does accounting quality change following a switch from u.s
TRANSCRIPT
Does Accounting Quality Change Following a Switch from U.S. GAAP to IFRS? Evidence from Germany
Stephen Lin* William Riccardi
Changjiang (John) Wang
Florida International University
January 2012
*corresponding author: Dr. Stephen Lin School of Accounting College of Business Administration Florida International University Miami, Floirda Email: [email protected]
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Does Accounting Quality Change Following a Switch from U.S. GAAP to IFRS? Evidence from Germany
ABSTRACT
This study examines whether accounting quality changed following a switch from U.S. GAAP to
IFRS. Using a sample of German high-tech firms that transitioned to IFRS from U.S. GAAP in
2005 (switching firms), we find that accounting numbers under IFRS generally exhibit more
earnings management, less timely loss recognition, and less value relevance compared to those
under U.S. GAAP. In addition, after analyzing the accounting quality of firms that applied IFRS
throughout the entire sample period, we find that, for the metrics suggesting a decline in
accounting quality for both groups of firms, the change is significantly more pronounced for
firms switching to IFRS from U.S. GAAP. Overall, our findings indicate that the application of
U.S. GAAP generally resulted in higher accounting quality than application of IFRS, and a
transition from U.S. GAAP to IFRS reduced accounting quality. Our findings provide the first
evidence on the potential consequences of a switch from U.S. GAAP to IFRS.
Keywords: Accounting Quality; U.S. GAAP; IFRS
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1. INTRODUCTION
Prior research provides some supporting evidence on the improvement in accounting
quality following a switch from non-U.S. domestic standards to International Financial
Reporting Standards (IFRS). However, to our knowledge, no study has examined the extent to
which accounting quality changes after a switch from United States Generally Accepted
Accounting Principles (U.S. GAAP) to IFRS. We believe that this is an important research
question because there are many current debates surrounding the costs and benefits of switching
from U.S. GAAP to IFRS. In this study, we examine whether accounting quality changed
following a switch from U.S. GAAP to IFRS for a sample of German high-tech firms that
applied U.S. GAAP and were required to switch to IFRS in 2005.
Based on various metrics for earnings management and timely loss recognition used in
prior research (Basu, 1997; Lang et al., 2003; Lang et al., 2006; Barth et al., 2008), we find that
firms in our sample generally exhibit more earnings management and less timely loss recognition
in the post-adoption period when using IFRS relative to the pre-adoption period when using U.S.
GAAP. In addition, using the return-earnings model as suggested by Easton and Harris (1991),
we find that accounting numbers under U.S. GAAP appear to provide more value relevant
information to investors compared to accounting numbers reported under IFRS after controlling
for firm-specific and time varying factors. Finally, we perform additional analyses using a
sample of similar firms that applied IFRS throughout the entire sample period in order to
compare the detected decline in accounting quality of firms that switched accounting standards
to those that did not. We find that, for the three metrics suggesting a decline in accounting
quality for both groups of firms, the change is significantly more pronounced for firms switching
to IFRS from U.S. GAAP. Overall, our findings indicate that U.S. GAAP generally provided
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higher accounting quality than IFRS, and a transition from U.S. GAAP to IFRS reduced
accounting quality. This study provides the first evidence on the impact of a switch to IFRS from
U.S. GAAP on accounting quality, which should be of interest to the SEC, the FASB, U.S. firms,
and academics in evaluating the potential benefits and disadvantages of a switch from U.S.
GAAP to IFRS. However, the above findings may not offer direct implications for the U.S.
adoption of IFRS and should be interpreted with caution because the sample firms examined in
this study may not be representative of firms in the U.S.
This study differs from prior studies in at least three aspects. First, there is a large body of
research regarding how various aspects of financial reporting are affected by a switch from non-
U.S. domestic accounting standards to IFRS. However, there is no direct evidence as to whether
firms using U.S. GAAP would experience similar benefits from adopting IFRS because U.S.
GAAP are believed to be superior to other domestic standards.
Second, some studies compare accounting quality of firms applying U.S. GAAP with
those applying IFRS after matching on certain characteristics (such as size and industry).
However, the extent to which these firms are comparable is unclear given that they operate in
different countries and face various socio-economic and legal environments. Examining the
accounting quality effect of a switch from U.S. GAAP to IFRS uses firms themselves as a control
to avoid this issue.
Third, some studies use the reconciled accounting information of foreign firms cross-
listed in the U.S. to compare accounting quality based on US GAAP and IFRS. In these papers,
the same firms provide two sets of financial information that are used to assess differences in
accounting quality. However, the reconciled information offers limited amounts of data for
empirical tests. Further, prior research has found evidence suggesting that firms will attempt to
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manage the magnitude of reported accounting differences, which can seriously affect any
empirical results drawn from the use of these reconciled amounts (Tarca, 2002; Landry and
Callimaci, 2003; Bradshaw et al., 2004). Moreover, these firms reconcile to U.S. GAAP from
IFRS, whereas we examine an actual switch from U.S. GAAP to IFRS.
The remainder of this study is organized as follows. The next section review prior
literature related to our study. Section three discusses our research design. Section four describes
our sample data. Section five presents our empirical results. In section six, we perform additional
tests that we find relevant to the setting of our study. In section seven, we offer some
implications of our results on the potential adoption of IFRS in the United States. We summarize
in section eight and provide implications for future research.
2. PRIOR RESEARCH
To our knowledge, no study has examined the extent to which accounting quality changes
after a switch from U.S. GAAP to IFRS. However, a number of studies examine change in
accounting quality following a switch to IFRS from non-U.S. domestic standards. The findings
of these studies are generally supportive. Most notably, Barth et al. (2008) find that firms exhibit
higher accounting quality after voluntarily switching to IFRS based on a variety of metrics.
There are also a number of papers that examine differences in accounting quality between
U.S. GAAP and IFRS in environments where firms are free to choose between multiple sets of
standards. For example, Bartov et al. (2005) find no significant difference in earnings quality,
measured by the price-earnings relationship, for a sample of German New Market firms that
were allowed to choose between IFRS and U.S. GAAP. Similarly, the findings of Van der
Muelen et al. (2007) suggest that there is no difference in value relevance between application of
IFRS and U.S. GAAP using a similar sample of German firms, though they do find that
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reconciling firms may attempt
application of U.S. GAAP results in more predictable earnings than application of IFRS. These
two studies provide consistent evidence suggesting that investors do not perceive accounting
numbers reported under U.S. GAAP compared to those reported under IFRS to provide
materially different information. This is consistent with Leuz (2003), which finds that market
liquidity and information asymmetry are similar across IFRS and U.S. GAAP firms.
