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JOURNAL OF INTERNATIONAL ACCOUNTING RESEARCH American Accounting AssociationVol. 11, No. 1 DOI: 10.2308/jiar-102122012pp. 119–146

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 of adoption 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, Grant Richardson, 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 IASB’s 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



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 their adoption 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 of standards 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 EU’s 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

120 Chua, Cheong, and Gould



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 world’s 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 et al. (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 lower quality 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

The Impact of Mandatory IFRS Adoption on Accounting Quality 121



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 ‘‘best ’’ disclosure 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




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




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




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, or vice 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

the Corporations 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 last financial 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 under Australian 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)




in the post-adoption period for comparison. Secondly, a matched sample using benchmark firms

from other countries that either preclude or have not mandated the use of IFRS introduces country-

level differences. Additionally, it is not plausible to identify a country that possesses similar uniquefeatures like Australia in terms of its institutional framework but is yet to adopt IFRS. Hence,

constructing a sample using the same firms as well as standardizing the firm-year observations in

both the pre-adoption and the post-adoption periods would make it more likely that any change

observed in accounting quality is attributable to the adoption of IFRS. At the same time, these

requirements also control for firm-specific factors.

Table 2 presents the sample industry breakdown that has been categorized based on the Global

Industry Classification Standard (GICS) Sector Codes (two-digit). As shown in the table, the

sample firms are spread across a wide range of industries, with most in financials, industrials,

consumer discretionary, and materials.

Consistent with the EU, the Australian transition to IFRS was made effective from January 1,

2005, and therefore would have expected the first IFRS accounts in the fiscal year-ended December

TABLE 1

Sample Selection

No. of Firms Total

Initial sample (All Ordinaries) as of June 30, 2004 (pre-adoption period)a 496

Initial sample (All Ordinaries) as of June 30, 2009 (post-adoption period)b 493

Firms included in All Ordinaries as of both June 30, 2004, and June 30, 2009 249

Less: Firms that are eliminated due to missing data from 2001 to 2009c 52

Less: Firms that are exempted from the Australian mandatory IFRS adoptiond 11

Less: Firms that changed their fiscal year-end during 2001 to 2009e 14

Final Sample: Number of Firms 172

Number of Reporting Years

(1) Pre-Adoption Period 4 years

(2) Post-Adoption Period 4 years

Total Reporting Years 8 years

Final Sample: Number of Firm-Year Observations 1,376

a,bThe ASX defines All Ordinaries (All Ords) as ‘‘capitalization weighted index of performance of share prices of about 500 of the largest Australian companies.’’ The constituent list for All Ordinaries Index includes only the eligible

companies listed on the ASX and thus may not have exactly 500 companies at a particular date (ASX 2011).c

Most firms are excluded from the sample due to the unavailability of data for closely held shares (CLOSE ). Given that this information is not readily available from the published annual reports, these missing values cannot be obtained

feasibly from other sources (for example, Connect4). The exclusion of these firms from the sample is unlikely toaffect the conclusions of this study on the basis that the remaining firm-year observations are still sufficient toconstruct a large sample (. 100).

d Firms are exempted from the Australian mandatory adoption of IFRS because they are either dual listed on the ASX(for example, Rio Tinto Limited) or incorporated outside Australia (for example, Singapore Telecom Ltd.).Therefore, they have not prepared their financial reports under Australian GAAP (in the pre-adoption period) and theAustralian equivalent of IFRS (in the post-adoption period) to enable a comparison in this study. This elimination isalso similarly done by Kvaal and Nobes (2010).

eFirms do not prepare financial statements for a fiscal year of 12 months in the first year of changing their fiscal year-end. For the purpose of determining annual return for the value relevance metrics, these firms are excluded from thesample to standardize comparison between periods.




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%

GICS ¼ Global 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 Vincent 2003; 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




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

(D NI ), the mean ratio of the variability of the change in net income (D NI ) 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) (D NI ). 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) (D NI ) 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 2001–2008

Post-December 31 149 596 86.63% Year 2006 2002–2009

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




Variability of D NI # ¼ r2 Error ðD NI Þi; ð1Þ

where:

D NI # ¼ residuals from the regression of D NI on the control variables (Equation (1a)).

