1 Has the introduction of IFRS improved accounting quality? A comparative study of five countries Corresponding author: Andreas Jansson, Assistant Professor, PhD, School of Business and Economics, Linnaeus University, Växjö, Sweden e-mail: [email protected]Phone: +46-470-708230 Fax: +46-772288000 Micael Jönsson, Research Assistant, School of Business and Economics, Linnaeus University, Växjö, Sweden Christopher von Koch, Assistant Professor, PhD, School of Business and Economics, Linnaeus University, Växjö, Sweden
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1
Has the introduction of IFRS improved accounting quality? A
comparative study of five countries
Corresponding author: Andreas Jansson, Assistant Professor, PhD, School of Business and Economics, Linnaeus University, Växjö, Sweden
These results provide some support for H2 but no support for H6. Although IFRS appear to
have a measurable impact on forecast dispersion, the degree of intangible asset appears not to
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affect this relationship. When examining cross-country differences, we note that in the UK,
forecast dispersion has not been affected by IFRS. The UK exhibits the smallest GAAP-
difference of all of the countries in the sample, which might explain why there is no effect on
forecast dispersion. At the same time, the UK has the strongest enforcement, which in theory,
should indicate a stronger effect. Another country with a low GAAP-difference is the
Netherlands, in which we detect an effect both in the 10th percentile and the 50th percentile,
which suggests that the impact of GAAP-difference is not so straightforward. The
Netherlands has the weakest enforcement in the sample but still displays positive significant
coefficients. The only other country that has two significant coefficients, in the 50th percentile
and in the 90th percentile, is France, which exhibits both a high GAAP-difference and strong
enforcement. That there is an effect in Sweden, a country with a large GAAP-difference but
weak enforcement, but not the UK, suggests that GAAP-difference is more important than
enforcement. Although the results for the Netherlands may appear to discredit this
interpretation, it is possible that GAAP-difference has a non-linear positive effect, suggesting
support for H4a. It is difficult to argue that enforcement has an effect based on our results,
indicating that H4b is likely false.
Broadly speaking, our results indicate that analysts have become more uniform in their
forecasts since the introduction of IFRS, suggesting that uncertainty among these
professionals has decreased. This decreased uncertainty appears not to be driven by IFRS
asset valuation methods’ better representation of firms’ underlying economic value because
the effect is not more pronounced in firms with higher degrees of intangible assets.
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5. Robustness analysis
As an alternative procedure, we have also estimated our equations using OLS regressions.
Because contrary to quantile regression, OLS is sensitive to skewness and outliers, the sample
was winsorized. A total of 2.5 percent of each tail was altered during this process, which
should resolve the outlier problem. The sample is still highly skewed, so the risk of biased
estimators remains and the result should be interpreted with caution. The estimated models
are provided in appendices 7-8.
The result for forecast accuracy differs slightly from that obtained using quantile regression.
There is a small but significant positive effect in Sweden and France, whereas there is a
significant and negative effect in the UK. We find no significant effect for the sample as a
whole. Obviously, these effects are not consistent and, because of the remaining skewness of
the sample, this result might be viewed as being uncertain. However, it is possible to argue
that this result suggests that IFRS have increased forecast accuracy in Sweden and France
while decreasing it in the UK. The interaction term is significant and positive for Germany
but not for any other country or overall. The result for forecast dispersion is practically the
same as that obtained using quantile regression. We record a positive effect of IFRS in all
countries except the UK. Three countries (Sweden, France and Germany) exhibit significant
interaction terms, but these point in different directions.
The OLS estimations thus do little to challenge the overall pattern identified in the data.
Although a few more coefficients become significant, there is no consistent pattern, which
causes us to suspect that they are spurious.
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6. Conclusions
In summary, our results demonstrate that IFRS have no impact on financial analysts’ forecast
accuracy but a more consistent impact on forecast dispersion, which has diminished in all
countries in the sample except the UK. These results appear not to be driven by the asset
valuation methods of IFRS, but the difference between IFRS and prior GAAP appears to have
an impact.
