chua cheong gould 2012
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JOURNAL OF INTERNATIONAL ACCOUNTING RESEARCH American Accounting AssociationVol. 11, No. 1 DOI: 10.2308/jiar-102122012pp. 119146
The Impact of Mandatory IFRS Adoption onAccounting Quality: Evidence from Australia
Yi Lin (Elaine) Chua, Chee Seng Cheong, and Graeme Gould
ABSTRACT: Following the mandatory implementation of International Financial
Reporting Standards (IFRS) in Australia as of January 1, 2005, this study examines
its impact on accounting quality by focusing on three perspectives: (1) earnings
management, (2) timely loss recognition, and (3) value relevance. Using four years ofadoption experience since the mandate was first made effective in Australia for a wide
range of accounting-based metrics and market-based information, we find that the
mandatory adoption of IFRS has resulted in better accounting quality than previously
under Australian generally accepted accounting principles (GAAP). In particular, the
findings indicate that the pervasiveness of earnings management by way of smoothing
has reduced, while the timeliness of loss recognition has improved post-adoption.
Additionally, the value relevance of financial statement information has improved,
especially for non-financial firms. This is despite the fact that there is evidence to suggest
that financial firms are engaged in managing earnings toward a small positive target after
the mandatory adoption of IFRS in Australia.
Keywords: IFRS; accounting quality; international accounting; Australia.
I. INTRODUCTION
In 2002, Australia and the European Union (EU) formalized their decision to adopt
International Financial Reporting Standards (IFRS) mandatorily as of January 1, 2005 (FRC
2002; Armstrong et al. 2010). Even though IFRS1 have been developed by the International
Accounting Standards Board (IASB) for a notably long period,2 these events marked the beginning
Yi Lin (Elaine) Chua is an Associate Lecturer, Chee Seng Cheong is a Senior Lecturer, and Graeme Gould is a
Lecturer, all at the University of Adelaide.
We gratefully acknowledge the valuable comments of Ervin Black (editor), Nabil Elias (discussant), Jim Larkin, GrantRichardson, two anonymous referees, and participants at the 2010 Journal of International Accounting Researchconference. All errors and omissions are our own.
Published Online: January 2012
1For simplicity, the term IFRS is used in this paper to include both old and new versions of internationalaccounting standards (including IAS). This is consistent with the definition of IFRS as stated in IAS 1.11(Deloitte 2009b).
2 This effort started in 1973 with the establishment of the IASBs predecessor, the International AccountingStandards Committee (IASC). Standards issued by the IASC were known as International Accounting Standards(IAS) and these standards were subsequently incorporated into IFRS in 2006 which resulted in a single set of
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of the era of mandatory IFRS adoption by countries around the world. This has introduced a new
phase of interest in IFRS, as many global capital market participants are becoming increasingly
concerned whether accounting quality had been significantly affected by the transition. To address
this question, we examine the association between IFRS adoption and accounting quality in the
context of the Australian capital market. Specifically, earnings management, timely loss
recognition, and value relevance of accounting numbers are compared before and after the
mandatory introduction of IFRS in Australia to determine its effect on accounting quality.3
As the adoption worldwide represents a major shift in the international financial reporting
arena, empirical evidence on IFRS adoption has become more and more imperative in accounting
literature. In particular, much related research began by focusing on the determinants and
consequences of adopting IFRS voluntarily (e.g., Ashbaugh and Pincus 2001; Barth et al. 2008).
Based on these studies, improved accounting quality due to high-quality accounting standards and
enhanced comparability are among the benefits claimed by proponents of IFRS adoption. However,
the inherent self-selection bias in the earlier research on voluntary IFRS adoption has prompted the
question whether the positive findings can be generalized to those adopting firms in the mandatory
environment. In contrast to the traditional approach of adopting IFRS voluntarily, such as those
commonly found in Germany for example (Soderstrom and Sun 2007), more and more countries
are now following the footsteps of the forerunner countries, like Australia and the EU, to make the
adoption compulsory for firms in their countries.4 As a consequence, these affected firms are
required to change to IFRS in compliance with the law and have little say about the resulting
impacts.
This study aims to exploit the unique features offered by the Australian adoption of IFRS and
to contribute to the literature examining the effects of adopting IFRS in several ways. First,
Australia is one of the first countries located outside of the EU that has mandated the adoption of
IFRS. Therefore, we contribute to the existing literature that has largely focused on EU adoption
only. The findings also provide more comparable evidence to other adopting countries, as theiradoption is not similarly motivated by the EU harmonization efforts5 and so their degree of
adoption impacts can vary from those in the EU (Daske et al. 2008). Additionally, Australia is a
forerunner country in mandating the adoption of IFRS and so it has a comparatively longer
adoption experience relative to other countries that mandated the adoption post-2005. This allows a
sufficient information window to assess the impact of mandating the adoption, as the effects often
require time to materialize post-implementation. Finally, Australia is also the first non-EU adopting
country that had fully prohibited an early adoption of IFRS prior to the 2005 mandate (Jeanjean and
Stolowy 2008). This provides a suitable setting to include only mandatory adopters in this study, as
the presence of voluntary adopters would create a self-selection bias to the findings that needs to be
controlled for (see Leuz and Verrecchia 2000; Ashbaugh and Pincus 2001; Van Tendeloo andVanstraelen 2005; Covrig et al. 2007; Barth et al. 2008).
With a cumulative four years of adoption experience on-hand for Australia, we compare the
quality of accounting numbers under Australian GAAP and IFRS by using a wide range of
accounting-based metrics and market-based data. Consistent with prior research, the impact on
3The research question focuses on the application of IFRS in the Australian context and therefore shouldaccurately refer to the Australian equivalent of IFRS (A-IFRS) and not IFRS per se. Given that both sets ofstandards are almost identical in most cases, for simplicity IFRS is used throughout this paper.
4Details about the adoption timetable for individual countries can be obtained from the IAS Plus website at http://www.iasplus.com
5 The EUs harmonization efforts began in the 1970s and since then have involved a number of AccountingDirectives. Among them, the Fourth Directive requires all limited liability companies to prepare annual financialstatements while the Seventh Directive requires a parent company to prepare consolidated financial statements
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accounting quality is examined from three different perspectives (Lang et al. 2003; Lang et al.
2006; Barth et al. 2008; Christensen et al. 2008; Paananen and Lin 2009). First, we compare the
pervasiveness of earnings management under Australian GAAP and IFRS, by examining the extent
in which earnings are smoothed and managed toward a positive target. Second, we assess whether
the mandatory change in accounting standards has affected the timely loss recognition in theAustralian capital market. Third, we assess whether IFRS has led to a change in the value relevance
of accounting numbers produced by Australian firms. Based on this research design, we not only
take into account the uniqueness of the Australian adoption of IFRS, but also provide more robust
evidence than previous Australian studies that have only included a single metric and a limited
timeframe in examining the quality of accounting numbers under IFRS (see Goodwin et al. 2008a;
Jeanjean and Stolowy 2008). By limiting the investigation in Australia, we also aim to hold
constant the influence of institutional factors in determining accounting quality to strengthen the
validity of our findings.
Overall, inferences based on a sample of 1,376 firm-year observations for 172 Australian listed
firms provide support that the adoption of IFRS in Australia has made an improvement to
accounting quality. Specifically, we find evidence that following the mandatory adoption of IFRS,
Australian firms engage in less earnings management by way of income smoothing, better timely
loss recognition, and improvement in value relevance of accounting information.
The remainder of this paper is organized as follows. The next section reviews the relevant
literature on the adoption of IFRS, which subsequently leads to the development of hypotheses. The
third section explains the research design and sample data employed in the study. The fourth section
presents the descriptive and empirical results, and we provide our conclusions in the final section.
II. LITERATURE REVIEW AND HYPOTHESES DEVELOPMENT
Consistent with the long-term objective of the IASB, IFRS purport to be a set of high-qualityaccounting rules that would ideally be applied consistently by public companies globally to ensure
that they are acceptable by the capital markets around the world (IASB 2009). While there is no
consensus as to what constitutes high-quality accounting standards, IFRS are perceived to be high
quality because they represent a collection of the worlds best accounting practices and are
purported to be more capital-market-oriented than many domestic accounting standards6 (Ding et
al. 2007). The principles-based nature of IFRS (Carmona and Trombetta 2008) also encourages
firms to report accounting information that better reflects the economic substance over form and
therefore promotes greater transparency (Maines et al. 2003). Accordingly, it is posited that the
adoption of IFRS is associated with high accounting quality, and the research by Barth et al. (2008)
is a prominent paper in support of this view.7
By using a sample of firms from 21 countries, Barth etal. (2008) show that firms that adopted IFRS voluntarily exhibit less earnings management, more
timely loss recognition, and greater value relevance of accounting income. Together, these findings
support the notion that the IFRS firms are of higher quality than those matched sample firms
applying non-U.S. local accounting standards. Furthermore, accounting quality is also found to
have improved after those adopting firms moved from local accounting standards to IFRS. Overall,
the research evidences that accounting quality, on average, has improved for voluntary IFRS
adopters around the world.