Finally, some studies focus on the accounting differences reported via the reconciliation
disclosure (Form 20-F) of foreign firms that cross list on U.S. exchanges. Such studies can be
classified into two groups. First, a number of studies examine the information content of the
accounting differences between IFRS and U.S. GAAP, and provide mixed evidence. Harris and
Muller (1999) find that reported differences in net income and shareholders’ equity are
associated with both market value and stock returns, but not price. Conversely, Henry et al.
(2009) find that these differences are only associated with returns. Using an abnormal trading
volume approach, Chen and Sami (2009) find that in the two-day window surrounding release of
reconciliations, abnormal trading volume is positively associated with the absolute magnitude of
differences. Overall, these findings suggest that reported differences in net income and
shareholders’ equity have at least some value to investors, implying that market may in fact
perceive differences in financial reporting under the two sets of standards.
Using the IFRS-U.S. GAAP reconciliation disclosures, Gordon, Jorgensen, and Lithicum
(2010) investigate differences in accounting quality between U.S. GAAP and IFRS. Based on
nine earnings attributes1, Gordon et al. (2010) find similar accounting quality between U.S.
GAAP and IFRS except that U.S. GAAP appear to be more value relevant than IFRS. However,
to reduce the reported differences between U.S. GAAP and IFRS
1 Specifically, their study examines: (1) accrual quality; (2) earnings persistence; (3) earnings predictability; (4) cash persistence; (5) cash predictability; (6) earnings smoothness; (7) relevance; (8) timeliness; and (9) conservatism.
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accounting information2 because large differences between U.S. GAAP and non-U.S. GAAP
income amounts may increase the uncertainty among market participants about the underlying
economic earnings of the firm (Bradshaw et al., 2004; Chen and Sami, 2008). Therefore,
inferences regarding the difference in accounting quality between U.S. GAAP and IFRS drawn
from this setting may not truly reflect the relative difference in accounting quality between the
two sets of standards.
Other studies focus on issues more relevant to the current debates surrounding the
relative quality between IFRS and U.S. GAAP. A recent study by Barth, Landsman, Lang, and
Williams (2011) examine the comparability and the difference in value relevance between firms
domiciled in 27 different countries and that adopted IFRS between 1995 and 2006 to a matched
sample of U.S. firms applying U.S. GAAP. The findings suggest that comparability has
increased between U.S. firms and foreign firms following adoption of IFRS by foreign issuers
and more so for firms whose country of origin shares similar economic, social and legal
characteristics with the U.S. (i.e., common- law countries). Barth et al. (2011) also provide
evidence that application of U.S. GAAP results in higher value relevance of accounting
information compared to foreign firms using IFRS. However, the results of this study do not
offer implications regarding how a switch from U.S. GAAP to IFRS could affect financial
reporting.
To summarize, prior research provides mixed evidence on the relative quality between
IFRS and U.S. GAAP. This study contributes to the literature by investigating the impact on
financial reporting quality after a switch to IFRS from U.S. GAAP.
2 For example, cross-listed foreign firms are more likely to apply accounting practices similar to those used under U.S. GAAP than other firms from the same country that are not cross-listed (Tarca, 2002; Landry and Callimaci, 2003; Bradshaw et al., 2004).
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3. RESEARCH DESIGN
3.1 Overview
We make no ex ante predictions regarding how accounting quality should change
following the switch from U.S. GAAP to IFRS. Although many studies attempt to examine the
relative quality between the two, they are generally investigative in nature because there is no
clear theory supporting these predictions. Arguably, the differences in the nature of the standards
themselves could be used to motivate certain predictions with respect to some of our metrics.
Still, it is unclear how managerial incentives may play a role in changes in financial reporting
when firms switch standards. Therefore, the nature of this study is purely investigative in order
to better understand the potential impacts of a switch from U.S. GAAP to IFRS, which has not
been examined in prior empirical research.
We classify all firm-year observations prior to the mandatory adoption of IFRS by the
European Union (EU) in 2005 as the pre-adoption period and all firm-year observations after as
the post-adoption period. Following prior research (Lang et al., 2003; Lang et al., 2006; Barth et
al., 2008), we infer differences in a variety of summary statistics (e.g., variances, correlations,
R2) relating to our earnings metrics between the pre- and post-adoption periods as evidence of
differences in accounting quality. We also directly examine regression coefficients where
applicable.
3.2 Accounting Quality Metrics
3.2.1. Earnings Management
The first three proxies for earnings management relate to earnings smoothing and the
remaining one refers to managing earnings to achieve positive income (to avoid losses).
Beginning with earnings smoothing, our first metric is based on the variability of the change in
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net income3 scaled by total assets, ΔNI. If managers take no discretionary action to smooth
earnings, then they should be relatively volatile and fluctuate over time. Therefore, we interpret a
smaller variance of ΔNI as suggestive of earnings smoothing. As the change in net income is at
least partially attributable to factors other than those of the financial reporting system, we follow
prior research to analyze the change in net income on variables to controls for such factors (Lang
et al., 2003; Lang et al., 2006; Barth et al., 2008) using the following regression model:
ΔNIit = α0 + α1SIZEit + α2GROWTHit + α3EISSUEit + α4DEBTit + α5DISSUEit + α6TURNit + α7AUDit + α8NUMEXit + α9CLOSEit + α10CFit + εit
(1)
where:
SIZE =
=
the natural logarithm of year-end market value of equity;
GROWTH
=
percent change in sales;
EISSUE
=
percent change in common stock;
DEBT
=
year-end total liabilities divided by year-end book value of equity;
DISSUE
=
percent change in total liabilities;
TURN
=
sales divided by year-end total assets;
AUD an indicator variable equal to 1 for observations where the firm’s auditor is PwC, Deloitte, E&Y, KPMG, or Arthur Andersen, and zero otherwise;
NUMEX =
=
the number of exchanges on which the firm’s stock is listed;
CLOSE
=
the percent of closely-held shares of the firm’s stock;
CF annual net cash flows from operating activities.
We denote our first metric as ΔNI*, the variance of the residuals estimated from the
above regression.4
3 Datastream offers several “Net Income” definitions for various income line items. We use Net Income before extraordinary and other non-operating items in all of our analyses. 4 In each test in which we use the residuals from regression models, we report results based on regressions run by year. For robustness, we run the same models using a pooled sample with the addition of year indicator variables. Results are similar.
