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Earnings management in the short- and long-term post-regulation periods

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Abstract

We examine the change in the level and significance of accrual and real earnings management over the period 1996–2018. Our univariate results show that both accrual and real earnings management continue to persist, although they have significantly decreased over time. After controlling for significant factors that have been shown to affect earnings management, we find that both accrual and real-activities earnings management have significantly decreased in the short term after the regulations, but accrual earnings management reverted back to its pre-regulation levels in the long run. Further, contrary to the findings of prior research (e.g., Cohen et al. in Account Rev 83:757–787, 2008) that managers have shifted away from accruals to real earnings management in the short-term post-regulation period, we find evidence that firms actually reduced real earnings management both in the short- and long-term post-regulation periods. The results are robust to alternative sample designs and inclusion of additional control variables, and go against the widely held beliefs that accruals-based earnings management is significantly less prevalent now than it used to be.

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Notes

  1. We also examine two aggregate measures of earnings management. Please see footnote 14. We do not examine abnormal cash flows from operations (ACFO) because it is difficult to interpret the coefficients of post-regulation variables in an ACFO regression. Specifically, in addition to price discounts and credit terms, ACFO is affected by abnormal production costs and abnormal selling and administrative expenses in different directions.

  2. In a different context, examining the impact of CEO and CFO power on accruals and real earnings management, Baker, Lopez, Reitenga, Ruch (2019) find that in the post-SOX period the effect of relative CEO power on accrual earnings management subsides, whereas the effect of relative CFO power on real earnings management persists.

  3. For an excellent summary of the effects of SOX, including a detailed discussion of a number of studies that document improvements in accounting quality following SOX, see Coates and Srinivasan (2014). Earnings management also has been studied around other regulations. For example, Liu (2020) examines the effect on real and accrual-based earnings management of requiring an engagement partner’s signature on the auditor report, and finds no significant change in the usage of accrual-based and/or real earnings management for firm-years suspected of beating/meeting the zero, last-year, or analyst forecast consensus earnings threshold from the pre- to post-signature period.

  4. Exceptions are the studies by Kedia et al. (2015), and Espahbodi et al. (2015), as discussed later.

  5. This conjecture is consistent with the findings of Espahbodi et al. (2015) that analyst forecast properties improved in the short run but deteriorated in the long run following the increase in regulations. They attribute the continued problem with the information environment to the quality of financial reports and relate their conclusion to the Hawthorne effect, and Tversky and Kahneman’s (1974) human decision-making biases. The conjecture is also consistent with Kedia et al. (2015) who find copying of poor accounting practices reappears from 2006 to 2008, possibly because the sting associated with SOX has worn off.

  6. Courts also may judge, ex post, that the executive certification of financial statements was inadequate or inaccurate.

  7. Although some of these requirements did not take effect until 2004, their inclusion in the Act likely affected earnings management practices upon the passage of SOX.

  8. We end the short-term post-regulation period in 2006 because prior research showing improvement in earnings quality following SOX, as discussed in the Introduction section, ended their sample period on or before quarter 4 of that year. The exception is Kedia et al. (2015) who studies copying of poor accounting practices and find that such practices stopped during the years 2003–2005 (in the short run after SOX) but reappeared during 2006–2008 (in the longer term, consistent with our finding).

  9. Including the scandal years in the study would only strengthen our results, as prior research (e.g., Cohen et al. 2008) indicates that the scandals and earnings management were at their peaks during 2000–2002, leading to the passage of SOX and other aforementioned regulations.

  10. We repeat our analyses using the 593 firms to see if the results hold for a constant sample of firms. The results are discussed in the Sensitivity subsection.

  11. Theoretically, earnings management may not always indicate managerial opportunism and rent-extraction type behavior. For example, Subramanyam (1996) shows that abnormal total accruals are priced by investors, although he states that the measurement error in the discretionary accruals proxy may explain his results. Similarly, Gunny (2010, 886) concludes that using real earnings management by firms just meeting earnings benchmarks (zero and last year’s earnings) “…is not opportunistic, but consistent with managers attaining benefits that allow better future performance or signaling.” However, consistent with the majority of prior published papers (e.g., Roychowdhury 2006; Graham et al. 2005; Cohen et al. 2008; Zang 2012), we assume that all types of earnings management are opportunistic and hence value-decreasing.

