007). CESRs review of the
implementation and enforcement of IFRS in the EU, Ref 07-352. Paris: CESR.
Available at http://www.cesr-eu.org/data/document/07_352.pdf.
Chordia, T., Roll, R., & Subrahmanyam, A. (2000). Co-Movements in Bid-Ask Spreads
and Market Depth. Financial Analysts Journal, 56, 23–27.
Christensen, H., Lee, E., & Walker, M. (2007). Cross-sectional variation in the
economic consequences of international accounting harmonization: The case of
mandatory IFRS adoption in the UK. The International Journal of Accounting, 42,
341-379.
Christensen, H., Hail, L., & Leuz, C. (2011). Capital Market Effects of Securities
Regulation: The Role of Implementation and Enforcement. NBER Working Paper
No. 16737. Available at http://www.nber.org/papers/w16737.
31
Daske, H. (2005). Adopting international financial reporting standards in the European
Union : Empirical essays on causes, effects and economic consequences. Johann
Wolfgang Goethe-Universität Frankfurt am Main: Thesis.
Daske, H., Hail, L., Leuz, C., & Verdi, R. (2008). Mandatory IFRS Reporting Around
the World: Early Evidence on the Economic Consequences. Journal of Accounting
Research, 46, 1085-1142.
Ding, Y., Jeanjean, T., & Stolowy, H. (2009). Observations on measuring the
differences between domestic accounting standards and IAS: A reply. Journal of
Accounting and Public Policy 28, 154-161.
Dunne, T., Fifield, S., Finningham, G., Fox, A., Hannah, G., Helliar, C., Power, D., &
Veneziani, M. (2008). The Implementation of IFRS in the UK, Ireland and Italy.
Edinburgh: ICAS.
EFRAG: European Financial Reporting Advisory Group (2011). Considering the
Effects of Accounting Standards. Brussels: EFRAG. Available at
http://www.efrag.org/files/News%20related%20documents/Jan31%202011%20Effe
cts%20Analysis%20DP_Final.pdf.
Fearnley, S., & Hines, T. (2007). How IFRS has destabilised financial reporting for UK
non-listed entities. Journal of Financial Regulation and Compliance, 15, 394-408.
Filzen, J., & Peterson, K. (2010). Accounting Complexity and Meeting Analysts’
Expectations, University of Oregon: Working Paper. Received from the authors.
FRC: Financial Reporting Council (2009). Louder than Words: Principles and Actions
for Making Corporate Reports Less Complex and More Relevant. London: The
FRC. Available at http://www.frc.org.uk/press/pub1994.html.
FREP: Financial Reporting Enforcement Panel (2009). Annual Activity Report 2008.
Berlin: FREP. Available at http://www.frep.info/docs/jahresberichte/
2008_tb_prufstelle.pdf.
32
FREP: Financial Reporting Enforcement Panel (2011). Annual Activity Report 2010.
Berlin: FREP. Available at http://www.frep.info/docs/jahresberichte/
2010/2010_tb_pruefstelle.pdf.
FRRP: Financial Reporting Review Panel (2009). Annual Activity Report 2009.
London: The FRC. Available at http://www.frc.org.uk/frrp/press/pub2039.html.
Glosten, L., & Milgrom, P. (1985). Bid, Ask and Transaction Prices in a Specialist
Market With Heterogeneously Informed Traders. Journal of Financial Economics,
14, 71–100.
Hail, L., & Leuz, C. (2007). Capital Market Effects of Mandatory IFRS Reporting in the
EU: Empirical Evidence. Amsterdam: Netherlands Authority for the Financial
Markets. Available at http://www.afm.nl/corporate/default.ashx?Document
Id=10519.
Healy, P., Hutton, A., & Palepu, K. (1999). Stock Performance and Intermediation
Changes Surrounding Sustained Increases in Disclosure. Contemporary Accounting
Research, 16, 485–520.
Hirshleifer, D. (2001). Investor psychology and asset pricing. Journal of Finance, 56,
1533–1598.
Hirst, E., & Hopkins, P. (1998). Comprehensive income reporting and analysts
valuation judgments. Journal of Accounting Research, 36, 47–75.