Prior research suggests that IAS/IFRS have a positive effect on analyst performance overall
(e.g., Ashbaugh and Pincus, 2001; Jiao et al., 2012) or at least if accounting standard
enforcement is strong (Byard et al., 2011; Horton et al., 2012; Preiato et al., 2010), prior
GAAP differ from IFRS (Beuselinck et al., 2010), or prior disclosure standards were of high
quality (Yang, 2010). Our study contributes to this literature because we combine a long time
period with a consistent estimation method that allows us to estimate the impact of IFRS on
both large and small errors without assuming the same impact for the entire distribution and
introduce a new proxy that allows us to determine whether it is the asset measurement
methods of IFRS that affect accounting quality. Our results differ slightly in that we find no
overall improvement in forecast accuracy regardless of prior GAAP-difference or
enforcement, whereas the impact of forecast dispersion is more broad and positive. The only
country in our sample that did not see a diminished forecast dispersion was the UK, a country
in which enforcement is strong and GAAP-difference is minimal. Because Sweden, a country
with weak enforcement of accounting standards but a significant GAAP-difference, displays
decreasing forecast dispersion, our results suggest, in line with Beuselinck et al. (2010), that
GAAP-difference is the crucial factor.
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Because forecast dispersion is a measure of information asymmetry (Krishnaswami and
Subramaniam, 1999), we claim that some aspect of information asymmetry has likely
decreased under IFRS although forecast accuracy has not increased. This effect is an
indication that analysts have a more even playing field, suggesting that some previously
private or withheld information is now available to more analysts, which would explain why
forecast dispersion has decreased. This explanation would imply that the qualitative increase
produced by IFRS may therefore be more connected to increasing information harmonization
and comparability than with making firms’ financial reports more accurately represent
underlying economic value. Standard setters argue that fair value accounting provides more
relevant information for predictions of firm performance (Hitz, 2007). If this is the case,
perhaps analysts processed accounting numbers acquired from historical cost accounting to
obtain estimates of fair value under their local GAAP. With IFRS, this processing is no longer
as necessary, as it is conducted by firms themselves. This method is likely to result in less
forecast dispersion because all analysts will have access to the same fair value accounting
numbers, although on average, these estimates may not be superior to the average estimates
processed under national GAAP. If this interpretation is correct, the implication is that IFRS
accounting methods create a more level playing field for accounting users but without
necessarily producing higher predictive value. Another more speculative implication is that
fair value accounting (which is more pronounced in IFRS than in previous GAAP) is
preferred over historical cost accounting by financial analysts.
Soderstrom and Sun (2007) argue that there is a direct link between accounting standard and
accounting quality. If this link exists, transitioning to an accounting standard of higher quality
should increase accounting quality. Our results imply that it is fruitful to distinguish between
how accounting quality affects users’ accuracy and how it affects users’ consistency.
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Although IFRS appears to affect consistency, we have little evidence to suggest that it affects
users’ accuracy. Therefore, IFRS can be viewed as a standard of higher quality than
previously used local accounting standards in Sweden, France, the Netherlands and Germany,
but only in the sense of consistency. This conclusion would appear to imply that there is a
need to develop additional measures of accounting quality that consider this distinction for
both practical and research applications because it is difficult to see how the standard
measures of accounting quality (earnings management, timely loss recognition and value
relevance) can capture this dimension.
A number of factors beyond the standards themselves could explain the limited effect of IFRS
on analyst performance. Our empirical design has limited power in isolating the effects of
IFRS from those of, for example, general macroeconomic changes or general developments in
financial markets. However, we do use control variables to mitigate those time periods in
which the job of prediction is especially troublesome. Another design limitation is the
assumption that financial analysts use financial reports as a primary source of information
when formulating predictions. Although this idea is supported by the literature (Block, 1999;
Roger and Grant, 1997) and our findings regarding forecast dispersion, more thorough
documentation is needed to demonstrate how analysts actually use financial reports for
developing forecasts and how different sources of information relate to one another. Such
documentation will aid us in gaining a better understanding of the effects of changing
accounting standards. A number of previous studies (Ball, 2006; Zeff, 2007) suggest that the
implementation of IFRS will not have a uniform impact in all countries and that
implementation will take time. Kvaal and Nobes (2012) find that national patterns still
prevail. Our design is likely better suited to distinguishing between immediate and uniform
effects.
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Despite these limitations, we conclude that when evaluated from a decision usefulness
perspective, IFRS has limited impact on accounting quality in the examined countries;
however, this impact is connected more to the presentation of more consistent pictures for
predictions of firm performance than to the presentation of more accurate pictures.
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Table 1. GAAP difference and enforcement
GAAP‐difference Bae et al. 2008
Enforcement Preiato et al. 2010
Sweden 10 14Netherlands 4 12
France 12 21Germany 11 18
United Kingdom 1 24
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Table 2. Independent variables Variable Explanation Predicted sign of the
dependent variables Number of analysts The number of analysts following a company. + Market value Market value is measured as the company’s
market value at the beginning of the fiscal year. +
Trading volume Trading volume refers to the company’s absolute daily trading volume during the first month of the fiscal year.