6This contrasts from the stakeholders-oriented accounting standards traditionally found in code-law countries,
like Germany and France. It is argued by prior literature that the stakeholders-oriented standards are of lowerquality than the capital-market-oriented standards (Ball et al. 2003).
7 See also Bartov et al (2005) in respect to the higher-value relevance of IFRS earnings over those under German
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Those in favor of IFRS adoption also argue that IFRS standards enhance comparability of
financial statements across countries and markets, which is also a component of high-quality
financial reporting (Pownall and Schipper 1999). By using the same accounting language in
preparing financial statements across different countries, global investors and financial analysts are
less likely to face interpretation difficulties, thereby facilitating information flow between capitalmarkets and encouraging cross-border capital raisings.8 Ashbaugh and Pincus (2001) find that for
firms in 13 countries, analysts forecast accuracy increases after they voluntarily adopted IFRS.
Additionally, they also find that forecast accuracy is negatively associated with the differences
between domestic accounting standards and IFRS. These findings support the argument that by
eliminating many differences in accounting standards and standardizing the format of reporting
through the use of IFRS, analysts and investors can reduce the need to make adjustments when
comparing financial statements internationally (Ball 2006), enabling them to better monitor and
evaluate the quality of financial statements across firms (Jeanjean and Stolowy 2008; Daske et al.
2008). This potentially induces management to provide higher-quality information to users for their
decision making.
Despite the persuasive arguments that IFRS adoption enhances accounting quality and that some
evidence exists supporting the claims, there are also prior studies that suggest the contrary, especially
in the mandatory adoption environment. For instance, Paananen and Lin (2009) find that the
development of IFRS had caused accounting quality to worsen over time. Specifically, they find that
German firms exhibit a fall in accounting quality after they adopted IFRS mandatorily. This is further
supported by Christensen et al. (2008), who find consistent results analogous to Barth et al. (2008) for
voluntary adopting firms in Germany, but could not find such improvements for German firms that
delayed their adoption until being mandated. Furthermore, Jeanjean and Stolowy (2008) find that the
first-time IFRS adopting firms in Australia and the U.K. showed relatively persistent earnings
management after the mandatory adoption of IFRS, while those in France showed an increase in
earnings management. In contrast to the positive results of earlier research on voluntary IFRS
adoption, these recent studies suggest that it is not appropriate to generalize the effects of adopting
IFRS from the previous voluntary adoption experience to the current mandatory environment.
Considering the mixed findings for the impact of adopting IFRS on accounting quality,
distinguishing prior studies across voluntary and mandatory adopters thus rests on the influence of
adopters incentive to utilize IFRS. Those voluntary IFRS adopters are said to have discretion to
choose the bestdisclosure rules (IFRS in this instance) that reduce information asymmetry with
principals (who are less informed) about future prospects of the firm and managers consumption of
perquisites9 (Jensen and Meckling 1976), whereas firms in countries that mandated IFRS adoption
must now apply IFRS regardless of whether they consider this to be an economical decision. As a
result, several recent studies have considered reporting incentives to be a more dominant factor in
determining the observed accounting quality (Ball et al. 2003; Burghstahler et al. 2006; Soderstrom
and Sun 2007; Christensen et al. 2008). Therefore, the inherent self-selection bias in the earlier
research of voluntary IFRS adoption potentially overestimates the positive impact of adopting
IFRS, and the findings cannot be generalized to the current trend of mandatory adoption without
caution.
8The endorsement of the International Organization of Securities Commission (IOSCO) in 2000, which permitscompanies to prepare IFRS-based accounts for cross-border offerings and listings in major capital markets, isindicative that IFRS are acceptable for international investments and transactions (Haller 2002; Deloitte 2009a).
Similarly, Covrig et al. (2007) also find that companies around the world attracted higher investments fromforeign mutual funds by adopting IFRS voluntarily instead of using domestic standards.
9 Each firm is expected to choose the best set of accounting standards based on their individual circumstances
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The mixed findings documented by prior studies also highlight that the effect of adopting IFRS
on accounting quality could vary across different countries. This is because prior literature suggests
that countries institutional structures play an important role in determining accounting quality
through the countries legal and political systems (Burghstahler et al. 2006; Soderstrom and Sun
2007; Holthausen 2009). Specifically, Daske et al. (2008) show that the incremental economicbenefits following the mandatory IFRS adoption only occur in countries where firms have
incentives to be transparent and where legal enforcement is strong. This contradicts the proposition
that switching to IFRS does not provide much incremental benefit to countries that have enjoyed
high-quality accounting standards and strong investor protection mechanisms. This is based on the
assumption that these countries would have better reporting practices prior to the introduction of
IFRS, all else equal, even if the presumption that high-quality accounting standards alone improve
firms reporting quality is valid (Jeanjean and Stolowy 2008). Having said that, many past studies
on IFRS adoption have particularly concentrated on the EU setting because of the large number of
countries involved (Armstrong et al. 2010) and the presence of many code-law countries in the EU
that facilitates a comparison of common-law and code-law standards (Christensen et al. 2008).
Nevertheless, it is difficult to generalize the findings of these EU studies to non-EU adopting
countries, as harmonization efforts within the EU may have resulted in a significantly larger impact
following the EU adoption than other non-EU adopting countries (Daske et al. 2008). Overall, there
is no clear evidence on how the implementation of IFRS impacts accounting quality for the growing
number of non-EU countries that have either mandated or are in the process of mandating the
adoption.
Hypotheses Development
In view of the conflicting arguments and mixed findings for the impact of adopting IFRS
mandatorily on accounting quality, the net effect for the Australian adoption of IFRS is thereforeuncertain. Although Australia began its mandatory adoption of IFRS from January 1, 2005,
Australian firms have had experience in using principles-based standards from the application of
Australian GAAP, which should be similarly applicable to the use of IFRS (Brown and Tarca
2005). This provides Australia with a potential competitive advantage over other adopting
countries, especially those code-law countries in the EU. Furthermore, the existence of a high-
quality national accounting regime in Australia and a well-regarded reputation for enforcement may
also imply that the country had already enjoyed high-quality reporting practices prior to the
introduction of IFRS10 (La et al. 1998; Kaufmann et al. 2008; Haswell and McKinnon 2003;
Haswell and Langfield-Smith 2008). This favorable position is expected to allow Australian firms to
have a more manageable and smoother transition to IFRS, suggesting that the adoption is expectedto result in a smaller or negligible impact on the change in Australian accounting quality.
Taking into account the benefits asserted by supporters of IFRS adoption and findings of prior
research, the Financial Reporting Council (FRC)11 of Australia claimed in 2002 that the adoption of
IFRS would improve the overall quality of financial reporting in Australia (FRC 2002).
However, this view was not entirely supported by all commentators, academics, and the business
community. Specifically, Haswell and McKinnon (2003) suggested that a change to IFRS could
possibly reduce the overall quality of financial reports in Australia, which potentially contradicts the
10There is no empirical evidence to suggest that Australia has experienced significant institutional changes during
the sample period.11
The FRC is an Australian government body responsible for providing broad oversight for the standards-settingprocess in Australia More details about this organization can be obtained from the FRC website at http://www
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objective of the Australian adoption of IFRS. The concern is also further exacerbated by the
findings of several studies, which documented that Australian firms were not well prepared for the
transition to IFRS, even within months prior to the mandate date (see Muir 2004; Jones and Higgins
2006; Goodwin et al. 2008b). Moreover, some commentators still observed many notable
differences between Australian GAAP and IFRS (Howieson and Langfield-Smith 2003; Haswelland McKinnon 2003; Haswell and Langfield-Smith 2008; Goodwin et al. 2008a), and that
implementation had also resulted in significant costs to many adopting firms (PWC 2008). Given
the unsettling results about the preparedness of Australian firms on the adoption, there are some
doubts about the effectiveness of implementation following the adoption and its influence on
decreasing accounting quality in Australia.
Two studies have directly examined the impact of the mandatory adoption of IFRS on
accounting quality in Australia. First, Goodwin et al. (2008a) investigate the effect of IFRS
adoption in Australia on both the accounts and value relevance, by examining the first-time
reconciliations to IFRS provided in the first annual accounts under IFRS. Despite finding that the
adoption of IFRS has resulted in significant adjustments to accounting numbers and ratios, they find
mixed findings in terms of the value relevance of the IFRS numbers over those under Australian
GAAP, suggesting that financial reporting quality has not been improved as claimed by the FRC. In
the other study, using earnings management as a proxy for accounting quality, Jeanjean and
Stolowy (2008) examine whether the adopting firms in Australia have managed their earnings to
avoid losses any less after the introduction of IFRS.12 By analyzing the distributions of earnings
between 2002 and 2006, they find that the pervasiveness of earnings management had not changed
in Australia. Although each of these two studies has assessed accounting quality from a different
perspective (value relevance and earnings management), both studies are subject to the same
limitation of relying on a single measure to investigate the multi-dimensional concept of accounting
quality. On top of that, both studies only focused on a short period of time after the implementation
of IFRS in Australia and so may not have allowed sufficient time for the effects of adoption to
materialize. To address these limitations, we therefore use multiple measures to proxy accounting
quality, as well as a longer information window than the existing literature.