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One concern with the preceding metric of earnings smoothing is that earnings variability
may be due to differences in cash flow activities that are not associated with discretionary
accounting choices (Lang et al., 2003). We attempt to control for these concerns by examining
the variability of ∆NI relative to change in cash flows, ∆CF. As the change in cash flows can also
be affected by factors other than those of the financial reporting system, we control for these
factors and estimate the following regression5:
ΔCFit = α0 + α1SIZEit + α2GROWTHit + α3EISSUEit + α4DEBTit + α5DISSUEit + α6TURNit + α7AUDit + α8NUMEXit + α9CLOSEit + α10CFit + εit
(2)
From equation (2), we take the variance of the residuals as the measure of variability in
cash flows, ∆CF*. Following prior research (Lang et al., 2003; Lang et al., 2006; Barth et al.,
2008), the second earnings smoothing metric is the ratio of variability of ∆NI* to the variability
of ∆CF*.
Following prior research (Lang et al., 2003; Leuz et al.2003; Lang et al., 2006; Barth et
al., 2008), we construct the third earnings smoothing metric as the Spearman correlation between
operating cash flows (CF) and total accruals (ACC), where ACC is measured as net income (NI)
minus CF. Managers can use accruals to make up for shortcomings in the cash component of
income in an attempt to smooth earnings, increasing accruals as the cash components of income
decrease. Thus, a more negative correlation between ACC and CF is suggestive of greater use of
accruals for this purpose. To mitigate the differences not attributable to the financial reporting
system, we measure the correlations of the residuals from the following two equations, denoted
as CF* and ACC*, rather than directly comparing correlations between CF and ACC. In the
5 To ease exposition, we use the same notation for regression coefficients and error terms where the same variables are used in multiple regression equations.
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following regressions, CF and ACC are both regressed on control variables, as with equations (1)
and (2), excluding CF:
CFit = α0 + α1SIZEit + α2GROWTHit + α3EISSUEit + α4DEBTit + α5DISSUEit + α6TURNit + α7AUDit + α8NUMEXit + α9CLOSEit + εit
(3)
ACCit = α0 + α1SIZEit + α2GROWTHit + α3EISSUEit + α4DEBTit + α5DISSUEit + α6TURNit + α7AUDit + α8NUMEXit + α9CLOSEit + εit
(4)
Prior research documents that a common target of earnings management is to achieve
positive income, thereby avoiding the reporting of losses (Burgstahler and Dichev, 1997). Our
metric for earnings management toward positive income is measured as the coefficient on the
time period variable, POST, from the following logistic regression6:
SPOSit = α0 + α1SIZEit + α2GROWTHit + α3EISSUEit + α4DEBTit + α5DISSUEit + α6TURNit + α7AUDit + α8NUMEXit + α9CLOSEit + α10CFit + α 11POSTit + εit
(5)
SPOS is an indicator variable equal to one if net income scaled by total assets is between 0 and
0.01 for a given observation and zero otherwise, and POST is an indicator variable equal to one
for observations in the post adoption period and zero otherwise. We base our conclusions on the
coefficient of POST rather than directly comparing frequencies because the coefficient takes into
account controls for factors not attributable to the financial reporting system. A positive
coefficient on POST indicates a higher likelihood of small positive earnings in the post-adoption
period than in the pre-adoption period (the opposite holding true for a negative coefficient)7.
6 Based on Burgstahler and Dichev (1997), only an abnormal frequency of small positive income is suggestive of earnings management. An “abnormal” frequency occurs when there is a significantly greater frequency of small positive earnings relative to small negative earnings (i.e., there is discontinuity in the distribution of earnings around zero). Accordingly, Leuz et al. (2003) test the frequency of small profits relative to the frequency of small losses. We are precluded from using the latter approach because the actual frequency of both small positive and small negative earnings for our sample of firms makes it unrealistic to draw statistical inferences. 7 Evidence from Beaver, McNichols and Nelson (2007) suggests that can certain income statement items can complicate the examination and comparison of the frequency of small positive and small negative income (i.e., effective tax rates and special items). We use Net Income before extraordinary and other non-operating items, which excludes these items and focuses instead on components of income over which management has more discretion.
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3.2.2. Timely Loss Recognition
We measure timely loss recognition metric in two ways. Ball (2001) suggests that firms
in different financial reporting environments differ in terms of timely loss recognition. Further,
other prior research (Ball, Kothari and Robin, 2000; Lang et al., 2003) suggests that firms
exhibiting more timely loss recognition should recognize large losses in the period in which they
occur rather than deferring them to future periods. Therefore, we should observe more frequent
incidences of extreme negative earnings for firms applying accounting standards that inherently
require a higher degree of conservatism. Consistent with prior research (Lang et al., 2003; Lang
et al., 2006; Barth et al., 2008), we estimate the following logistic regression:
LNEGit = α0 + α1SIZEit + α2GROWTHit + α3EISSUEit + α4DEBTit + α5DISSUEit + α6TURNit + α7AUDit + α8NUMEXit + α9CLOSEit + α10CFit + α 11POSTit + εit
(6)
In equation (6), LNEG is an indicator variable equal to one for given observations where
net income scaled by total assets is less than -0.20 and zero otherwise. As with the tests for
earnings management to avoid losses, POST is an indicator variable equal to one in the post-
adoption period and zero otherwise. We base our interpretations on the coefficient of POST after
controlling for potential effects other than those of the financial reporting system. A negative
coefficient on POST indicates that firms are more likely to recognize large losses in the pre-
adoption period than in the post-adoption period.
Our second measure for timely loss recognition is based on Basu (1997), where earnings
is regressed on an indicator for “bad news” (negative returns) in a given period, the actual return,
and an interaction of these two variables. Following prior studies, we include control variables
for firm-level differences that could lead to differences in conservatism among firms in our
sample. Ball and Shivakumar (2005), for example, note that larger firms may have more timely
recognition of losses than smaller firms due to increased litigation risk or differences in agency
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costs. Similarly, firms that have more debt obligations may also have incentives to recognize
losses in a timelier manner (Watts, 2003). Prior research also finds that the market-to-book ratio
can impact accounting conservatism (LaFond and Roychowdhury, 2008; Khan and Watts, 2009).