Equation (1a): Regression of D NI on the control variables:

D NI i ¼ a0 þ a1SIZE i þ a2GROWTH i þ a3 EISSUE i þ a4 LEV i þ a5 DISSUE i þ a6TURN iþ a7CF i þ a8 AUDi þ a9 NUMEX i þ a10 XLIST i þ a11CLOSE i þ a12 INDi þ a13TIME iþ Error ðD NI Þi;

ð1a Þ

where:

SIZE ¼ natural logarithm market value of equity;GROWTH ¼ percentage change in sales;

EISSUE ¼ percentage change in common stock;

LEV ¼ total liabilities divided by equity book value;

DISSUE ¼ percentage change in total liabilities;

TURN ¼ sales divided by total assets;

CF ¼ annual net cash flow from operating activities divided by total assets;

AUD ¼ dummy variable that equals 1 if the firm’s auditor is PwC, KPMG, Arthur Andersen,

Ernst & Young, or Deloitte Touche Tohmatsu, and 0 otherwise;

NUMEX ¼ number of exchanges on which a firm’s stock is listed;

XLIST ¼ 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 firm’s primary exchange;

CLOSE ¼ percentage of closely held shares of the firm as reported by Worldscope;

IND ¼ dummy variables for industry fixed effects, classified using the two-digit Global

Industry Classification Standard (GICS) Codes; and

TIME ¼ dummy 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




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 (D NI ) 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 of D NI #

Variability of DOCF # ¼

r2 Error ðD NI Þi

r2 Error ðDOCF Þi

; ð2Þ

where:

D NI # ¼ residuals from the regression of D NI on the control variables (Equation (1a)); and

DOCF # ¼ residuals from the regression of DOCF on the control variables (Equation (2a)).

Equation (2a): Regression of DOCF on the control variables:

DOCF i ¼ a0 þ a1SIZE i þ a2GROWTH i þ a3 EISSUE i þ a4 LEV i þ a5 DISSUE i þ a6TURN iþ a7CF i þ a8 AUDi þ a9 NUMEX i þ a10 XLIST i þ a11CLOSE i þ a12 INDi

þ a13TIME i þ Error ðDOCF Þi:

ð2a Þ

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 (D NI # / 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 flows CF # and accruals ACC #

¼ CORR ðError ðCF Þi; Error ð ACCÞiÞ; ð3Þ

where:

19 Since one of the dependent variables used for this analysis is CF the same variable is now excluded as a control




CF # ¼ residuals from the regression of CF on the control variables (Equation (3a)); and

ACC # ¼ residuals from the regression of ACC on the control variables (Equation (3b)).

Equation (3a): Regression of CF on the control variables:

CF i ¼ a0 þ a1SIZE i þ a2GROWTH i þ a3 EISSUE i þ a4 LEV i þ a5 DISSUE i þ a6TURN iþ a7 AUDi þ a8 NUMEX i þ a9 XLIST i þ a10CLOSE i þ a11 INDi þ a12TIME iþ Error ðCF Þi: ð3a Þ

Equation (3b): Regression of ACC on the control variables:

ACCi ¼ a0 þ a1SIZE i þ a2GROWTH i þ a3 EISSUE i þ a4 LEV i þ a5 DISSUE i þ a6TURN iþ a7 AUDi þ a8 NUMEX i þ a9 XLIST i þ a10CLOSE i þ a11 INDi þ a12TIME iþ Error ð ACCÞi;

ð3bÞ

where ACCi ¼ NI i – CF i.

After obtaining the Spearman correlations rho for the pre-adoption and the post-adoption periods

respectively, the two Spearman correlations rho are 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 coefficient b1 from a logit model.

Equation (4): Logit regression of SPOS on POST and the control variables:

SPOSi ¼ b0 þ b1 POST i þ b2SIZE i þ b3GROWTH i þ b4 EISSUE i þ b5 LEV i þ b6 DISSUE iþ b7TURN i þ b8CF i þ b9 AUDi þ b10 NUMEX i þ b11 XLIST i þ b12CLOSE i

þ b13 INDi þ b14TIME i þ Error i;

ð4Þ

where:

POST ¼ dummy variable that equals 1 if observations are in the post-adoption period, and 0

otherwise; and

SPOS ¼ dummy variable that equals 1 if net income scaled by total assets is between 0 and

0.01, 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.




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 a dummy 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 coefficient k1.