  12. Srivastava (2019) shows that the inclusion of the last five variables (market capitalization, lagged ROA, market to book as a measure of growth, future sales, and the lagged dependent variable) significantly reduces the measurement errors in the usual proxies for real earnings management and improve the inferences of tests involving real earnings management (our Eqs. 2 through 4). We extend this logic to our accrual measure (Eq. 1) as he states that any model that estimates a firm’s abnormal or manipulative behavior by comparison to that of other industry peers should account for variations in strategy-driven characteristics across firms. As Srivastava acknowledges, however, these enhancements will overcorrect for misspecifications if earnings management varies with the firm’s opportunity set, if the firm always manages earnings, or if other firms in the industry equally manage earnings.

  13. To correct for serial correlation induced by the inclusion of the lagged dependent variable, we use the AUTOREGRESS procedure in SAS to estimate Eqs. (1) through (4).

  14. Srivastava (2019) also uses a cohort-adjusted measure, calculated by subtracting the abnormal earnings measure of a control firm from that of each sample firm. A control firm is the one that belongs to the same industry and has the same listing vintage (proxy for age) as a sample firm, and the closest size. We don’t adjust our abnormal earnings measures based on firm size or performance for the reasons explained in the rest of this paragraph, and because using cohort-adjusted measures actually decreased both the percentage improvement in true positives and the percentage reduction in false negatives for abnormal production cost and abnormal selling and administrative expense (Srivastava, 2019, Table 5, Panel D).

  15. We also examine two aggregate measures of earnings management: abnormal real and abnormal total earnings management (AREM and ATEM). AREM is the absolute value of the sum of abnormal production cost and negative of abnormal selling and administrative expenses, the two dominant real earnings management measures; and ATEM is the absolute value of the sum of abnormal revenues, abnormal production costs, and negative of abnormal selling and administrative expenses, the three measures that significantly affect earnings. We use the negative of abnormal selling and administrative expenses because it affects earnings in the opposite direction of abnormal revenues and abnormal production costs. We discuss some of the results for these aggregate measures, but for brevity do not tabulate them.

  16. We do not include all potentially significant variables to ensure a more parsimonious model and avoid severe multicollinearity issues. However, we add two additional corporate governance variables (executives’ options and shares incentive ratios) to our model in our sensitivity tests. Also, we do not examine the interaction between the incentives and the time intervals. This is in contrast with some prior research (e.g., Cohen et al. 2008) which includes some interaction effects. Including interaction effects in a model induces severe multicollinearity and if we were to add the interaction effects to our 17 variables plus the one-digit industry fixed effect and the firm fixed effect, the number of variables would increase substantially and although we will have a much better fit (higher R2), the results will be uninterpretable.

  17. Similarly, to the extent that managerial compensation is based on reported earnings, managers have an incentive to increase (e.g., considering job security or bonus) or decrease the reported earnings (e.g., considering the upper limit in some bonus plans). We include executives’ options and shares incentive ratios in our sensitivity tests as additional variables in our model.

  18. Rather than including additional control variables to capture the incentive to meet or beat earnings target, we rerun our regressions on the suspect firms only. Following prior research (e.g., Cheng et al. 2016; Roychowdhury 2006), we define suspect firms as those with actual EPS less the most recent consensus analyst forecast before earnings announcement between 0 and 1 percent of the closing stock price. The results are discussed in our sensitivity tests.