We include these control variables and interact them with both the return and negative return
indicator variables. Since we are interested in the accounting quality for the same sample of
firms in two periods—that is, before and after the switch from US GAAP to IFRS—we further
add an indicator variable, POST, and additional two- and three-way interaction terms, presented
in the following equation,
EPSit = β0 + β1Rit + β2DRit + β3POSTit + β4SIZE it + β5LEVit + β6MBit + β7(R*DR)it + β8(DR*POST)it + β9(DR*SIZE)it + β10(DR*LEV)it + β11(DR*MB)it + β12(R*POST)it + β13(R*SIZE)it + β14(R*LEV)it + β15(R*MB)it + β16(R*DR*POST)it + β17(R*DR*SIZE)it + β18(R*DR*LEV)it + β19(R*DR*MB)it + εi
(7)
EPS = Earnings per share, scaled by beginning of the year price;
R = Annual return;
DR = An indicator variable equal to 1 if R < 0, and zero otherwise.
MB = The market-to-book ratio, measured as the ratio of market value of equity
over book value of equity;
POST = An indicator variable equal to 1 for observations in the post-adoption
period, and zero otherwise.
The interaction term R*DR captures the incremental timeliness when the return is
negative with the argument that conservative reporting leads to bad news being impounded in
earnings in a timelier manner relative to good news (i.e. β3 is expected to be positive and
significant). Our metric based on the Basu’s conservatism model is the coefficient on the
interaction term R*DR*POST, which captures the magnitude and the direction of the incremental
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timely loss recognition of “bad news” for observations in the post-adoption period relative to
those in the pre-adoption period. A positive coefficient suggests that there is more timely loss
recognition in the post-adoption period while a negative coefficient suggests that there is more
timely loss recognition in the pre-adoption period.
3.2.3. Value Relevance
To examine changes in value relevance between the pre- and post-adoption periods, we
focus on stock returns rather than price because returns are only affected by information and
events in the period over which they are calculated. We follow Easton and Harris (1991) and
construct the following earnings response coefficient model,
Rit = γ0 + γ 1EPSit + γ2 ΔEPSit + YEAR + εit (8)
where:
R = Stock return measured nine months prior to three months after fiscal year-
end;
EPS = Earnings per share, scaled by beginning of the year stock price;
ΔEPS = Annual change in earnings per share, scaled by beginning of the year stock
price;
YEAR = Year indicator variables.
First, we estimate the regression coefficients on both current period earnings (γ1) and
annual change in earnings (γ2) in the pre- and post-adoption period. We compute the first return-
based value relevance metric, the earnings response coefficient, ERC, by summing these two
coefficients. We interpret a higher ERC as evidence of higher value relevance. Second, we
estimate the total explanatory power from the regression of return on current period earnings and
annual change in earnings. Thus, we use the adjusted R2 from equation (9) in the pre- and post-
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adoption period as a second metric for value relevance and test for statistical difference between
the two periods. We interpret a higher R2 as evidence of higher value relevance.
Except for the earnings metrics that are measured as regression coefficients, we apply a
bootstrapping procedure to compare the significance in difference of our earnings metrics. The
bootstrapping procedure requires no assumptions about the distributions of our metrics (Bickel
and Freedman, 1981) and it allows us to test those metrics with unknown distributions, such as
the ratio of variability of change in net income to variability of change in cash flows. It also
mitigates the concern that our inferences are a result of sample bias. To illustrate the
bootstrapping procedure, we use the test of differences in variability of change in net income as
an example. First, we randomly select, with replacement from the original sample, the same
number of firm-year observations as in the original sample to obtain a random sample. Second,
we run the regression of change in net income on the control variables on this random sample to
obtain the residuals — that is, the change in net income unexplained by the reporting
environment, managerial incentives, and firm-specific characteristics. We then calculate the
variance of these residuals, which is the metric used in this test. Third, we repeat this process 500
times to obtain a sample of the metric (i.e., a sample of variances of change in net income). We
perform this procedure separately for the pre- and post-adoption periods. We then apply a t-test
for statistical significance of the differences of the metric in the pre- and post-adoption periods.
4. Sample and DATA
4.1 Sample Firms
A number of firms (95% of these firms are in hi-tech industries) in Germany applied U.S.
GAAP and were required to switch to IFRS in 2005. These firms voluntarily chose to apply U.S.
GAAP in order to (1) add credibility to their financial statements, (2) report financial information
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that is more comparable and of similar quality to their U.S. counterparts, and (3) attract U.S.
investors in a time when use of IFRS was not as widespread as it is now8. Further, even after the
subsequent failure of the New Market on which many of these firms had initially listed (2002),
they continued to report their financial statements using U.S. GAAP rather than switching to
German GAAP or IFRS. Despite the similarities between these German firms and their U.S.
counterparts, our results may not offer direct evidence on the accounting quality consequences of
a switch from U.S. GAAP to IFRS for firms in the U.S.
4.2 Data
We use Worldscope to identify the accounting standards used by firms during our sample
period. We identify 153 publicly listed German high-tech firms who applied U.S. GAAP in the
pre-adoption period (2000-2005) and have accounting and market data available from 2000-
2010. We eliminate firms that: voluntarily adopted IFRS before 2005 (30); used German GAAP
in the years immediately preceding adoption of IFRS; have unverifiable standards data (11);
postponed adoption of IFRS, or changed accounting standards before 2005 (18); and firms that
are not in high-tech industry (3). Our main sample size comprises 582 firm-year observations
representing 63 firms.
For Basu’s test of conservatism and the value relevance of earnings, we require additional
data (e.g., monthly stock price and return data). After excluding observations with missing stock
price and returns, we are left with 533 firm-year observations representing 58 firms for these
tests. Table 1 presents the sample selection process.
[Table 1]
8 The German government allowed German firms to prepare their financial statements in accordance with U.S. GAAP, IFRS, or German GAAP before 2005. These companies voluntarily adopted U.S. GAAP for reasons stated.
16
Table 2 presents descriptive statistics for variables used in our analyses. To mitigate the
effects of outliers, we winsorize all continuous variables used in our analyses at the top and
bottom 1% level. On average, firms in our sample exhibit a greater (less negative) change in
earnings (change in cash flows) in the post-adoption period compared to the pre-adoption period.
There is also a higher amount of both cash flows and absolute accruals in the post-adoption
period compared to the pre-adoption period. In the pre-adoption period, firms tend to have more
debt relative to equity, higher growth rates, and greater issuance of both debt and equity. Stock
prices, returns, and earnings are, on average, higher in the post-adoption period.
[Table 2]
5. EMPIRICAL RESULTS
5.1 Earnings Management
Panel A of Table 3 shows the results for our earnings management metrics. The first
finding relating to earnings smoothing shows that our sample firms exhibits a significantly
higher variability of change in net income, ∆NI*, in the pre-adoption period, 0.2689, than in the
post-adoption period, 0.1493 (p < .0001). The ratio of the variance of ∆NI* to the variance of
∆CF* in the pre-adoption period, 2.2819, is also significantly higher than that in the post-
adoption period, 1.5243 (p < .0001). This result suggests that net income variability is not simply
a result of cash flow variability. As we infer more earnings variability to be indicative of less
earnings smoothing, these results suggest that there is higher accounting quality under U.S.