+
Profit/Loss Loss, a dummy variable that takes the value of 1 if the company reported a loss and 0 otherwise.
-
Earnings surprise The absolute value of the year’s earnings per share, minus the previous year’s earnings per share, scaled by the share price at the beginning of the fiscal year. EPSt is the earnings per share during period t (of a given year), and EPSt-1 is the earnings per share in period t-1 (the previous year).
-
Std ROE The company’s standard deviation return on equity over the previous three years.
-
Accounting standard followed
A dummy variable that takes the value of 1 if the company used IFRS for preparing last years’ financial reports and 0 otherwise.
+
Proportion of intangible assets
Reported value of intangible assets divided by reported total assets.
?
Interaction term Accounting standard followed multiplied by proportion of intangible assets.
+
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Table 3. Sample statistics
Number of observations Mean Std Dev
Country Number
of firms Total
sample
Valid
Forecast
accuracy
Valid
Forecast
dispersion
Forecast
accuracy
Forecast
dispersion
Forecast
accuracy
Forecast
dispersion
Sweden 259 1829 1829 1460 -0.0633 -0.0201 0.1748 0.0424
Table 6. Quantile regression on dependent variable ‘forecast accuracy’ – results for the variable ‘accounting standard followed’ and the interaction variable between the proportion of intangible assets and accounting standard followed
(1) (2) (3) (4) (5) (6) Sweden Netherlands France Germany United Kingdom All Panel A: Quantile 10 Standard 0.0137 -0.0213 0.0101 0.0040 0.0021 -0.0030 (0.0116) (0.0140) (0.0084) (0.0118) (0.0029) (0.0048) Interaction -0.0390 -0.0954 -0.0061 0.0591 0.0036 0.0113 Term (0.0450) (0.0628) (0.0327) (0.0551) (0.0103) (0.0192) Panel B: Quantile 50 Standard 0.0053 -0.0003 0.0032 0.0024 0.0005 0.0014 (0.0053) (0.0065) (0.0030) (0.0063) (0.0027) (0.0018) Interaction -0.0232 0.0029 -0.0051 -0.0078 0.0011 -0.0017 Term (0.0207) (0.0293) (0.0119) (0.0292) (0.0095) (0.0071) Panel C: Quantile 90 Standard 0.0014 0.0010 0.0008 0.0007 0.0000 0.0001 (0.0018) (0.0015) (0.0010) (0.0017) (0.0003) (0.0004) Interaction -0.0028 -0.0007 -0.0014 -0.0012 0.0005 0.0004 Term (0.0069) (0.0066) (0.0039) (0.0081) (0.0009) (0.0017) Standard errors in parentheses * p < .1, ** p < .05, *** p < .01
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Table 7. Quantile regression on dependent variable ‘forecast dispersion’ – results for the variable ‘accounting standard followed’ and the interaction variable between the proportion of intangible assets and accounting standard followed
(1) (2) (3) (4) (5) (6) Sweden Netherlands France Germany United Kingdom All Panel A: Quantile 10 Standard 0.0088 0.0038 0.0093** 0.0099 0.0008 0.0044*** (0.0056) (0.0077) (0.0040) (0.0084) (0.0021) (0.0015) Interaction -0.0124 0.0043 -0.0114 0.0002 0.0076 -0.0005 Term (0.0213) (0.0349) (0.0149) (0.0403) (0.0075) (0.0059) Panel B: Quantile 50 Standard 0.0025** 0.0020* 0.0018** 0.0020* 0.0005 0.0007** (0.0011) (0.0011) (0.0008) (0.0012) (0.0004) (0.0004) Interaction -0.0075* -0.0049 -0.0022 0.0015 -0.0004 -0.0002 Term (0.0043) (0.0051) (0.0028) (0.0058) (0.0015) (0.0014) Panel C: Quantile 90 Standard 0.0001 0.0012** 0.0003 0.0002 0.0001 0.0001 (0.0004) (0.0006) (0.0003) (0.0004) (0.0001) (0.0001) Interaction -0.0013 -0.0008 -0.0012 0.0016 0.0001 0.0000 Term (0.0016) (0.0026) (0.0010) (0.0020) (0.0004) (0.0005) Standard errors in parentheses * p < .1, ** p < .05, *** p < .01
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Appendix 1 Quantile regression (10%) on dependent variable ‘forecast accuracy’