On the whole, we predict that the mandatory implementation of IFRS had affected accounting
quality in Australia. Even though Australian firms are perceived to have a more superior position in
the changeover to IFRS, prior research has shown that the compulsory move from Australian
GAAP to IFRS still resulted in significant adjustments to both the accounts and the transition
process (see Muir 2004; Jones and Higgins 2006; Goodwin et al. 2008a, 2008b). While earlier
studies on IFRS adoption provide support to the claim that accounting quality should improve
following the use of IFRS (e.g., Bartov et al. 2005; Barth et al. 2008), there are also several
instances where a negative impact has been found on accounting quality in the recent mandatoryenvironment (e.g., Christensen et al. 2008; Paananen and Lin 2009). As a consequence, these mixed
findings do not provide us with a clear prediction about the impact on accounting quality in the
context of the Australian adoption of IFRS. On one hand, the mandatory introduction of IFRS in
Australia can be justified by the positive findings of earlier research. The benefit of improved
accounting quality following IFRS adoption is also likely to eventuate in the Australian
environment where both legal enforcement and investor protection are purported to be strong. On
the other hand, the recent studies have shown that mandating such a radical change in financial
reporting is less likely to increase firms incentive to benefit from IFRS adoption; thereby, this
potentially impedes the effective implementation of IFRS and hampers the existing high-quality
12 Apart from Australia Jeanjean and Stolowy (2008) also include France and the U K in their study The findings
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reporting practices in Australia. Furthermore, the well-regarded reputation for a high-quality
Australian accounting regime in the time preceding IFRS adoption is also likely to set a relatively
high benchmark for an improvement in accounting quality to materialize following the mandatory
change in Australia. Putting the aforementioned limitations aside, Goodwin et al. (2008a) and
Jeanjean and Stolowy (2008) show that accounting quality in terms of value relevance and earningsmanagement has not improved within the short timeframe after the mandatory implementation of
IFRS in Australia. If accounting quality has indeed been enhanced as a result of the mandatory
adoption of IFRS, then we should expect to find less earnings management, more timely
recognition of losses, as well as higher value relevance of accounting numbers in Australia post-
adoption, orvice versa.
Taken together that we have no clear prediction about the direction of which accounting quality
had been affected by the mandatory adoption of IFRS in Australia, we therefore propose the
following research hypotheses:
H1: Earnings management has changed following the mandatory adoption of IFRS in
Australia.
H2: Timely loss recognition has changed following the mandatory adoption of IFRS in
Australia.
H3: The degree of association between accounting data and share price (i.e., value relevance)
has changed following the mandatory adoption of IFRS in Australia.
III. RESEARCH METHODOLOGY
Sample and Dataset Selection
As stated earlier, we focus on the Australian capital market to analyze the impact of mandating
the adoption of IFRS. Table 1 presents the sample selection process. We began by selecting the top
500 firms by market capitalization listed on the Australian Stock Exchange (ASX) in both the pre-
adoption and the post-adoption periods.13 We retain firms that are part of the top 500 by market
capitalization in both periods for our study. This enables the inclusion of firms that are of similar
size before and after the adoption of IFRS, which have previously used Australian GAAP in the
pre-adoption period and later transited mandatorily to IFRS in the post-adoption period for
investigation. Also, sample firms must have fiscal year-end of 12 months for each sample period
and data available both before and after the adoption of IFRS to enable a comparison between
periods of the same firms, for which all financial and accounting data were collected from
Connect4, Worldscope, and Thomson One databases. Based on these requirements, our final
sample consists of 172 Australian listed firms, which provides 1,376 (8 years3172 firms) firm-year
observations for the study.14
We use each firm as its own control for two reasons. First, the adoption of IFRS in Australia is
compulsory, beginning on the same date for both listed and unlisted reporting entities governed by
theCorporations Act 2001. Therefore, there is no benchmark firm using Australian GAAP available
13Our cut-off dates in selecting the top 500 Australian listed firms by market capitalization for both the pre-adoption and the post-adoption periods are June 30, 2004, and June 30, 2009. June 30, 2009, was the lastfinancial year end (in the post-adoption period) for our sample period, while June 30, 2004, was the last financial
year end in June (in the pre-adoption period) in which all sample firms prepared their financial statements underAustralian GAAP (including for firms with December 31 year end and those with post-December 31 year end).
14 There are equal numbers of firm-year observations in the pre-adoption period (i e 688 firm-year observations)
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financial year end date. As a result, the 2006 reporting year became the first period in which the
majority sample firms with non-December financial year end dates were required to comply with
IFRS reporting. Table 4 presents the reporting years for which data are grouped into the pre-
adoption and the post-adoption periods, on the basis of whether the sample firms have fiscal year-
end of December 31 or post-December 31.15 This approach ensures that data for the post-adoption
period consist of the first four reporting periods under IFRS for all sample firms, with an equalnumber of observations for the same firms in the pre-adoption period.
Accounting Quality Metrics
Following prior research, we operationalize accounting quality based on three perspectives: (1)
earnings management, (2) timely loss recognition, and (3) value relevance (Lang et al. 2006; Barth et
al. 2008; Christensen et al. 2008; Paananen and Lin 2009). Albeit there are numerous ways proposed
by prior studies in measuring accounting quality,16 there is still a lack of consensus on the definition
of the concept. Therefore, we attempt to adopt these three perspectives, in order to draw upon the
interpretation of Ball et al. (2003, 237) on accounting quality. That is, financial reporting quality is
related to the concept of transparency, defined as the ability of users to see through the financial
statements to comprehend the underlying accounting events and transactions in the firm.
Consistent with this interpretation, we attempt to associate our concept of accounting quality
with accounting-based attributes,17 by adopting earnings management and timely loss recognition
constructs that allow us to concentrate on the quality of accounting information prepared under
TABLE 2
Industry Breakdown
GICS Classification GICS Sector Code Number of Firms Percentage
Energy 10 8 4.65%
Materials 15 25 14.54%
Industrials 20 30 17.44%
Consumer Discretionary 25 28 16.28%
Consumer Staples 30 10 5.82%
Health Care 35 16 9.30%
Financials 40 40 23.26%
Information Technology 45 9 5.23%
Telecommunication Services 50 3 1.74%
Utilities 55 3 1.74%
Total 172 100.00%
GICSGlobal Industry Classification Standard.
15A year end date of June 30 is most common for this group of firms.
16Other measures include accrual quality, persistence, predictability, and conservatism (Schipper and Vincent2003; Francis et al. 2004).
17Francis et al. (2004) identify seven earnings attributes as related to earnings quality (similar to accountingquality). They classify seven earnings attributes into two categories: accounting based (accrual quality,persistence, predictability, and smoothness) and market based (value relevance, timeliness, and conservatism).As explained in their paper accounting-based attributes use only accounting information while market-based
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Australian GAAP (in the pre-adoption period) and IFRS (in the post-adoption period). At the same
time, we also include market-based constructs for value relevance to complement those accounting-
based constructs in strengthening our findings for the multi-faceted concept of accounting quality.
Earnings Management
We develop four constructs to proxy two perspectives of earnings management: (1) earnings
smoothing and (2) managing earnings toward a positive target. This is done by closely following
the metrics used in Barth et al. (2008), and they include the variability of the change in net income
(DNI), the mean ratio of the variability of the change in net income (DNI) to the variability of the
change in operating cash flows (DOCF), the Spearman correlation of accruals (ACC) and cash
flows (CF), as well as the coefficient from a logit regression of small positive earnings (SPOS).
By using a variety of constructs to measure earnings management, we aim to provide evidence
that is less circumstantial, given that earnings management is neither directly observable nor can beeasily disentangled from the effects of accounting differences arising from the changes in the
underlying economics (Lang et al. 2003). Nevertheless, we also attempt to minimize the influence of
other factors on earnings management, by including several control variables that are identified by
prior studies to be unrelated to the mandatory adoption of IFRS (Lang et al. 2006; Barth et al. 2008).
The first earnings smoothing measure is based on the variability of the change in annual net
income (scaled by total assets) (DNI). This measure is designed to detect the presence of earnings
smoothing because to the extent that earnings are being opportunistically managed, all else equal,
there should be lower earnings variability. Therefore, we measure the fluctuation in earnings stream
by the change in annual net income. The reported earnings are also first being deflated (by total assets)
so that the earnings series is more likely to demonstrate a random walk and can be inferred as lessaffected by the fundamental differences among firms (Lev 1983). Nonetheless, the reported earnings
can still be sensitive to a wide range of other factors that are unattributable to the mandatory adoption
of IFRS. As a result, we include a number of control variables identified in prior literature (Lang et al.