GAAP in the pre-adoption period. Our third finding is also consistent, suggesting less earnings
smoothing in the pre-adoption period. Specifically, the correlation between accruals, ACC*, and
cash flows, CF*, is -0.2512 in the pre-adoption period and it is significantly less negative than
the correlation in the post-adoption period of -0.3009 (p < .0001).
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Panel B of Table 3 shows the results of the likelihood of small positive earnings
regression. Interestingly, the coefficient on POST, -1.0983, is negative and statistically
significant (p = 0.0121), suggesting that firms in our sample manage toward positive earnings
more often in the pre-adoption period than in the post-adoption period. Limitations in our sample
preclude us from statistically testing small positive earnings relative to small negative earnings.
However, without drawing statistical inferences, the actual frequency of small positive earnings
relative to small negative earnings is higher in the post-adoption period for our sample.
[Table 3]
Overall, our results indicate that there is generally less earnings management in the pre-
adoption period than in the post-adoption period.
5.2 Timely Loss Recognition
Panel A of Table 4 presents the results of our first measure of timely loss recognition
metric based on the frequency of large negative income. The negative and significant coefficient
on POST, -0.734 (p = 0.0236) is suggestive of more timely loss recognition in the pre-adoption
period. The results from our second measure of timely loss recognition based on Basu’s model
are reported in Panel B of Table 4. The coefficient on the interaction term POST*R*DR, -0.177,
is negative and marginally significant (p = 0.0814), which suggests a higher degree of
conservatism and higher accounting quality in the pre-adoption period. Consistent with the debt
contracting demand for accounting conservatism (Watts, 2003), the coefficient on LEV*R*DR is
positive and significant, and the coefficient on MB*R*DR is negative, which is consistent with
the arguments of Roychowdhury and Watts (2007).
[Table 4]
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5.3 Value Relevance
Table 5 presents the results of the return value relevance regression. The ERC (the sum of
the regression coefficients γ1 and γ2) in the pre-adoption period, 1.0424, is significantly greater
than the ERC in the post-adoption period, 0.4898 (p < .0001). We also compare the explanatory
power of the return regression and find that the adjusted R2 in the pre-adoption period, 58.35%,
is significantly higher than the adjusted R2 in the post-adoption period, 53.47% (p < .0001) .
Based on these results, earnings appear to have higher value relevance to investors in the pre-
adoption period than in the post-adoption period.
[Table 5]
Overall, our results are suggestive of higher accounting quality for our sample firms in
the pre-adoption period while applying U.S. GAAP than in the post-adoption period after
adoption of IFRS. All earnings smoothing metrics suggest higher quality for our sample in the
pre-adoption period. We also find evidence of increased timely loss recognition in the pre-
adoption period. With respect to value relevance, our results suggest that the value relevance of
current period earnings and annual change in earnings decrease following the switch from U.S.
GAAP to IFRS.
5.4 Sensitivity Tests
We consider two potential factors that may have a confounding effect on the inferences
drawn from our previous results. First, there are inherent differences between U.S. GAAP and
IFRS that could lead to the detected differences in earnings quality after firms switched to IFRS.
We identify Research and Development expense (R&D) as a key difference because our sample
firms are high-tech firms that may heavily invest in R&D. Under U.S. GAAP, all R&D is
expensed in the period in which it is incurred, whereas part of R&D (i.e., development costs) is
19
capitalized under IFRS. This could lead to less volatile earnings when firms apply IFRS even if
managers do not use discretionary actions to smooth earnings. Therefore, we include R&D
scaled by year-end total assets as an additional control variable and repeat all tests reported in
Table 3.9 Untabulated results yield inferences consistent with our main findings.
Second, our sample period overlaps with the recent global financial crisis, which could
have a significant impact on our results. We eliminate all firm-year observations in 2009 and
2010 from the post-adoption period in our sample10 and repeat all the tests used in our main
analyses. Although most of our results (untabulated) remain intact, we note two major
differences. First, after we eliminate these years, our timely loss recognition metric based on
Basu’s model is no longer significant (p = 0.2535), implying that firms may have adopted more
aggressive (i.e., less conservative) accounting practices in periods of economic distress. Second,
we find the correlation of ACC* and CF* in the post-adoption period (-0.1848) is significantly
less negative (p < .0001) than that of the pre-adoption period (-0.2512). This finding implies that
firms attempt to smooth earnings more during periods of financial crisis or that accruals and cash
flows are less perfectly matched during economic downturns due to firms’ altered operations and
consumer behavior.
Overall, the above sensitivity tests show that our main findings of a decline in accounting
quality of U.S. GAAP firms are generally robust after controlling for R&D expenditure and the
effect of the recent financial crisis.
9 An ideal method to address this issue would be to restate the income amount used in our analyses to exclude R&D. However, this would likely result in significant measurement error since we would need to estimate only the portion of R&D reported under U.S. GAAP that would be capitalized under IFRS. 10 We deleted all the observations in 2009 and 2010 because the financial crisis should affect non-U.S. firms after 2008.
20
smoothing in more recent yea
6. ADDITIONAL ANALYSES
In order to confirm that the decrease in the accounting quality of U.S. GAAP firms is
mainly caused by the change from U.S. GAAP to IFRS and not due to other factors, we
investigate the accounting quality change for firms that applied IFRS throughout the entire
sample period (“IFRS firms”). We also compare the changes in accounting quality of U.S.
GAAP firms with any changes of IFRS firms by applying difference-in-differences tests. We
would expect that the financial reporting quality of IFRS firms should be either constant or
increasing throughout the sample period for three reasons. First, there is no evidence from prior
research suggesting that the accounting quality of IFRS has reduced during the sample period.
Second, the SEC’s Roadmap clearly states that the ongoing convergence projects between IASB
and FASB should have increased the quality of IFRS. Third, since these firms applied IFRS even
before it was mandatory, their reporting incentives should remain consistent.