  19. Many studies suggest that the composition of the board of directors determines its effectiveness. For example, Rosenstein and Wyatt (1990) and Klein (2002) argue that boards composed largely of outsiders are more effective than boards with few outsiders. Jensen (1994) argues that boards of directors are ineffectual monitors when the board’s equity ownership is small and when the CEO is also the Chairman of the Board. Prior research has also used numerous other measures of corporate governance. We use the percentage ownership by institutional investors as the only measure of corporate governance to avoid losing degrees of freedom due to missing data and to have a more parsimonious model (although in sensitivity tests we consider two additional corporate governance variables.

  20. Results for our aggregate measures AREM, which is the sum of APROD, and negative ASGA and ATEM, which is the sum of AREV, APROD, and negative of ASGA, are consistent with those of their individual components.

  21. Both our aggregate measures, AREM and ATEM, also decreased at the 1% levels in the short- and long-term post-regulation periods, respectively, suggesting that overall regulations may have been effective in reducing earnings management.

  22. The results are somewhat different from the univariate results because of the presence of control variables. For example, while the Wilcoxon Z-Score and T-Statistic show that the median and mean ATACRU and APROD have decreased significantly in the long-term post-regulation period compared to the pre-regulation period, the regression results show that such decreases are not significant at the conventional levels.

  23. The contradiction is potentially due to the addition of five variables, representing financial performance and strategy-driven firm characteristics, to our Eqs. 1 through 4 to reduce measurement errors in the usual proxies for earnings management, following Srivastava (2019). When we drop those five variables from our regressions 1 through 4, our results are consistent with prior research and show an increase in ASGA and our two aggregate measures both in the short- and long-term post-regulation periods. As Srivastava acknowledges, the addition of the five variables will overcorrect for misspecifications if earnings management varies with the firm’s opportunity set, if the firm always manages earnings, or if other firms in the industry equally manage earnings. Alternatively, prior studies may have simply arrived at the wrong conclusion due to the measurement errors in their earnings management proxies.

  24. These variables are calculated following Bergstresser and Philippon (2006). Specifically, executives’ options incentive ratio = (0.01* stock price * number of options held by executives) divided by (SENSITIVE + total current compensation); executives’ shares incentive ratio = (0.01 * stock price * number of shares held by executives) divided by (SENSITIVE + total current compensation); and SENSITIVE = 0.01 * stock price * (number of shares held by executives + number of options held by executives).

  25. Earlier studies (e.g., Cohen et al. 2008) do not scale the earnings surprise by price, and define suspect firms as those that meet or beat analyst forecasts by one cent per share or less.

  26. Non-tabulated results show that both aggregate measures, AREM and ATEM decreased in the short term and the long term after the regulations. The coefficients of POSTREG_ST and POSTREG_LT in these two regressions were significant at the 1% level.

  27. The differences in our findings could be due to the measurement errors in the usual proxies for earnings management used in previous studies. Alternatively, as Srivastava (2019) acknowledges, the earnings expectation models used in this study might have overcorrected for misspecifications, resulting in observance of lower earnings management levels.

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Acknowledgements

We appreciate the suggestions and guidance from Bharat Sarat and Rob Weigand. Also appreciated are the helpful comments from the participants at the 2019 Journal of Contemporary Accounting and Economics Annual Symposium and the 2020 Hawaii Accounting Research Conference. We are grateful for the suggestions and guidance from C.F. Lee (editor) and two anonymous reviewers. The remaining errors are ours.

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Authors and Affiliations

  1. Western Illinois University, Macomb, USA

    Hassan Espahbodi

  2. Christopher Newport University, Newport News, USA

    Reza Espahbodi

  3. New York University, New York, USA

    Kose John

  4. University of Louisville, Louisville, USA

    Hua Christine Xin

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  1. Hassan Espahbodi
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  2. Reza Espahbodi
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  3. Kose John
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Correspondence to Reza Espahbodi.

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Appendix 1: Variable definitions

Appendix 1: Variable definitions

AGE is the number of years a firm has been listed on Compustat database as of the end of prior fiscal year.