2006; Barth et al. 2008) to partially mitigate these confounding effects before inferring the results as
the effect of changing to IFRS compulsorily. This means that the interpretation of the regression thus
focuses on the residuals that are generated from the relevant regression, rather than on the reported
earnings themselves. On this basis, the first earnings smoothing measure is taken as the variance of
the residuals (Equation (1)) from a regression of the change in annual net income (scaled by total
assets) (DNI) on the control variables (Equation (1a)):18
TABLE 3
Fiscal Year End Breakdown
Fiscal Year EndNumber of
Firms
Number of
Firm-YearObservations Percentage
First IFRSReporting Year
Reporting YearObservations
December 31 23 92 13.37% Year 2005 20012008
Post-December 31 149 596 86.63% Year 2006 20022009
Total 172 688 100.00%
18 As explained by Barth et al (2008) using this approach assumes that the measure of the variability of the change
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Variability ofDNI# r2ErrorDNIi; 1
where:
DNI#residuals from the regression ofDNIon the control variables (Equation (1a)).
Equation (1a): Regression ofDNIon the control variables:
DNIi a0 a1SIZEia2GROWTHi a3EISSUEia4LEVia5DISSUEi a6TURNia7CFia8AUDi a9NUMEXia10XLISTia11CLOSEia12INDia13TIMEiErrorDNIi;
1a
where:
SIZEnatural logarithm market value of equity;GROWTHpercentage change in sales;EISSUEpercentage change in common stock;LEVtotal liabilities divided by equity book value;DISSUE percentage change in total liabilities;TURNsales divided by total assets;CFannual net cash flow from operating activities divided by total assets;
AUDdummy variable that equals 1 if the firms auditor is PwC, KPMG, Arthur Andersen,Ernst & Young, or Deloitte Touche Tohmatsu, and 0 otherwise;
NUMEXnumber of exchanges on which a firms stock is listed;XLIST dummy variable that equals 1 if the firm is listed on any U.S. stock exchange, and
Worldscope indicates that the U.S. exchange is not the firms primary exchange;
CLOSE percentage of closely held shares of the firm as reported by Worldscope;IND dummy variables for industry fixed effects, classified using the two-digit Global
Industry Classification Standard (GICS) Codes; and
TIMEdummy variables for time (year) fixed effects.
The above regression is run separately for the pre-adoption and the post-adoption periods by using
the firm-year observations that have been pooled into the respective time periods (either the pre-
adoption or the post-adoption periods). This results in two sets of residuals being generated, and the
variance of the residuals is calculated for each respective group before being compared using a
variance ratio F-test.
To the extent that a variety of control variables have been included in the first measure to
account for the influence of other factors, the volatility of earnings may still be influenced by
TABLE 4
Reporting Years: The Pre-Adoption Period and the Post-Adoption Period
Firms with Fiscal
Year End of:
Pre-Adoption Period Post-Adoption Period
1st Year 2nd Year 3rd Year 4th Year 1st Year 2nd Year 3rd Year 4th Year
December 31 2001 2002 2003 2004 2005 2006 2007 2008
Post-December 31
(e.g., June 30)
2002 2003 2004 2005 2006 2007 2008 2009
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cash flow stream. When firms experience more volatile cash flows, then firms should also expect a
naturally more volatile net income. Therefore, the second earnings smoothing measure extends the
analysis of the first measure by benchmarking it against the volatility of cash flows. This involves
calculating the ratio of the variance of the changes in annual net income (DNI) to the variance of the
change in operating cash flows (DOCF).
Similar to the first measure, the volatility of cash flows is taken as the variance of the residuals
(DOCF#) (Equation (2)) from a regression of the change in operating cash flows (scaled by total
assets) (DOCF) (Equation (2a)):
Variability ofDNI#
Variability ofDOCF#
r2ErrorDNIi
r2ErrorDOCFi
; 2
where:
DNI#residuals from the regression ofDNIon the control variables (Equation (1a)); andDOCF#residuals from the regression ofDOCFon the control variables (Equation (2a)).
Equation (2a): Regression ofDOCFon the control variables:
DOCFi a0a1SIZEia2GROWTHi a3EISSUEia4LEVia5DISSUEi a6TURNia7CFia8AUDi a9NUMEXia10XLISTia11CLOSEia12INDia13TIMEiErrorDOCFi:
2a
Again, the above regression is run separately for the pre-adoption and the post-adoption periods by
using the firm-year observations that have been pooled into the respective time periods. This results
in two sets of residuals being generated for the change in operating cash flows (DOCF#), and the
variance of the residuals is calculated for each respective group before computing the ratio for thepre-adoption and the post-adoption periods.
Unlike the first earnings smoothing measure, there is no known formal statistical test to
compare the difference between the respective ratios of variances (DNI#/DOCF#) for the pre-
adoption and the post-adoption periods. As an alternative, we follow the methodology of Lang et al.
(2003) to test whether the ratio of variances is significantly less than 1 for each group respectively
using a variance ratio F-test.
Our third earnings smoothing measure is the Spearman correlation between accruals and cash
flows. It is expected that firms use accruals when they engage in earnings management, especially in
time of poor cash flows, to smooth cash flows variability. While there is naturally a negative correlation
between accruals (ACC) and cash flows (CF), prior studies argue that a larger magnitude of negativecorrelation between these variables is indicative of earnings smoothing, all else equal (Myers et al.
2007; Land and Lang 2002; Lang et al. 2003; Lang et al. 2006). Consistent with the previous two
measures, other factors could similarly influence cash flows (CF) and accruals (ACC). As a result, the
Spearman partial correlation between these two variables (Equation (3)) is determined based on the
residuals from regressions of cash flows and accruals (Equation (3a) and Equation (3b)) as follows:19
Spearman correlation between cash flowsCF#and accrualsACC#
CORRErrorCFi; ErrorACCi; 3
where:
19 Since one of the dependent variables used for this analysis is CF the same variable is now excluded as a control
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CF#residuals from the regression ofCFon the control variables (Equation (3a)); andACC#residuals from the regression ofACC on the control variables (Equation (3b)).
Equation (3a): Regression ofCFon the control variables:
CFi a0a1SIZEi a2GROWTHia3EISSUEia4LEVia5DISSUEia6TURNia7AUDia8NUMEXia9XLISTia10CLOSEia11INDia12TIMEiErrorCFi: 3a
Equation (3b): Regression ofACC on the control variables:
ACCi a0a1SIZEia2GROWTHia3EISSUEia4LEVia5DISSUEia6TURNia7AUDi a8NUMEXia9XLISTia10CLOSEia11INDi a12TIMEiErrorACCi;
3b
where ACCiNIi CFi.After obtaining the Spearman correlationsrhofor the pre-adoption and the post-adoption periods
respectively, the two Spearman correlationsrhoare then compared using a significance test suggested
by Sheskin (2004) to evaluate a change in the earnings smoothing behavior after IFRS adoption.
To examine earnings management from the perspective of managing earnings toward a positive
target, we pool all observations for the pre-adoption and the post-adoption periods to measure the
frequency of small positive earnings (SPOS). Following prior research, we use a dummy variable
for small positive earnings (SPOS) that sets to 1 for observations for which annual net income
(scaled by total assets) is between 0 and 0.01, and sets to 0 otherwise (Lang et al. 2003; Lang et al.
2006; Barth et al. 2008). We also modify the model by Barth et al. (2008), by swapping the binary
variable of POST with the binary variable of SPOS as the dependent variable for the logit
regression. We consider this modification to be more appropriate for this study because theAustralian adoption of IFRS was compulsory, and thus the variable POST is no longer
representative of an event that could be dependent on firms reporting small positive earnings (i.e.,
SPOS). Instead, this enables us to examine whether the probability of firms reporting small positive
earnings (SPOS) has changed after firms transited to IFRS (POST), together with the control
variables used in previous measures, by interpreting the coefficientb1 from a logit model.
Equation (4): Logit regression ofSPOS on POSTand the control variables:
SPOSi b0b1POSTi b2SIZEib3GROWTHib4EISSUEi b5LEVi b6DISSUEib7TURNib8CFib9AUDib10NUMEXib11XLISTib12CLOSEib13INDib14TIMEiErrori;
4
where:
POST dummy variable that equals 1 if observations are in the post-adoption period, and 0otherwise; and
SPOS dummy variable that equals 1 if net income scaled by total assets is between 0 and0.01, and 0 otherwise.
Timely Loss Recognition
Considering that prior studies often cite the reluctance of firms to recognize large losses in a
timely manner (Ball et al. 2003; Leuz et al. 2003; Lang et al. 2003; Lang et al. 2006; Barth et al.
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losses being reported, by using a dummy variable that sets to 1 for observations for which annual
net income (scaled by total assets) is less than 0.20, and sets to 0 otherwise (Leuz et al. 2003;
Lang et al. 2003; Lang et al. 2006; Barth et al. 2008). Having pooled all observations, we again
modify the timely loss recognition model used by Barth et al. (2008). We use the result for the
frequency of large losses (LNEG) as the dependent variable and estimate a logit regression on adummy variable for the post-adoption period (POST), together with the control variables. The
probability that the adopting firms report large losses differently between the pre-adoption and the
post-adoption periods is interpreted based on the coefficientk1.