We identify 63 high-tech German firms that had applied IFRS during the sample period
(2000-2008)11 and have all the accounting and market data available for further analyses. Panel
A of Table 6 shows that, like U.S. GAAP firms, ΔNI* is significantly less negative in the pre-
adoption period (p < .0001), suggesting less smoothing in earlier years. However, the ratio of
∆NI* to ∆CF* is significantly greater in the post-adoption period (p < .0001), suggesting an
increase in accounting quality for these firms. We focus on the latter result because this metric
adjusts for earnings volatility resulting from cash flow volatility. Consistent with this
supposition, the correlation of ACC* and CF* is significantly less negative in the post-adoption
period (p < .0001). Taken together, our results suggest that IFRS firms have less earnings
rs, suggesting higher accounting quality. With respect to earnings
11 To be consistent with the results from our most strict specification, we eliminate the financial crisis years from tests in our additional analyses.
21
experienced the change from U
management to avoid losses, the result suggests that there has not been a significant change in
the frequency of reporting small positive income.
Turning to Panel B, the test for the frequency of large negative income and Basu’s
conservatism both yield insignificant positive coefficients. Therefore, we do not find evidence of
a change in timely loss recognition for IFRS firms over the sample period. Lastly, Panel C
summarizes the results of our value relevance tests. The earnings responsive coefficient (ERC) is
significantly greater (p < .0001) in the post-adoption period (1.335) than in the pre-adoption
period (0.7052). However, the return model adjusted R2 of 44.06% is significantly lower (p <
.0001) in the post-adoption period of 54.11%.
Since some metrics (ΔNI*; the Correlation of ACC* and CF*; return model adjusted R2)
suggest that the accounting quality of IFRS firms may have declined in the post-adoption
period,12 we perform a difference-in-differences test to examine whether the decline in
accounting quality is more pronounced for firms that switched from U.S. GAAP to IFRS relative
to IFRS firms. In all three cases, the results reported in Table 7 show that the change is
significantly greater for firms that switched from U.S. GAAP to IFRS.
Our robustness tests confirm an overall decrease in accounting quality when German
firms switched from U.S. GAAP to IFRS. Most metrics show that there was little change in
accounting quality for IFRS firms, suggesting that our results are driven by the switch from U.S.
GAAP to IFRS. Further, any potential decline in the accounting quality of IFRS firms captured
by our metrics is significantly less than the decline in accounting quality for firms that
.S. GAAP to IFRS.
12 Each of these tests relies on the previously described bootstrapping procedure to compare summary statistics. To calculate the change, we first match each observation from the pre-adoption period with the equivalent observation in the post-adoption. We then obtain a distribution of the change in the metric and test for significant differences between the two samples of firms.
22
7. IMPLICATIONS FOR U.S.
Our findings suggest that a switch from U.S. GAAP to IFRS could lead to a decline in
accounting quality. However, there are a number of reasons why adoption of IFRS by U.S. firms
may have different consequences. First, the convergence projects between the IASB and FASB
should continue to reduce the accounting differences between IFRS and U.S. GAAP, leading to
similar quality between the two sets of standards. Second, these convergence efforts may lead to
a reduction in the amount of managerial discretion allowed by IFRS. Since it is not certain when
the U.S. will adopt IFRS, the passage of time may allow for further changes to be made to
converge the two sets of standards and reduce accounting quality differences. Third, it is unlikely
that the reporting incentives of U.S. managers will change following adoption of IFRS. As
argued by Hail et al. (2010), managers of U.S. firms should still strive to provide accounting
information that is of a high enough quality to be useful to users of financial information in U.S.
capital markets. Finally, it is possible that in the absence of further improvements to IFRS, the
SEC may take some regulatory action to circumvent a potential decline in the accounting quality
of U.S. firms.
8. CONCLUSION
A few studies have provided mixed evidence on the relative quality between IFRS and
U.S. GAAP. This study contributes to the literature by examining the change in accounting
quality following a switch from U.S. GAAP to IFRS for a sample of German high-tech firms that
applied U.S. GAAP and were required to switch to IFRS in 2005.
We find that accounting numbers under IFRS generally exhibit more earnings
management, less timely loss recognition, and less value relevance after controlling for firm-
specific and time varying factors. We also find that for the three metrics suggesting a decline in
23
accounting quality for both switching and non-switching firms, the change is significantly more
pronounced for firms switching to IFRS from U.S. GAAP. Overall, our findings indicate a
switch from U.S. GAAP to IFRS could reduce accounting quality. Although our findings may
contribute to the debates surrounding the possible consequences of a switch from U.S. GAAP to
IFRS, this study does not attempt to infer potential financial reporting consequences of a switch
to IFRS by U.S. firms.
24
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Table 1. Sample Selection
Firms Identified firms from Datastream 153Less: Firms that voluntarily adopted IFRS (30)Less: Firms that used German GAAP prior to adoption of IFRS (16)Less: Firms for which we cannot verify missing standards data (11)Less: Firms that postponed adoption of IFRS (18)Less: Firms that changed accounting standards multiple times (12)Less: Firms that are not in high-tech industries (3)
Main sample of firms 63
Less: Firms with missing stock price and return data (5)Firms used for Basu Conservatism and Value Relevance Tests 58