Holthausen, R. (2009). Accounting Standards, Financial Reporting Outcomes, and
Enforcement. Journal of Accounting Research, 47, 447-458.
Hong, H., & Stein, J. (1999). A unified theory of underreaction, momentum trading and
overreaction in asset markets. Journal of Finance, 54, 2143–2184.
IASB: International Accounting Standards Board (2006). Discussion Paper: Preliminary
Views on an Improved Conceptual Framework for Financial Reporting: Objective
of Financial Reporting and Qualitative Characteristics of Decision-Useful Financial
33
Reporting Information. London: IASB. Available at
https://www.imanet.org/pdf/conceptual_framework.pdf.
Jermakowicz, E., & Gornik-Tomaszewski, S. (2006). Implementing IFRS from the
perspective of EU publicly traded companies. Journal of International Accounting,
Auditing and Taxation, 15, 170-196.
Kaufmann, D., Kraay, A., & Mastruzzi, M. (2009). Governance Matters VIII:
Aggregate and Individual Governance Indicators 1996–2008. Washington, DC: The
World Bank. Available at http://papers.ssrn.com/sol3/
papers.cfm?Abstract_id=1424591&rec=1&srcabs=1165343.
Lang, M., & Lundholm, R. (2000). Voluntary Disclosure and Equity Offerings:
Reducing Information Asymmetry or Hyping the Stock? Contemporary Accounting
Research, 17, 623-662.
Lang, M., Raedy, J., & Wilson, W. (2006). Earnings management and cross listing: Are
reconciled earnings comparable to US earnings? Journal of Accounting &
Economics, 42, 255-283.
La Porta, R., Lopez-de-Silanes, F., Shliefer, A., & Vishny, R. (1998). Law and Finance.
Journal of Political Economy, 106, 1113–1155.
Larson, R., & Street, D. (2004). Convergence with IFRS in an expanding Europe:
progress and obstacles identified by large accounting firms survey. Journal of
International Accounting, Auditing and Taxation, 13, 89-119.
Leuz, C., & Verrecchia, R. (2000). The Economic Consequences of Increased
Disclosure. Journal of Accounting Research, 38, 91-124.
Leuz, C., Nanda, D., & Wysocki, P. (2003). Earnings Management and Investor
Protection: An International Comparison. Journal of Financial Economics, 69, 505–
527.
34
Li, F. (2008). Annual Report Readability, Current Earnings, and Earnings Persistence.
Journal of Accounting and Economics, 45, 221-247.
McEwen, R., & Hunton, J. (1999). Is analyst forecast accuracy associated with
accounting information use? Accounting Horizons, 13, 83–96.
Miller, B. (2008). Data Overload and Investor Trading. Penn State University:
Dissertation.
Palmrose, Z. (2009). Science, Politics, and Accounting: A View from the Potomac. The
Accounting Review, 84, 281-297.
Picconi, M. (2006). The perils of pensions: does pension accounting lead investors and
analysts astray? The Accounting Review, 81, 925-955.
SEC : Securities and Exchange Commission (2008). Final Report of the Advisory
Committee on Improvements to Financial Reporting to the United States Securities
and Exchange Commissions. Washington, DC: SEC. Available at
http://www.sec.gov/about/offices/oca/acifr/acifr-finalreport.pdf.
Stoll, H. (1978). The Supply of Dealer Services in Securities Markets. Journal of
Finance, 33, 1133–51.
Welker, M. (1995). Disclosure policy, information asymmetry, and liquidity in equity
markets. Contemporary Accounting Research, 11, 801-828.
Werlauff, E. (1993). EC Company Law: The Common Denominator for Business
Undertakings in 12 States. Copenhagen: Jurist- og Okonomforbundets Forlag.
You, H., & Zhang, X-j. (2009). Financial reporting complexity and investor
underreaction to 10-K information. Review of Accounting Studies, 14, 559-586.
35
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
Table 1. (continued)
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
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)
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
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)
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
Table 3. (continued)
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
Table 4. (continued)
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
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
-0.099*
(-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
Table 5. (continued)
Panel B: Sensitivity Analysis without Benchmark Observations
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
0.453***
(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
Table 5. (continued)
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.