Equation (5): Logit Regression ofLNEG on POSTand the Control Variables:
LNEGi k0k1POSTik2SIZEik3GROWTHik4EISSUEik5LEVik6DISSUEik7TURNik8CFik9AUDi k10NUMEXik11XLISTik12CLOSEik13INDik14TIMEiErrori;
5
where:
LNEGdummy variable that equals 1 if net income scaled by total assets is less than 0.20,
and 0 otherwise.
Value Relevance
As mentioned earlier, the preceding analyses focus mainly on the quality of accounting
information without much reference to market data. Considering that the introduction of IFRS has a
capital-market orientation, we employ three value relevance measures that are consistent with Barth
et al. (2008) to examine the association between accounting data and share price. All else equal,
firms with higher accounting quality are expected to have a higher association between share priceand accounting data.
Our first value relevance measure is based upon the explanatory power of the price regression.
To obtain the adjusted R2 that is controlled for industry and for time effects, we adopt the two-stage
regression technique used in Barth et al. (2008). We first obtain residuals from a regression of share
price (P) on industry and time (year) fixed effects, before regressing the residuals (P*) on net
income per share (NI/P) and book value of equity per share (BVEPS). To ensure that accounting
information has had sufficient time to be absorbed by the market, we measure share price three
months after the fiscal year-end.20
Equation (6): Regression ofP* on BVEPS and NIPS:
Pi d0d1BVEPSid2NIPSiErrori; 6
where:
Pshare price three months after the fiscal year-end date;
P* residuals from a regression ofP on industry and time (year) fixed effects;
BVEPSbook value of equity per share; and
NIPSnet income per share.
Consistent with other measures, the above regression is run separately for the pre-adoption and the
20This is in line with Section 319 of the Corporations Act 2001, which requires Australian listed corporations tolodge their financial reports within three months after the end of the fiscal year-end to the Australian Securities
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post-adoption periods by using the firm-year observations that have been pooled into the respective
time periods.
The second and third value relevance measures are based upon the explanatory power from a
Basu (1997) reverse return regression of net income per share (NI/P) on annual share price
returns. Consistent with prior research, we run separate regressions for firms with good news
(firms with non-negative annual share returns) and firms with bad news (firms with negative
annual share returns) (Basu 1997; Ball et al. 2000; Barth et al. 2008), while also similarly
controlling for industry and time (year) fixed effects as in the previous measure.
Equation (7): Regression of [NI/P]* on RETURN:
NI=Pi d0d1RETURNiErrori; 7
where:
NI/P net income per share divided by the beginning of fiscal year share price;[NI/P]*residuals from a regression ofNI/P on industry and time (year) fixed effects; and
RETURN shareholders total annual return from nine months before the fiscal year-end tothree months after the fiscal year-end.
The above regression is run separately for the pre-adoption and the post-adoption periods for both
good newsand bad newsfirms using the firm-year observations that have been pooled into the
respective time periods.
IV. RESULTS
Descriptive Statistics
Table 5 presents the descriptive statistics for both test variables and control variables across the
pre-adoption and the post-adoption periods. A comparison between the periods reveals that themean or median values across all continuous test variables are significantly different, with the
exception of accruals (ACC). This could possibly be explained by the economic downturn
experienced worldwide during the post-adoption period, therefore causing significant changes to the
test variables. It is interesting to note that the change in net income (DNI) was increasing (mean and
median are greater than 0) during the pre-adoption period, but the opposite trend is observed during
the post-adoption period (negative DNI). In addition, the shareholders return (RETURN) has
decreased tremendously from 32.05 percent (mean) during the pre-adoption period to 7.87 percent
(mean) after the adoption of IFRS. Without controlling for other factors, Table 5 indicates that the
sample firms experienced significant changes in variability (standard deviation) in the post-adoption
period than in the pre-adoption period. This could also partially reflect the uncertainty in the
economic environment faced by the sample firms during the economic crisis, which emphasizes the
need to incorporate control variables in the regression analyses.
In terms of control variables, Table 5 shows that the sample firms have grown significantly
larger (SIZE) after moving toward IFRS (both mean and median), despite showing insignificant
difference in the change in common stock (EISSUE) (both mean and median). Given that firms are
getting bigger (SIZE) but the level of common stock (EISSUE) remains relatively stable, it is not
surprising to find that the leverage ratio (LEV) and the percentage change in total liabilities
(DISSUE) have increased following IFRS adoption. Moreover, the adoption of IFRS increases the
likelihood that the sample firms are audited by one of the Big 4 auditors21 (AUD), possibly to
21Previously, the five largest auditing firms were known as the Big 5 auditors. With the collapse of ArthurAndersen the remaining four firms are collectively labeled as the Big 4 auditors Both expressions equally
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TABLE 5
Descriptive Statistics
Pre (n 688) Post (n 688)
Mean Median Std. Dev. Mean Median Std. Dev.
Test Variables
DNI 0.0060 0.0028 0.0794 0.0085*** 0.0026*** 0.0919***DOCF 0.0074 0.0035 0.0948 0.0007 0.0016* 0.0828***ACC 0.0332 0.0291 0.0787 0.0347 0.0234 0.0764CF 0.0866 0.0858 0.1296 0.0994** 0.0818 0.1113***
SPOS 0.0465 0.0000 0.2107 0.0698* 0.0000 0.2549
LNEG 0.0451 0.0000 0.2076 0.0378 0.0000 0.1908
P 5.5676 3.3950 5.8873 8.0574*** 4.1400*** 9.7041***
NI/P 0.0536 0.0590 0.1033 0.0383** 0.0594 0.1393***
BVEPS 2.2565 1.5340 2.1987 3.1783*** 1.8944*** 3.3482***NIPS 0.3016 0.1806 0.4369 0.4825*** 0.2615*** 0.7284***
RETURN 0.3205 0.2206 0.4770 0.0787*** 0.0490*** 0.3908***
Control Variables
SIZE 6.3523 6.0717 1.7512 6.9340*** 6.7380*** 1.6881
GROWTH 0.1931 0.1015 0.3929 0.1480** 0.0974 0.3337***
EISSUE 0.1920 0.0394 0.3650 0.2022 0.0356 0.4919***
LEV 1.6581 0.8716 2.7939 1.9260* 0.9859*** 3.1401***
DISSUE 0.2186 0.0634 0.5980 0.2556 0.1109** 0.6488**
TURN 0.8893 0.6769 0.7689 0.8361 0.7064 0.7013**
AUD 0.8372 1.0000 0.3694 0.8677 1.0000 0.3390
NUMEX 1.1831 1.0000 0.5696 1.1570 1.0000 0.5410
XLIST 0.0349 0.0000 0.1836 0.0349 0.0000 0.1836
CLOSE 0.3651 0.3744 0.2326 0.3453 0.3613 0.2299
*, **, *** Represent significant difference between the pre-adoption and the post-adoption periods at the 10 percent, 5percent, and 1 percent confidence levels, respectively (two-tailed).All continuous variables are winsorized at the 5 percent level.
Variable Definitions:
DNIchange in annual net income, where net income is scaled by end-of-year total assets;DOCF change in annual net cash flows from operating activities, where cash flows is scaled by end-of-year total assets;
ACCnet income less cash flow from operating activities, scaled by end-of-year total assets;CFannual net cash flow from operating activities divided by total assets;
SPOS dummy variable that equals 1 for observations for which annual net income scaled by total assets is between 0and 0.01, and 0 otherwise;
LNEGdummy variable that equals 1 for observations for which annual net income scaled by total assets is less than0.20, and 0 otherwise;
Pstock price three months after the fiscal year-end;NIPSnet income per share;BVEPSbook value of equity per share;NI/Pnet income per share divided by beginning of year price;RETURN shareholders total annual return from nine months before the fiscal year-end to three months after the fiscal
year-end;
SIZEnatural logarithm market value of equity;GROWTHpercentage change in sales;
EISSUEpercentage change in common stock;
LEV total liabilities divided by equity book value;DISSUEpercentage change in total liabilities;
(continued on next page)
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overcome the reporting complexity faced during the transition to new standards. Surprisingly, the
sample firms are, on average, listed on fewer stock exchanges (NUMEX) in the post-adoption
period (1.1570) than in the pre-adoption period (1.1837). Additionally, there is no change in terms
of firms listing on the U.S. stock exchanges22 (XLIST) before and after the adoption. These two
preliminary findings are contrary to the common argument that the use of IFRS facilitates access to
international capital markets (Jones and Higgins 2006).Table 6 provides a Spearman correlation matrix for the continuous variables, with correlations
for the pre-adoption period being shown in Panels A and B and the post-adoption period being
shown in Panels C and D. Overall, correlations between the variables in both periods are modest,
which suggests that multicollinearity is not a substantive issue. The only exception is correlation
between share price (P) and net income per share (NIPS), in which correlation between these two
variables is the highest in both the pre-adoption and the post-adoption periods, and is greater than
0.70. Furthermore, accruals (ACC) and cash flows (CF) are also found to be negatively correlated in
both the pre-adoption (0.55 significant at 1 percent) and the post-adoption periods (0.47
significant at 1 percent), which is consistent with the prior expectation that the negative correlation
reflects the natural outcome of accrual accounting (Leuz et al. 2003; Barth et al. 2008). In addition,three variablesincluding the change in net income (DNI), the change in cash flows (DOCF), as
well as cash flows (CF)are all positively correlated at the 1 percent significance level in both the
pre-adoption and the post-adoption periods. These positive relationships are also expected, given
that a firms reported earnings (e.g., DNI) should be reflective of its own cash flow stream (e.g.,
DOCFand CF).