Equation (5): Logit Regression of LNEG on POST and the Control Variables:

LNEGi ¼ k0 þ k1 POST i þ k2SIZE i þ k3GROWTH i þ k4 EISSUE i þ k5 LEV i þ k6 DISSUE iþ k7TURN i þ k8CF i þ k9 AUDi þ k10 NUMEX i þ k11 XLIST i þ k12CLOSE iþ k13 INDi þ k14TIME i þ Error i;

ð5Þ

where:

LNEG ¼ dummy 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 of P* on BVEPS and NIPS:

Pi ¼ d0 þ d1 BVEPSi þ d2 NIPSi þ Error i; ð6Þ

where:

P ¼ share price three months after the fiscal year-end date;

P* ¼ residuals from a regression of P on industry and time (year) fixed effects;

BVEPS ¼ book value of equity per share; and

NIPS ¼ net 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




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 :

½ NI = Pi ¼ d0 þ d1 RETURN i þ Error i; ð7Þ

where:

NI/P ¼ net income per share divided by the beginning of fiscal year share price;

[ NI/P]* ¼ residuals from a regression of NI / P on industry and time (year) fixed effects; and

RETURN ¼ shareholders’ total annual return from nine months before the fiscal year-end to

three months after the fiscal year-end.

The above regression is run separately for the pre-adoption and the post-adoption periods for both

‘‘good news’’ and ‘‘bad news’’ firms 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 (D NI ) 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 D NI ). 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 Arthur Andersen the remaining four firms are collectively labeled as the Big 4 auditors Both expressions equally




TABLE 5

Descriptive Statistics

Pre (n ¼ 688) Post (n ¼ 688)

Mean Median Std. Dev. Mean Median Std. Dev.

Test Variables

D NI 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 0.0764

CF 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:

D NI ¼ change in annual net income, where net income is scaled by end-of-year total assets;DOCF ¼ change in annual net cash flows from operating activities, where cash flows is scaled by end-of-year total assets;

ACC ¼ net income less cash flow from operating activities, scaled by end-of-year total assets;

CF ¼ annual net cash flow from operating activities divided by total assets;

SPOS ¼ dummy variable that equals 1 for observations for which annual net income scaled by total assets is between 0and 0.01, and 0 otherwise;

LNEG ¼ dummy variable that equals 1 for observations for which annual net income scaled by total assets is less than0.20, and 0 otherwise;

P ¼ stock price three months after the fiscal year-end; NIPS ¼ net income per share; BVEPS ¼ book value of equity per share;

NI/P ¼ net income per share divided by beginning of year price; RETURN ¼ shareholders’ total annual return from nine months before the fiscal year-end to three months after the fiscal

year-end;

SIZE ¼ natural logarithm market value of equity;GROWTH ¼ percentage change in sales;

EISSUE ¼ percentage change in common stock;

LEV ¼ total liabilities divided by equity book value; DISSUE ¼ percentage change in total liabilities;

(continued on next page)




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 variables—including the change in net income (D NI ), 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 firm’s reported earnings (e.g., D NI ) should be reflective of its own cash flow stream (e.g.,

DOCF and 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 (for D NI # ) 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)

TURN ¼ sales divided by total assets; AUD¼ dummy variable that equals 1 if the firm’s auditor is PwC, KPMG, Arthur Andersen, Ernst & Young, or Deloitte

Touche Tohmatsu, and 0 otherwise;

NUMEX ¼ number of exchanges on which a firm’s stock is listed; XLIST ¼ 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 firm’s primary exchange, and 0 otherwise; andCLOSE ¼ percentage 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




TABLE 6

Spearman Correlation Matrix between Variables for the Pre-Adoption Period and the

Post-Adoption Period

Panel A: Pre-Adoption Period

D NI DOCF ACC CF P NI/P BVEPS NIPS

D NI 1.00

DOCF 0.42*** 1.00

ACC 0.10** 0.28*** 1.00

CF 0.22*** 0.35*** 0.55*** 1.00

P 0.03 0.03 0.04 0.21*** 1.00

NI/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*** 1.00

NIPS 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*** 1.00

GROWTH 0.18*** 0.00 1.00

EISSUE 0.05 0.03 0.19*** 1.00

LEV 0.00 0.37*** 0.00 0.01 1.00

DISSUE 0.06 0.02 0.36*** 0.31*** 0.06 1.00

TURN 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*** 1.00

CLOSE 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


D NI 1.00

DOCF 0.35*** 1.00

ACC 0.18*** 0.28*** 1.00

CF 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*** 1.00

NIPS 0.26*** 0.012 0.28*** 0.24*** 0.78*** 0.53*** 0.66*** 1.00




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 (D NI # )

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)


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*** 1.00

LEV 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** 1.00

NUMEX 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.