Table 2. Descriptive Statistics Relating to Variables Used in Analyses
N Mean Median Std Dev N Mean Median Std Dev N Mean Median Std Dev t-test Z-testEarning Management and Frequency of Large Negative Income Test variablesΔNI 582 -0.007 0.002 0.277 295 -0.015 -0.002 0.323 287 0.002 0.005 0.221 0.4649 0.0753ΔCF 582 -0.006 -0.003 0.152 295 -0.006 -0.008 0.163 287 -0.005 0.002 0.140 0.9028 0.2556ACC 582 -0.069 -0.049 0.163 295 -0.091 -0.058 0.185 287 -0.047 -0.039 0.134 0.0010 0.0059CF 582 0.024 0.055 0.155 295 0.008 0.037 0.159 287 0.041 0.072 0.149 0.0090 0.0002SPOS 582 0.052 0.000 0.221 295 0.075 0.000 0.263 287 0.028 0.000 0.165 0.0108 0.0056LNEG 582 0.144 0.000 0.352 295 0.173 0.000 0.379 287 0.115 0.000 0.320 0.0470 0.0238
Earning Management and Frequency of Large Negative Income Control variablesDEBT 582 1.495 0.700 3.501 295 1.604 0.741 3.757 287 1.383 0.662 3.219 0.4473 0.4403GROWTH 582 0.322 0.040 1.754 295 0.583 0.018 2.400 287 0.054 0.066 0.433 0.0003 0.4474EISSUE 582 0.586 0.026 3.497 295 0.991 -0.022 4.664 287 0.169 0.070 1.464 0.0045 0.0031DISSUE 582 0.476 0.004 2.748 295 0.764 -0.012 3.726 287 0.181 0.024 0.946 0.0105 0.0310TURN 582 1.112 0.999 0.663 295 1.092 0.948 0.696 287 1.132 1.041 0.627 0.4617 0.0346SIZE 582 516.6 670.9 1573,9 295 457.2 78.9 1207.5 287 577.7 56.0 1877.9 0.3563 0.0232AUD 582 1.856 2.000 1.021 295 1.895 2.000 1.023 287 1.815 2.000 1.019 0.3477 0.1252NUMEX 582 0.687 1.000 0.464 295 0.671 1.000 0.471 287 0.704 1.000 0.457 0.3966 0.1983CLOSE 582 30.843 26.440 28.136 295 32.386 30.490 28.282 287 29.257 21.440 27.946 0.1801 0.1238RD 582 0.055 0.021 0.077 295 0.051 0.020 0.074 287 0.058 0.023 0.080 0.2891 0.2838
Basu Test of Conservatism and Value Relevance Test VariablesP 533 10.103 5.763 12.799 260 9.965 6.493 10.915 273 10.235 5.039 14.385 0.8082 0.0241R 533 -0.141 -0.065 0.738 260 -0.204 -0.144 0.873 273 -0.08 -0.045 0.576 0.0521 0.0569EPS 533 -0.043 0.024 0.255 260 -0.082 0.003 0.255 273 -0.007 0.058 0.251 0.0006 0.0000∆EPS 533 0.097 0.012 0.413 260 0.123 0.008 0.472 273 0.073 0.017 0.347 0.1638 0.2286NI 533 -0.082 0.026 0.314 260 -0.138 0.003 0.345 273 -0.028 0.054 0.271 0.0001 0.0000SIZE 533 11.362 11.066 1.656 260 11.448 11.192 1.619 273 11.281 10.938 1.689 0.2470 0.1211LEV 533 0.461 0.415 0.272 260 0.473 0.434 0.290 273 0.449 0.395 0.254 0.3082 0.2158MB 533 2.522 1.406 3.941 260 2.837 1.486 4.292 273 2.222 1.377 3.557 0.0716 0.1220
Full sample Pre Post p-value
28
ΔNI is the change in annual earnings, where earnings is scaled by total assets. ΔCF is the change in annual net cash flow from operations, where cash flow is scaled by total assets. ACC is defined as earnings (NI) less annual operating cash flows (CF), scaled by total assets. CF is annual operating cash flows, scaled by total assets. SPOS is an indicator variable that equals 1 for observations with annual earnings scaled by total assets
29
between 0.00 and 0.01, and zero otherwise. LNEG is an indicator variable for observations with annual earnings scaled by total assets less than -0.20, and zero otherwise. DEBT is total liabilities divided by book value of equity. GROWTH is annual percentage change in sales. EISSUE is annual percentage change in common stock. DISSUE is annual percentage change in total liabilities. TURN is sales divided by total assets. SIZE is the natural logarithm of market value of equity. AUD is an indicator equal to 1 for observations where the firm's auditor is PricewaterhouseCoopers, Deloitte & Touche, Ernst & Young, KPMG, or Arthur Andersen and zero otherwise. NUMEX is the number of Exchanges the firm's security is traded on. CLOSE is the percentage of closely held shares as reported in WorldScope. RD is the research and development expense scaled by total assets. P is the stock price six months after fiscal year-end. R is annual stock return from nine months prior to three months after the firm's fiscal year-end. EPS is earnings per share, scaled by beginning of the year stock price. ∆EPS is the annual change in earnings per share, scaled by beginning of the year stock price. SIZE is the natural logarithm of market value of equity. LEV is total liabilities divided by total assets. MB is the market-to-book ratio, measured as the ratio of market value of equity over book value of equity.
Table 3: Earnings Management Metrics P
anel A: Earnings Smoothing Metrics
Pre Post p-valueVariability of ∆NI* 0.2698 0.1493 <.0001Variability of ∆NI* over ∆CF* 2.2819 1.5243 <.0001Correlation of ACC* and CF* -0.2512 -0.3009 <.0001N 295 287
ΔNIit = α0 + α1SIZEit + α2GROWTHit + α3EISSUEit + α4DEBTit + α5DISSUEit + α6TURNit + α7AUDit + α8NUMEXit + α9CLOSEit + α10CFit + εit
(1)
ΔCFit = α0 + α1SIZEit + α2GROWTHit + α3EISSUEit + α4DEBTit + α5DISSUEit + α6TURNit + α7AUDit + α8NUMEXit + α9CLOSEit + α10CFit + εit
(2)
CFit = α0 + α1SIZEit + α2GROWTHit + α3EISSUEit + α4DEBTit + α5DISSUEit + α6TURNit + α7AUDit + α8NUMEXit + α9CLOSEit + εit
(3)
ACCit = α0 + α1SIZEit + α2GROWTHit + α3EISSUEit + α4DEBTit + α5DISSUEit + α6TURNit + α7AUDit + α8NUMEXit + α9CLOSEit + εit
(4)
∆NI*, ∆CF*, CF*, and ACC* are the residuals from the annual regressions of the above models respectively. ΔNI is the change in annual earnings, where earnings is scaled by total assets. ΔCF is the change in annual net cash flow from operations, where cash flow is scaled by total assets. ACC is defined as earnings (NI) less annual operating cash flows (CF), scaled by total assets. CF is annual operating cash flows, scaled by total assets. DEBT is total liabilities divided by book value of equity. GROWTH is annual percentage change in sales. EISSUE is annual percentage change in common stock. DISSUE is annual percentage change in total liabilities. TURN is sales divided by total assets. SIZE is the natural logarithm of market value of equity. AUD is an indicator equal to 1 for observations where the firm's auditor is PricewaterhouseCoopers, Deloitte & Touche, Ernst & Young, KPMG, or Arthur Andersen and zero otherwise. NUMEX is the number of Exchanges the firm's security is traded on. CLOSE is the percentage of closely held shares as reported in WorldScope.
30
Table 3: Earnings Management Metrics (Cont.)
