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Review of Quantitative Finance and Accounting

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01.01.2013 | Original Research

The role of industry classification in estimating discretionary accruals

verfasst von: Karel Hrazdil, Thomas Scott

Erschienen in: Review of Quantitative Finance and Accounting | Ausgabe 1/2013

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Abstract

This study compares the properties of the Global Industry Classification Standard (GICS) with three alternatives: Standard Industrial Classification, North American Industry Classification System, and Fama–French classification. First, we demonstrate that GICS results in more reliable industry groupings for financial analysis and research; in particular, we find that estimations of performance-adjusted discretionary accruals (PADA) based on GICS significantly outperform estimates derived using each of the three alternative classifications systems in capturing discretionary accruals. Second, we show that the difference between GICS and the other systems can provide significantly different results, and hence different inferences, in empirical studies that rely on industry classification. Specifically, we revisit findings by Teoh et al. (J Financ 53[6]:1935–1970, 1998a) and assess the conclusion that initial public offering (IPO) issuers with high abnormal accruals during the IPO year experience subsequent poorer long-term stock performance than issuers with low discretionary accruals do. We find that this result disappears when PADA estimates are based on GICS. Our results call for serious consideration of using GICS classifications in research, either in the primary analysis or as a necessary corroboration.

Vorheriger Artikel Are oil, gold and the euro inter-related? Time series and neural network analysis

Nächster Artikel Asset write-offs discretion and accruals management in Taiwan: the role of corporate governance

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Since 1991, more than 700 published studies have used industry classification when estimating discretionary accruals and have tested hypotheses of earnings management in connection with its motivation (DeFond and Jiambalvo 1994), consequences (Chan et al. 2004a, b), earnings properties (Tucker and Zarowin 2006), analyst coverage (Yu 2008), audit quality (Krishnan 2003), executive compensation (Richardson and Waegelein 2002), and performance of IPOs (Nagata and Hachiya 2007).

Using AAERs to validate tested models is an established approach in financial literature (Bradshaw et al. 2001; Dechow et al. 2003).

Examples of insignificant results obtained using an alternative industry classification system include firms in more-concentrated industries earning lower future stock returns (Hou and Robinson 2006); the effect of bankruptcy announcements on the equity values of competitors being more positive in more-concentrated industries (Lang and Stulz 1992); and turnover of firms’ chief executive officers being negatively associated with industry concentration (DeFond and Park 1999).

For a more detailed review of the NAICS, see Krishnan and Press (2003).

GICS information is available in two forms. First, current gics (mnemonic: spgicx) is the most recent GICS classification for a given year and is available through Research Insight and through Compustat. Second, historical gics (mnemonic: spgicm) provides the most accurate measure of the company’s industry classification as of a given historical date and is available from S&P. It provides the most comprehensive historical coverage for more than 25,000 active and inactive North American firms going back to June 1985. This study obtained the spgicm from S&P and so uses the comprehensive version of GICS.

Whereas misreporting simply relocates an amount of earnings from one period to another, real earnings management changes the firm’s operations and has real consequences beyond earnings reversal. Discretionary accruals estimated based on the existing cross-sectional regression approach are predominantly associated with misreporting.

The more uniform distribution does not necessarily imply a more homogenous grouping; Table 1 merely shows that the distribution of firms within an average GICS functional industry is normal and differs from the other distributions, which may affect variation in discretionary accrual estimates.

BLO explain that for a set number of firms, a classification system having more industry categories will mechanically produce greater R² values. They run Monte Carlo simulations and show that this has little effect on their finding that GICS produces more homogenous industry groups. We accept this result as a given; however, even if there were a modest effect attached to the number of industry categories, researchers typically apply the classification system as it exists, and our tests are conducted on this basis. In this regard, this paper addresses a different facet of industry categorization and is complementary to BLO.

If we divide at the median, the results are the same for ROA in both samples, but GICS is significantly better for only the larger firms for TAC. This is similar to the BLO finding, that GICS homogeneity advantage is most pronounced for larger firms.

The appendix provides demographic data on total accrual regressions by industry group for each classification.

The SEC only provides the AAERs for the most recent 10-year period. The absence of SEC enforcement actions during the earlier and latter years of our sample (pre 1995 and after 2005) is due to the long lag between the date the GAAP violation occurs and the date the action is disclosed by the SEC.

Our abnormal accrual estimation differs from that used by TWW who focused on current accruals. This reflects the evolution of discretionary accruals models and our use of current state of the art models. Importantly, with respect to the methodological issue of whether results can vary solely because of the classification industry system chosen, we are able to replicate their result using SIC codes despite a different discretionary accrual model.

Data are available from http://bear.cba.ufl.edu/ritter/ipodata.htm.

The t values for the two-tailed test are 1.65 for the 10%, 1.96 for the 5%, and 2.58 for the 1% level of significance.

We note that, in failing to reject the null hypothesis that earnings management does not mislead investors, we have not proven the null hypothesis. Whether or not papers with ambiguous results or “no results” would have ever been published is another issue.

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Titel: The role of industry classification in estimating discretionary accruals
verfasst von: Karel Hrazdil
Thomas Scott
Publikationsdatum: 01.01.2013
Verlag: Springer US
Erschienen in: Review of Quantitative Finance and Accounting / Ausgabe 1/2013
Print ISSN: 0924-865X
Elektronische ISSN: 1573-7179
DOI: https://doi.org/10.1007/s11156-011-0268-6

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