Empirical Results
Earnings Management
In terms of earnings management, the results reported in Panel A of Table 7 are mostlyconsistent with our expectations that the adoption of IFRS in Australia had significantly impacted
accounting quality.
As emphasized earlier, the analyses for the first three earnings management measures focus on the
residuals from regressing each dependent variable on a specific set of control variables. Based on this
approach, a comparison of the residual variance (forDNI#) shows that the variability of the change in
net income is significantly higher in the post-adoption period (0.0072) than in the pre-adoption period
(0.0056), suggesting that income-smoothing behavior has reduced following IFRS adoption.
To further support the first finding, the second earnings management measure analyzes the
variability of the change in operating cash flows for both the pre-adoption and the post-adoption
TABLE 5 (continued)
TURNsales divided by total assets;AUD dummy variable that equals 1 if the firms auditor is PwC, KPMG, Arthur Andersen, Ernst & Young, or Deloitte
Touche Tohmatsu, and 0 otherwise;
NUMEXnumber of exchanges on which a firms stock is listed;XLIST dummy variable that equals 1 if the firm is listed on any U.S. stock exchange and Worldscope indicates that theU.S. exchange is not the firms primary exchange, and 0 otherwise; and
CLOSEpercentage of closely held shares of the firm as reported by Worldscope.
22 This can be interpreted from the variable XLIST because all sample firms with a listing on any U S stock
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TABLE 6
Spearman Correlation Matrix between Variables for the Pre-Adoption Period and the
Post-Adoption Period
Panel A: Pre-Adoption Period
DNI DOCF ACC CF P NI/P BVEPS NIPS
DNI 1.00
DOCF 0.42*** 1.00
ACC 0.10** 0.28*** 1.00CF 0.22*** 0.35*** 0.55*** 1.00
P 0.03 0.03 0.04 0.21*** 1.00NI/P 0.32*** 0.08** 0.23*** 0.19*** 0.06 1.00
BVEPS 0.05 0.04 0.01 0.06 0.59*** 0.25*** 1.00NIPS 0.22*** 0.03 0.14*** 0.25*** 0.70*** 0.51*** 0.66*** 1.00
RETURN 0.31*** 0.17*** 0.10** 0.20*** 0.05 0.10** 0.13*** 0.03SIZE 0.02 0.03 0.01 0.05 0.65*** 0.05 0.60*** 0.55***
GROWTH 0.18*** 0.10*** 0.07* 0.13*** 0.10** 0.18*** 0.03 0.17***EISSUE 0.10*** 0.12*** 0.10** 0.21*** 0.01 0.12*** 0.05 0.05
LEV 0.07* 0.01 0.08** 0.04 0.35*** 0.13*** 0.31*** 0.32***DISSUE 0.19*** 0.14*** 0.14*** 0.12*** 0.03 0.01 0.03 0.03
TURN 0.13*** 0.13*** 0.26*** 0.57*** 0.12*** 0.14*** 0.09** 0.13***NUMEX 0.03 0.00 0.07* 0.06 0.14*** 0.07* 0.07* 0.05
CLOSE 0.00 0.03 0.04 0.00 0.16*** 0.02 0.19*** 0.15***
Panel B: Pre-Adoption Period (continued)
RETURN SIZE GROWTH EISSUE LEV DISSUE TURN NUMEX CLOSE
RETURN 1.00
SIZE 0.14*** 1.00GROWTH 0.18*** 0.00 1.00
EISSUE 0.05 0.03 0.19*** 1.00LEV 0.00 0.37*** 0.00 0.01 1.00
DISSUE 0.06 0.02 0.36*** 0.31*** 0.06 1.00TURN 0.11*** 0.12*** 0.11*** 0.13*** 0.22*** 0.12*** 1.00
NUMEX 0.10** 0.29*** 0.15*** 0.03 0.07* 0.18*** 0.14*** 1.00CLOSE 0.03 0.21*** 0.02 0.09** 0.07* 0.00 0.02 0.05 1.00
*, **, *** Represent the 10 percent, 5 percent and 1 percent level of significance in two-tailed tests, respectively.
Panel C: Post-Adoption Period
DNI DOCF ACC CF P NI/P BVEPS NIPS
DNI 1.00
DOCF 0.35*** 1.00
ACC 0.18*** 0.28*** 1.00CF 0.20*** 0.27*** 0.47*** 1.00
P 0.13*** 0.01 0.09** 0.20*** 1.00
NI/P 0.28*** 0.04 0.30*** 0.24*** 0.07* 1.00
BVEPS 0.04 0.02 0.16*** 0.14*** 0.65*** 0.18*** 1.00NIPS 0.26*** 0.012 0.28*** 0.24*** 0.78*** 0.53*** 0.66*** 1.00
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periods to ascertain whether the observed increase in the volatility of income is also similarly found in
the volatility of cash flows. It is found that the ratio of the variance of the change in net income (DNI#)
to the variance of the change in operating cash flows (DOCF#) is substantially higher in the post-
adoption period (1.3250) than in the pre-adoption period (0.8070). Even without a statistical test to
determine whether the difference between the ratios is significant, a change in the ratio from less than
1 to greater than 1 further indicates that it is not a higher volatility of cash flows that drives the higher
earnings variability in the post-adoption period relative to the pre-adoption period. By analyzing the
ratio of variances for the respective periods, only the pre-adoption period has a ratio significantly lessthan 1 (at the 0.01 level). This again provides an indication that the variability of the change in net
income in the pre-adoption period is below the variability of the change in operating cash flows. All
these results together suggest that the smoother earnings stream observed when Australian GAAP
were being used is not a result of smoother cash flow stream but more likely by the effect of accruals,
and that the adoption of IFRS has subsequently reversed that practice.
The result for our third measure of correlation between accruals (ACC) and cash flows (CF)
shows that the correlation between these two variables has become less negative in the post-
adoption period (0.4499) than in the pre-adoption period (0.4553). This corresponds with the results
on the first two measures, although the difference is not significant, to suggest that earnings
smoothing has reduced following the adoption of IFRS.
While all the findings so far consistently support the notion that the adoption of IFRS has
TABLE 6 (continued)
DNI DOCF ACC CF P NI/P BVEPS NIPS
RETURN 0.29*** 0.16*** 0.04 0.20*** 0.28*** 0.06 0.03 0.18***SIZE 0.09** 0.01 0.07* 0.00 0.65*** 0.01 0.54*** 0.52***
GROWTH 0.16*** 0.18*** 0.02 0.16*** 0.20*** 0.09** 0.09** 0.18***EISSUE 0.05 0.05 0.02 0.16*** 0.06 0.10*** 0.08** 0.00
LEV 0.01 0.01 0.07* 0.16*** 0.27*** 0.01 0.20*** 0.20***DISSUE 0.06 0.14*** 0.15*** 0.04 0.09** 0.07* 0.00 0.15***
TURN 0.09** 0.07* 0.24*** 0.51*** 0.11*** 0.11*** 0.10*** 0.08**NUMEX 0.03 0.04 0.05 0.09** 0.05 0.08** 0.01 0.01
CLOSE 0.03 0.00 0.07* 0.05 0.13*** 0.02 0.19*** 0.13***
Panel D: Post-Adoption Period (continued)
RETURN SIZE GROWTH EISSUE LEV DISSUE TURN NUMEX CLOSE
RETURN 1.00SIZE 0.13*** 1.00
GROWTH 0.13*** 0.07* 1.00
EISSUE 0.02 0.11*** 0.22*** 1.00LEV 0.04 0.32*** 0.09** 0.08** 1.00
DISSUE 0.12*** 0.03 0.39*** 0.19*** 0.16*** 1.00
TURN 0.07* 0.18*** 0.14*** 0.06 0.12*** 0.08** 1.00NUMEX 0.03 0.19*** 0.08** 0.01 0.01 0.03 0.19*** 1.00
CLOSE 0.01 0.20*** 0.06 0.10*** 0.09** 0.06* 0.09** 0.01 1.00
*, **, *** Represent the 10 percent, 5 percent and 1 percent level of significance in two-tailed tests, respectively.
