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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

    Armstrong, C. S., M. E. Barth, A. D. Jagolinzer, and E. J. Riedl. 2010. Market reaction to the adoption of

    IFRS in Europe. Accounting Review 85: 3161.

    Ashbaugh, H., and M. Pincus. 2001. Domestic accounting standards, international accounting standards,

    and the predictability of earnings. Journal of Accounting Research 39: 417434.

    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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    Australian Securities Exchange (ASX). 2011. Glossary. Available at: http://www.asx.com.au/glossary/

    items/all_ordinaries_all_ords.htm

    Ball, R. 2006. International Financial Reporting Standards (IFRS): Pros and cons for investors. Accounting

    and Business Research 36: 527.

    Ball, R., S. P. Kothari, and A. Robin. 2000. The effect of international institutional factors on pro


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