P
anel B: Toward Positive Income
Variable Estimate p-ValueIntercept -1.732 0.2968SIZE -0.034 0.8277GROWTH 0.039 0.7833EISSUE 0.061 0.0759DEBT -0.048 0.546DISSUE -0.180 0.3552TURN -0.072 0.8103AUD -0.341 0.4451NUMEX 0.010 0.965CLOSE -0.004 0.5356CF 1.917 0.1989POST -1.098 0.0121Likelihood ratioPercent ConcordantN
14.524567.70%
Dependent variable = SPOS
582
SPOS is an indicator variable that equals 1 for observations with annual earnings scaled by total assets between 0.00 and 0.01, and zero otherwise. POST is an indicator variable equal to one for observations in the post adoption period, and zero otherwise. DEBT is total liabilities divided by book value of equity. GROWTH is annual percentage change in sales. EISSUE is annual percentage change in common stock. DISSUE is annual percentage change in total liabilities. TURN is sales divided by total assets. SIZE is the natural logarithm of market value of equity. AUD is an indicator equal to 1 for observations where the firm's auditor is PricewaterhouseCoopers, Deloitte & Touche, Ernst & Young, KPMG, or Arthur Andersen, and ero otherwise. NUMEX is the number of Exchanges the firm's security is traded on. CLOSE is he percentage of closely held shares as reported in WorldScope.
zt
31
Table 4: Timely Loss Recognition
Panel A: Frequency of Large Negative Earnings
Variable Estimate p-ValueIntercept 4.137 0.006SIZE -0.570 <.0001GROWTH -0.248 0.3316EISSUE -0.056 0.3168DEBT 0.022 0.5328DISSUE -0.008 0.9591TURN -0.189 0.4188AUD 0.662 0.0762NUMEX -0.072 0.7381CLOSE 0.007 0.2123CF -9.604 <.0001POST -0.734 0.0236Likelihood ratioPercent ConcordantN
Dependent variable = LNEG
89.30%183.0493
582
LNEG is an indicator variable for observations with annual earnings scaled by total assets less than -0.20, and zero otherwise. POST is an indicator variable equal to one for observations in the post adoption period, and zero otherwise. DEBT is total liabilities divided by book value of equity. GROWTH is annual percentage change in sales. EISSUE is annual percentage change in common stock. DISSUE is annual percentage change in total liabilities. TURN is sales divided by total assets. SIZE is the natural logarithm of market value of equity. AUD is an indicator equal to 1 for observations where the firm's auditor is PricewaterhouseCoopers, Deloitte & Touche, Ernst & Young, KPMG, or Arthur Andersen, and zero otherwise. NUMEX is the number of Exchanges the firm's security is traded on. CLOSE is the percentage of closely held shares as reported in WorldScope.
32
Table 4: Timely Loss Recognition (Cont.)
Panel B: Timely Loss Recognition Test based on Basu’s Model
Variable Estimate p-ValueIntercept -0.255 0.1199DR -0.369 0.1315POST -0.026 0.5943MB -0.003 0.71LEV -0.220 0.039SIZE 0.034 0.0186POST*DR 0.114 0.0858MB*DR 0.004 0.647LEV*DR 0.254 0.0654SIZE*DR 0.012 0.5607R 0.078 0.7951POST*R 0.181 0.0261MB*R 0.029 0.2478LEV*R 0.147 0.3655SIZE*R -0.026 0.3326R*DR -0.026 0.9433POST*R*DR -0.177 0.0814MB*R*DR -0.028 0.2654LEV*R*DR -0.038 0.8433SIZE*R*DR 0.022 0.4995
Adj. R-squareN
Dependent variable = EPS
5330.207
EPS is earnings per share, scaled by beginning of the year stock price. R is annual stock return from nine months prior to three months after the firm's fiscal year-end. DR equals 1 if R is negative, and 0 otherwise. MB is the market-to-book ratio, measured as the ratio of market value of equity over book value of equity LEV is total liabilities divided by total assets. SIZE is the natural logarithm of market value of equity. POST is an indicator variable equal to one for observations in the post adoption period, and zero otherwise.
33
Table 5: Value Relevance using the Return Model
Pre Post
Estimate Estimate p-valueIntercept -0.02 0.1775 <.0001EPS 0.6932 0.3763 <.0001ΔEPS 0.3492 0.1134 <.0001ERC 1.0424 0.4898 <.0001Year indicator Included IncludedN 260 273Return Model adjusted R2 0.5835 0.5347 <.0001
Dependent Variable = R
R is annual stock return from nine months prior to three months after the firm's fiscal year-end. EPS is earnings per share, scaled by beginning of the year stock price. ∆EPS is the annual change in earnings per share, scaled by beginning of the year stock price. ERC is the sum of the two coefficients on EPS and ∆EPS.
34
Table 6: Earnings Quality Metrics for IFRS Firms
This Table presents the results for a set of firms that used the IFRS throughout our sample period. All the variables are defined as in previous tables.
Panel A: Earnings Management Metrics
Pre Post p-valueVariability of ∆NI* 0.1949 0.1673 <.0001Variability of ∆NI* over ∆CF* 2.1666 2.5227 <.0001Correlation of ACC* and CF* -0.3005 -0.2809 <.0001Small Positive Income (SPOS ) 0.2369
N 322 370-0.5899
Panel B: Timely Loss Recognition and Conservatism Large Negative Income (LNEG ) 0.904
Basu's Conservatism Model 0.39920.1420.1086
Panel C: Value Relevance
Pre Post p-valueReturn Model Adjusted R2
0.5411 0.4406 <.0001Earnings Response Coefficient (ERC ) 0.7052 1.335 <.0001N 267 372
35
Table 7: Difference-in-differences Test This Table presents the difference-in-differences results between the firms that switched from US GAAP to IFRS and firms that used IFRS throughout our sample period. All the variables are defined as in previous tables. The comparison of the pre- to post-adoption period changes is based on Boot-strapping method with 500 replications.
Panel A: Earnings Management Metrics
U.S. GAAP IFRS p-value U.S. GAAP IFRS p-value U.S. GAAP IFRS p-valueVariability of ∆NI* 0.2698 0.1949 <.0001 0.1531 0.1673 <.0001 0.1167 0.0276 <.0001Variability of ∆NI* over ∆CF* 2.2819 2.1666 <.0001 1.4123 2.5227 <.0001 0.8696 -0.3561 <.0001Correlation of ACC* and CF* -0.2512 -0.3005 <.0001 -0.1848 -0.281 <.0001 -0.0665 -0.0195 <.0001N 295 322 183 224 500 500
ChangePost (2006-2008)Pre (2000 - 2004)
P
anel B: Value Relevance
U.S. GAAP IFRS p-value U.S. GAAP IFRS p-value U.S. GAAP IFRS p-value
Return Model Adjusted R2 0.5835 0.5411 <.0001 0.3776 0.4406 <.0001 0.206 0.1005 <.0001Earnings Response Coefficient (ERC ) 1.0424 0.7052 <.0001 0.6054 1.335 <.0001 0.437 -0.6298 <.0001N 260 267 172 225 500 500
ChangePre (2000 - 2004) Post (2006-2008)
36