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TABLE 7
Accounting Quality Analysisa
Panel A: Earnings Management Metrics
Prediction
Pre
(n 688)Post
(n 688)
Eq. (1): Variability of DNI#b Post6 Pre 0.0056 0.0072***Eq. (2): Variability of DNI# overDOCF#
c,d,1 0.8070
1.3250
Eq. (3): Correlation of ACC# and CF#e Post6 Pre (0.4553) (0.4499)Eq. (4): Small positive net income (SPOS)f 6 0 1.8400***
Panel B: Timely Loss Recognition Metric
Prediction Pre (n 688)
Post
(n 688)
Eq. (5): Large negative net income (LNEG)g 6 0 2.0834***
Panel C: Value Relevance Metrics
Prediction Pre (n 688)Post
(n 688)
Eq. (6): Price modelh Post6 Pre 0.4827 0.5396***Eq. (7): Return model
i
Good news Post 6 Pre (0.0001) (0.0005)Bad news Post 6 Pre 0.0700 0.0869***
*** Represents significant difference between the pre-adoption and the post-adoption periods at the 1 percent confidencelevel (two-tailed). Significantly less than 1 at the 1 percent level (left-tailed).a We have not presented the full regression results in this paper, but would be happy to provide them upon request.b Variability ofDNI# is the variance of residuals from a regression of the change in annual net income (scaled by total
assets), DNI, on the control variables.c Variability ofDOCF# is the variance of residuals from a regression of the change in operating cash flows (scaled by
total assets), DOCF, on the control variables.d
Variability ofDNI#
overDOCF#
is the ratio ofDNI#
divided by DOCF#
.e Correlation ofACC# and CF# is the partial Spearman correlation between the residuals from accruals, ACC, and cash
flows, CF, regressions.f SPOS is a dummy variable that sets to 1 for observations for which the annual net income scaled by total assets isbetween 0 and 0.01, and sets to 0 otherwise. SPOS is regressed on a dummy variable (POST) that equals 1 forobservations in the post-adoption period, and 0 otherwise. The coefficient on POSTis tabulated.
g LNEGis a dummy variable that sets to 1 for observations for which the annual net income scaled by total assets is lessthan 0.20, and sets to 0 otherwise.LNEGis regressed on a dummy variable (POST) that equals 1 for observations inthe post-adoption period, and 0 otherwise. The coefficient on POSTis tabulated.
h Adjusted R2 is obtained from a two-stage regression of stock price (P), whereP is stock price as of three months afterthe fiscal year-end. In the first stage,P is regressed on industry and time (year) fixed effects to obtain the residual (P*).In the second stage, P* is regressed on book value of equity per share (BVEPS) and net income per share (NIPS).Adjusted R2 is tabulated.
iAdjusted R
2is obtained from a two-stage regression for good/bad news. Good (bad) news observations represent those
for which RETURNis non-negative (negative), where RETURNis shareholders total annual return from nine months
before the fiscal year-end to three months after the fiscal year-end. NI/P is first regressed on industry and time (year)fixed effects, where NI/P is net income per share divided by beginning of year price. In the second stage, the residual([NI/P]*) from the first-stage regression is regressed on RETURN. Adjusted R2 is tabulated separately for good and badnews subsamples
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logit regression of small positive income (SPOS),23 1.84, indicates that there is a significant
difference in terms of firms managing earnings toward a positive target across the pre-adoption and
the post-adoption periods. This finding is found to be inconsistent with Barth et al. (2008), as they
find that the IFRS adopting firms exhibit no significant difference in terms of managing earnings
toward a positive target from those firms that do not adopt IFRS, as well as across time when theseadopting firms moved from local standards to IFRS voluntarily. Further analysis of our sample
reveals that the logit regression result is mainly driven by financial firms during the post-adoption
period. Once financial firms are removed from the sample, the subsample results are consistent with
the findings in Barth et al. (2008).24
Overall, the findings for earnings management metrics provide support that the mandatory
adoption of IFRS has generally improved accounting quality in Australia, especially in the form of
less earnings smoothing behavior.25
Timely Loss Recognition
As shown in Panel B of Table 7, the timely loss recognition measure has a significantly
positive coefficient for the variable POSTfrom the logit regression,26 2.0834. This result indicates
that there is a higher probability that large losses are being reported in a timely manner by the
adopting firms in the post-adoption period than in the pre-adoption period. Consistent with the
aforementioned findings for earnings management metrics, this again suggests that there is an
improvement in accounting quality after the mandatory adoption of IFRS in Australia.
Value Relevance
In terms of the value relevance tests, the results are reported in Panel C of Table 7. The
adjusted R2
for the price model has increased from 48.27 percent in the pre-adoption period to53.96 percent in the post-adoption period.27 For the return model, there is also an increase in the
association between accounting income and the report of bad news. Both findings provide
evidence that the value relevance of accounting data has improved after IFRS adoption, which is in
line with the finding of timely recognition of losses.
Overall, results for all accounting quality metrics are consistent with our expectations that the
adoption of IFRS in Australia had significantly impacted accounting quality.
Sensitivity Analyses
Excluding Reporting Years during the Transition Period
One concern with the preceding analysis is that the uncertainty surrounding the transition from
Australian GAAP to IFRS may influence the differences in accounting quality. In particular, there is
a possibility that firms may experience significant adjustments due to the uncertainty effect during
23 The statistical significance of the SPOS measure is the same by using a probit regression (untabulated).24 The subsample results are discussed in the sensitivity analyses section.25
Although we do not attempt to test the presence of earnings management during the transition period like thatdone by Capkun et al. (2008) because this is not the objective of our research, our similar conclusions from thesensitivity analysis suggest that our results are not significantly affected by the transition uncertainty. Moreover,Capkun et al. (2008) also find that transition earnings management is less pronounced in countries with stronger
legal enforcement. This further suggests that this issue is of less concern to a country that has a well-establishedlegal institution, such as Australia.
26The statistical significance of the LNEG measure is the same by using a probit regression (untabulated)
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the transitory reporting years immediately before and after the adoption and so may consequently
affect the results obtained earlier (Jeanjean and Stolowy 2008).
To address this concern, we replicate the analysis by excluding two transitory reporting years
from the sampling period, which are represented by the last reporting year under Australian GAAP
and the first reporting year under IFRS. This means that the analysis includes only three reportingyears before and after the transition respectively, with the results being reported in Table 8.
Overall, conclusions based on this sensitivity analysis are similar to the earlier discussions.
Specifically, most of the measures provide some support for the adoption of IFRS given that
accounting quality has improved during the post-adoption period. Particularly, the difference in
ratio of the variability of the change in net income (DNI) to the variability of the change in
operating cash flows (DOCF) has increased significantly from the pre-adoption period to the post-
adoption period. Moreover, for the price model, the adjusted R2 has increased from 45.57 percent in
the pre-adoption period to 55.20 percent in the post-adoption period.28 This indicates an
improvement in the strength of the relationship between accounting data and stock price.
Excluding Financial Firms
Another potential concern with our analyses is that the regulatory environment of financial firms is
significantly different from non-financial firms and so may potentially influence our results. Several
studies on IFRS adoption also excluded financial firms from their sample (e.g., Barth et al. 2008;
Christensen et al. 2008; Goodwin et al. 2008a; Paananen and Lin 2009) for the reason that the financial
industry is arguably more influenced by its own industry-specific factors and therefore potentially not
homogeneous with other industries (Jeanjean and Stolowy 2008). Although we already attempted to
control for industry-specific factors by including the industry fixed effects in all regressions, this
concern is also further addressed by repeating the analysis on only non-financial sample firms.
Based on the results shown in Table 9, non-financial firms still exhibit less earnings smoothing,an improvement in reporting large negative losses timely and stronger association between
accounting data and market-based data. Contrary to our main results, there is no significant
difference in terms of managing earnings toward a positive target ( SPOS) for non-financial firms
after the adoption of IFRS. This result is consistent with the findings in Barth et al. (2008). In
addition, the price model and the return model (bad news) do provide clearer evidence that there
are greater associations between financial statement information and market-based data after IFRS
adoption. In particular, the adjusted R2 for bad newshas increased from 3.48 percent in the pre-
adoption period to 13.03 percent in the post-adoption period. This implies that non-financial firms
report negative earnings in a more timely manner after IFRS adoption, which is consistent with the
finding of a timely loss recognition measure.
29
V. CONCLUSION
This paper examines the impact of mandating IFRS adoption on accounting quality in
Australia. Specifically, we compare whether there is a change in terms of earnings management,
timely loss recognition, and value relevance of accounting information before and after the
mandatory implementation of IFRS as of January 1, 2005, for a period of four years.
28The F-statistic has increased in the post-adoption period as well (untabulated).