TABLE 7

Accounting Quality Analysisa

Panel A: Earnings Management Metrics

Prediction

Pre

(n ¼ 688)

Post

(n ¼ 688)

Eq. (1): Variability of D NI # b Post 6¼ Pre 0.0056 0.0072***

Eq. (2): Variability of D NI # over DOCF # c,d,1 0.8070

1.3250

Eq. (3): Correlation of ACC # and CF #e Post 6¼ Pre (0.4553) (0.4499)

Eq. (4): Small positive net income (SPOS)f 6¼ 0 1.8400***

Panel B: Timely Loss Recognition Metric

Prediction Pre (n ¼ 688)

Post

(n ¼ 688)

Eq. (5): Large negative net income ( LNEG)g 6¼ 0 2.0834***

Panel C: Value Relevance Metrics

Prediction Pre (n ¼ 688)

Post

(n ¼ 688)

Eq. (6): Price modelh Post 6¼ Pre 0.4827 0.5396***

Eq. (7): Return modeli

Good news Post 6¼ Pre (0.0001) (0.0005)

Bad news Post 6¼ 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 of D NI # is the variance of residuals from a regression of the change in annual net income (scaled by total

assets), D NI , on the control variables.c Variability of DOCF # 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


over DOCF #

is the ratio of D NI #

divided by DOCF #

.e Correlation of ACC # 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 for

observations in the post-adoption period, and 0 otherwise. The coefficient on POST is tabulated.g LNEG is a dummy variable that sets to 1 for observations for which the annual net income scaled by total assets is less

than 0.20, and sets to 0 otherwise. LNEG is regressed on a dummy variable ( POST ) that equals 1 for observations in

the post-adoption period, and 0 otherwise. The coefficient on POST is tabulated.h Adjusted R2 is obtained from a two-stage regression of stock price ( P), where P is stock price as of three months after

the 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 RETURN is non-negative (negative), where RETURN is 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 bad

news subsamples




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 POST from 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 that done 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)




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 (D NI ) 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 news’’ has 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 our conclusion 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




TABLE 8

Sensitivity Analysis—Excluding Reporting Years during the Transition Perioda


Prediction

Pre

(n ¼ 516)

Post

(n ¼ 516)

Eq. (1): Variability of D NI # b Post 6¼ Pre 0.0057 0.0074***

Eq. (2): Variability of D NI # over DOCF # c,d, 1 0.7810

1.3427


Eq. (4): Small positive net income (SPOS)f 6¼ 0 1.3951**


Prediction

Pre

(n ¼ 516)

Post

(n ¼ 516)

Eq. (5): Large negative net income ( LNEG)g 6¼ 0 2.0453**


Prediction

Pre

(n ¼ 516)

Post

(n ¼ 516)

Eq. (6): Price modelh

Post 6¼ Pre 0.4557 0.5520***


Good news Post 6¼ Pre (0.0010) (0.0033)

Bad news Post 6¼ 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 of D NI # is the variance of residuals from a regression of the change in annual net income (scaled by total




over DOCF #


divided by DOCF #








iAdjusted R




news subsamples




TABLE 9

Sensitivity Analysis—Excluding Financial Firmsa


Prediction

Pre

(n ¼ 528)

Post

(n ¼ 528)

Eq. (1): Variability of D NI # b Post 6¼ Pre 0.0065 0.0077*

Eq. (2): Variability of D NI # over DOCF # c,d, 1 0.8538

1.3312


Eq. (4): Small positive net income (SPOS)f 6¼ 0 1.1521


Prediction

Pre

(n ¼ 528)

Post

(n ¼ 528)

Eq. (5): Large negative net income ( LNEG)g 6¼ 0 1.8605*


Prediction

Pre

(n ¼ 528)

Post

(n ¼ 528)

Eq. (6): Price modelh

Post 6¼ Pre 0.4149 0.5451***


Good news Post 6¼ Pre (0.0021) 0.0011

Bad news Post 6¼ 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 of D NI # is the variance of residuals from a regression of the change in annual net income (scaled by total




over DOCF #


divided by DOCF #








iAdjusted R




news subsamples




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 after Australian listed firms moved from Australian GAAP to IFRS. This supports the FRC’s 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.

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