APROD is abnormal production costs. It is the absolute value of the residual from regressing production costs (PROD) on an intercept, sales, change in sales in the current and preceding years, natural log of market capitalization, lagged return on assets, market to book ratio, next year’s sales, and lagged PROD. A separate regression is estimated for each two-digit SIC industry for each year. PROD, intercept, sales, change in sales in the current and preceding years, and next year’s sales are scaled by total assets at the beginning of the year. A constant is added to each regression to reduce misspecification.

AREV is abnormal revenue. It is the absolute value of the residual from regressing the change in accounts receivable (ΔAR) on an intercept, change in net sales of the first three quarters, change in net sales of the fourth quarter, natural log of market capitalization, lagged return on assets, market to book ratio, next year’s sales, and lagged ΔAR. ΔAR, intercept, change in net sales of the first three quarters, change in net sales of the fourth quarter, and next year’s sales are scaled by total assets at the beginning of the year. A separate regression is estimated for each two-digit SIC industry for each year. A constant is added to each regression to reduce misspecification.

ASGA is abnormal selling and administrative expenses. It is the absolute value of the residual from regressing selling and administrative expenses (SGA) on an intercept, lagged sales, natural log of market capitalization, lagged return on assets, market to book ratio, next year’s sales, and lagged SGA. A separate regression is estimated for each two-digit SIC industry for each year. SGA, intercept, lagged sales, and next year’s sales are scaled by total assets at the beginning of the year. A constant is added to each regression to reduce misspecification.

ATACRU is the absolute value of abnormal total accruals (TACRU) estimated using the Jones (1991) model adjusted for financial performance and strategy-driven firm characteristics. Specifically, ATACRU is the absolute value of the residual from the regression of total accruals on an intercept, change in sales, gross property plant and equipment (PPE), natural log of market capitalization, lagged return on assets, market to book ratio, next year’s sales, and lagged TACRU. TACRU, intercept, change in sales, PPE, and next year’s sales are scaled by total assets at the beginning of the year. A constant is added to each regression to reduce misspecification.

AREM is abnormal real earnings management. It is the absolute value of the sum of abnormal production cost and negative of abnormal selling and administrative expense.

ATEM is abnormal total earnings management. It is the absolute value of the sum of abnormal revenue, abnormal production cost, and negative of abnormal selling and administrative expense.

AUDITOR refers to the audit firm; it is set to one if the audit firm is a Big-Four, and to zero otherwise.

DEBISSUE is one if the firm issued debt during the current or following year, and zero otherwise.

EQISSUE is one if the firm issued or repurchased shares amounting to three percent or more of total outstanding shares during the current or following year, and zero otherwise.

FOLLOW is the number of analysts following a firm.

INSTOWNPCT is the fraction of voting shares held by institutional investors.

LEVERAGE is debt as a percentage of total assets.

LITIGATION is one if the firm is in an industry with a high litigation risk, and zero otherwise. High litigation industries are defined as SIC codes 2833-2836, 3570-3577, 3600-3674, 7371-7379, and 8731-8734 (Matsumoto 2002).

LMKVAL is the natural log of market capitalization, a measure of size.

LTA is the natural log of total assets (TA), another measure of size.

MKBK is market value divided by book value of equity.

POSTREG_LT is equal to one in the long-term post-regulation period (firm-year observations corresponding to fiscal years 2007–2018), and zero otherwise.

POSTREG_ST is equal to one in the short-term post-regulation period (firm-year observations corresponding to fiscal years 2003–2006), and zero otherwise.

PPE is gross property, plant, and equipment.

ROA is income before extraordinary items as a percentage of beginning assets.

SPCHG is the percentage change in S&P index from the previous fiscal year.

SALES1_3 is net sales (SALES) of the first three quarters.

SALES4 is net sales of fourth quarter.

σCFO and σREV are the standard deviations of cash flow from operations and revenues over the last five years, respectively, deflated by total assets at the beginning of the year.

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Espahbodi, H., Espahbodi, R., John, K. et al. Earnings management in the short- and long-term post-regulation periods. Rev Quant Finan Acc 58, 217–244 (2022). https://doi.org/10.1007/s11156-021-00993-2

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