29Although our empirical tests do not directly analyze the impact of the Global Financial Crisis (GFC) on theadoption of IFRS for accounting quality, these sensitivity analyses provide consistent findings to suggest that ourconclusion on the change in accounting quality is likely to be attributable to the mandatory adoption of IFRS.Furthermore Australia is among the few developed countries that are least affected by the GFC The Australian
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TABLE 8
Sensitivity AnalysisExcluding Reporting Years during the Transition Perioda
Panel A: Earnings Management Metrics
Prediction
Pre
(n 516)Post
(n 516)
Eq. (1): Variability of DNI#b Post6 Pre 0.0057 0.0074***Eq. (2): Variability of DNI# overDOCF#
c,d, 1 0.7810
1.3427
Eq. (3): Correlation of ACC# and CF#e Post6 Pre (0.4607) (0.4249)Eq. (4): Small positive net income (SPOS)f 6 0 1.3951**
Panel B: Timely Loss Recognition Metric
Prediction
Pre
(n 516)
Post
(n 516)
Eq. (5): Large negative net income (LNEG)g 6 0 2.0453**
Panel C: Value Relevance Metrics
Prediction
Pre
(n 516)Post
(n 516)
Eq. (6): Price modelh
Post6 Pre 0.4557 0.5520***Eq. (7): Return modeli
Good news Post 6 Pre (0.0010) (0.0033)Bad news Post 6 Pre 0.0309 0.0835***
**, *** Represent significant difference between the pre-adoption and the post-adoption periods at the 5 percent and 1percent confidence levels, respectively (two-tailed). Significantly less than 1 at the 1 percent level (left-tailed).a We have not presented the full regression results in this paper, but would be happy to provide them upon request.b Variability ofDNI# is the variance of residuals from a regression of the change in annual net income (scaled by total
assets), DNI, on the control variables.c Variability ofDOCF# is the variance of residuals from a regression of the change in operating cash flows (scaled by
total assets), DOCF, on the control variables.d
Variability ofDNI#
overDOCF#
is the ratio ofDNI#
divided by DOCF#
.e Correlation ofACC# and CF# is the partial Spearman correlation between the residuals from accruals, ACC, and cash
flows, CF, regressions.f SPOS is a dummy variable that sets to 1 for observations for which the annual net income scaled by total assets isbetween 0 and 0.01, and sets to 0 otherwise. SPOS is regressed on a dummy variable (POST) that equals 1 forobservations in the post-adoption period, and 0 otherwise. The coefficient on POSTis tabulated.
g LNEGis a dummy variable that sets to 1 for observations for which the annual net income scaled by total assets is lessthan 0.20, and sets to 0 otherwise. LNEGis regressed on a dummy variable (POST) that equals 1 for observations inthe post-adoption period, and 0 otherwise. The coefficient on POSTis tabulated.
h Adjusted R2 is obtained from a two-stage regression of stock price (P), whereP is stock price as of three months afterthe fiscal year-end. In the first stage,P is regressed on industry and time (year) fixed effects to obtain the residual (P*).In the second stage, P* is regressed on book value of equity per share (BVEPS) and net income per share (NIPS).Adjusted R2 is tabulated.
iAdjusted R
2is obtained from a two-stage regression for good/bad news. Good (bad) news observations represent those
for which RETURNis non-negative (negative), where RETURNis shareholders total annual return from nine months
before the fiscal year-end to three months after the fiscal year-end. NI/P is first regressed on industry and time (year)fixed effects, where NI/P is net income per share divided by beginning of year price. In the second stage, the residual([NI/P]*) from the first-stage regression is regressed on RETURN. Adjusted R2 is tabulated separately for good and badnews subsamples
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TABLE 9
Sensitivity AnalysisExcluding Financial Firmsa
Panel A: Earnings Management Metrics
Prediction
Pre
(n 528)Post
(n 528)
Eq. (1): Variability of DNI#b Post6 Pre 0.0065 0.0077*Eq. (2): Variability of DNI# overDOCF#
c,d, 1 0.8538
1.3312
Eq. (3): Correlation of ACC# and CF#e Post6 Pre (0.4218) (0.4124)Eq. (4): Small positive net income (SPOS)f 6 0 1.1521
Panel B: Timely Loss Recognition Metric
Prediction
Pre
(n 528)
Post
(n 528)
Eq. (5): Large negative net income (LNEG)g 6 0 1.8605*
Panel C: Value Relevance Metrics
Prediction
Pre
(n 528)Post
(n 528)
Eq. (6): Price modelh
Post6 Pre 0.4149 0.5451***Eq. (7): Return modeli
Good news Post 6 Pre (0.0021) 0.0011Bad news Post 6 Pre 0.0348 0.1303***
*, *** Represent significant difference between the pre-adoption and the post-adoption periods at the 10 percent and 1percent confidence levels, respectively (two-tailed).
Significantly less than 1 at the 5 percent level (left-tailed).a We have not presented the full regression results in this paper, but would be happy to provide them upon request.b Variability ofDNI# is the variance of residuals from a regression of the change in annual net income (scaled by total
assets), DNI, on the control variables.c Variability ofDOCF# is the variance of residuals from a regression of the change in operating cash flows (scaled by
total assets), DOCF, on the control variables.d
Variability ofDNI#
overDOCF#
is the ratio ofDNI#
divided by DOCF#
.e Correlation ofACC# and CF# is the partial Spearman correlation between the residuals from accruals, ACC, and cash
flows, CF, regressions.f SPOS is a dummy variable that sets to 1 for observations for which the annual net income scaled by total assets isbetween 0 and 0.01, and sets to 0 otherwise. SPOS is regressed on a dummy variable (POST) that equals 1 forobservations in the post-adoption period, and 0 otherwise. The coefficient on POSTis tabulated.
g LNEGis a dummy variable that sets to 1 for observations for which the annual net income scaled by total assets is lessthan 0.20, and sets to 0 otherwise.LNEGis regressed on a dummy variable (POST) that equals 1 for observations inthe post-adoption period, and 0 otherwise. The coefficient on POSTis tabulated.
h Adjusted R2 is obtained from a two-stage regression of stock price (P), whereP is stock price as of three months afterthe fiscal year-end. In the first stage,P is regressed on industry and time (year) fixed effects to obtain the residual (P*).In the second stage, P* is regressed on book value of equity per share (BVEPS) and net income per share (NIPS).Adjusted R2 is tabulated.
iAdjusted R
2is obtained from a two-stage regression for good/bad news. Good (bad) news observations represent those
for whichRETURNis non-negative (negative), whereRETURNis shareholders total annual return from nine months
before the fiscal year-end to three months after the fiscal year-end. NI/P is first regressed on industry and time (year)fixed effects, where NI/Pis net income per share divided by beginning of year price. In the second stage, the residual([NI/P]*) from the first-stage regression is regressed onRETURN. Adjusted R2 is tabulated separately for good and badnews subsamples
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After controlling for other confounding factors, our results indicate that subsequent to IFRS being
implemented, the adopting firms exhibit less earnings management by way of income smoothing, better
timely loss recognition, and stronger association between accounting information and market-based
data. The results are even more prevalent when financial firms are excluded from the analysis.
The overall findings suggest that there has been an improvement to accounting quality afterAustralian listed firms moved from Australian GAAP to IFRS. This supports the FRCs expectation
that the adoption by Australia should enhance the overall quality of the financial reporting system,
which is also of great interest to the IASB and other countries that are moving toward to IFRS. In
particular, we provide more comparable evidence to other non-EU adopting countries that are
similarly not motivated by the EU harmonization efforts in implementing the mandate to adopt
IFRS. Our findings are also more robust as we examine accounting quality without the presence of
voluntary IFRS adopters, by utilizing multiple measures and a longer information window than
prior Australian studies.
Nevertheless, our study is not free from its limitations. As in the case of many prior studies on
accounting quality, we cannot ascertain whether our accounting quality metrics absolutely measureaccounting quality per se. This is due to the fact that accounting quality is a multi-dimensional
concept, and so some accounting quality metrics may be used to address multiple attributes of
accounting quality, yet provide different interpretations.30 To overcome this limitation, we rely on a
wide range of empirical measures to strengthen the validity of our inferences. In addition, there is
no definitive way to determine that our results capture only the effects of the mandatory adoption of
IFRS and not observing differences in other factors.31 We attempt to mitigate the confounding
effects of other factors, by first using the same set of sample firms as a control group, as well as
including a number of control variables that have been identified by prior studies.
To the extent that our results provide supporting evidence for the adoption of IFRS, we
acknowledge that there is still scope for future research to expand on our study. For example, future
research can explore the reasons why IFRS adoption improves accounting quality, especially by
narrowing down the cause to specific accounting standards.32 Furthermore, the IASB continuously
carries out improvement projects to meet the fast-changing economic environment, and so from
time to time IFRS standards are being revised. This also provides invaluable opportunities for future
research to closely monitor the effects of adopting IFRS on accounting quality at different phases.
Together, the findings would be of interest to the IASB, as well as to countries that have either
mandated or are in the process of mandating the adoption of IFRS.
REFERENCES
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30For example, while this research interprets earnings smoothing as indicative of earnings management (i.e., lowaccounting quality), other research may use the same metric to indicate high predictability to investors throughthe incorporation of additional information by managers via smoothed earnings (i.e., high accounting quality)(Francis et al. 2006; Ewert and Wagenhofer 2010). We thank our anonymous reviewer for highlighting thispoint.
31 Accounting quality represents the outcome of the financial reporting system, therefore it can be sensitive to theeffects attributable to the financial reporting systems, as well as those unattributable to the financial reportingsystem such as the economic environment and reporting incentives (Barth et al 2008)
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