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Abnormal real operations, real earnings management, and subsequent crashes in stock prices

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Abstract

We study the impact of firms’ abnormal business operations on their future crash risk in stock prices. Computed based on real earnings management (REM) models, firms’ deviation in real operations (DROs) from industry norms is shown to be positively associated with their future crash risk. This association is incremental to that between discretionary accruals (DAs) and crash risk found by prior studies. Moreover, after Sarbanes–Oxley Act (SOX) of 2002, DRO’s predictive power for crash risk strengthens substantially, while DA’s predictive power essentially dissipates. These results are consistent with the prior finding that managers shift from accrual earnings management to REM after SOX. We further develop a suspect-firm approach to capture firms’ use of DRO for REM purposes. This analysis shows that REM-firms experience a significant increase in crash risk in the following year. These findings suggest that the impact of DRO on crash risk is at least partially through REM.

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Notes

  1. This is less of a concern when DRO is used as the dependent variable, which is the case in most prior REM studies.

  2. Suspect firms are those that report earnings that are just above zero or just above last years'.

  3. Substitution effect between REM and AEM is also evident in Zang (2012).

  4. While not directly analyzing the consequence of REM on operating performance, Kim and Sohn (2013) and Ge and Kim (2014a, b) find that DRO increases the cost of capital.

  5. In their dynamic rational expectations model, Benmelech et al. (2010) show that equity incentives may induce managers to conceal bad news about future growth options through investment policies. Those suboptimal real activities are implemented to support the pretense; Kedia and Philippon (2009) predict and find firm-level evidence that, in equilibrium, low-productivity firms need to hire and invest excessively in order to appear as high productivity firms. Consistent evidence is also found in the results by McNichols and Stubben (2008).

  6. The impact of DRO on stock prices may also depends on firm characteristics. For example, Chen et al. (2012) shows that increases in R&D are associated with much more upward movements in stock prices for firms that have "focus" strategy as opposed to those with "diversification" strategy.

  7. The technical reason for this conjecture is well explained in Hutton et al. (2009). Our DA and DRO measures are residuals from Eqs, (1, 2, 3, 5). Those measures include both intentional earnings management (REM and AEM) and errors due to problems in model fitness. If SOX reduces AEM (increases REM), then in the post-SOX period, the DA measure should be composed of more model errors (DRO measure should be composed of less model errors). Assuming that those errors due to fitness problems are random and not correlated with crash variables, we should see a decrease in DA's power (an increase in DRO's) to predict crash.

  8. The R&D and advertising expenses are set to zero if they are not available in COMPUSTAT.

  9. In the study by Ascioglu et al. (2012), both signed and unsigned REM proxies are used.

  10. Even though we do not think DRO is a direct measure of REM, it captures both earnings-inflating and earnings-deflating REM. Special corporate events, such as stock repurchases or management buyouts, may be surrounded by income-decreasing activities since mangers benefit from lowered reacquisition prices. Existing literature has already provided such evidence for AEM (see Jones 1991; Perry and Williams 1994; DeFond and Subramanyam 1998; Baker et al. 2003; Guan et al. 2005; Gong et al. 2008). The evidence about downward REM is scarce in this relatively young literature. The working papers by Mao and Renneboog (2013) and Hasan et al. (2014) have provided some supporting empirical evidence.

  11. However, one difference between the reversion of DRO and DA is that the latter is a mechanic reversion.

  12. RM_CFO is not used here because of the aforementioned ambiguity problem and also the inconsistent results for this measure shown in Table 4; our measure of discretionary expenses (DISX) already includes R&D expenses, so it represents general expense-related real earnings management.

  13. For expense-related REM proxies, Gunny (2010) actually identifies the lowest quintile as REM firms (firms that cut expenses to boost earnings). However, for convenience, our REM_DISX has already been multiplied by negative one so positive/negative values have upward/downward effects on earnings. As a result, we identify as REM firms those in the highest quintile. REM_CFO is not used to identify REM firms because, first, it is an ambiguous measure of REM for the aforementioned reasons, and, second, our results in Table 4 indicate that it does not significantly predict crashes, potentially due to the first reason.

  14. To avoid confusion, we use the following terminologies in a consistent manner throughout this paper: (1) we use "probability to observe crash during a full year", "average crash probability", etc. to refer to the average number of firm-year observations that have at least one crash during a fiscal year; (2) we use "marginal impact on crash risk", "change in crash risk", etc. (instead of crash likelihood) to refer to the results from logistic regressions, in which the dummy Crash is the dependent variable; (3) we use "crash likelihood" when the dependent variables are our two continuous variables that measure how crash-prone a firm is: NCSKEW and DUVOL.

  15. The coefficients of DRO in Columns (3, 6, 9) are all smaller than those in (2, 5, 8), suggesting the importance of controlling for operating volatility measures.

  16. Our robustness analysis shows that DRO_CFO's impact on crash risk becomes significant when we consider the non-linearity in the relationship.

  17. This is the case because those results are from logistic regressions.

  18. Setting DRO2 equal to its own mean −1/2 SD overstates the impact from the square term, because DRO2 is much more dispersed in value than DRO.

  19. Our DRO and DA measures have been winsorized on 1 % level on both ends.

  20. The Pseudo R squares reported for the logistic regressions in the previous section, being around 1 %, are McFadden's Pseudo R Square. Like many other varieties of Pseudo R square, it is not directly comparable to the adjusted R square from OLS.

  21. The values in Panel B are based on one standard deviation change around the mean of earnings management (mean −0.5 SD to mean + 0.5 SD).

  22. This comparison is made based on one standard deviation change in DRO, as shown in Table 5 (B).

  23. Comparison is made based on DRO_3's impact.

  24. This is the case because real activities take time to carry out, while accruals manipulation can be done at the last minute. Also, technically speaking, DA can be done even after the end of the fiscal period end, as long as it is before the statements issuance date.

  25. We also used multinomial logistic regressions to re-examine the issue. Since EA crash and non-EA crash are not mutually exclusive for a given firm-year, we have to drop firm-years that have two or more crashes during the year. The results are extremely close to what we show in Table 9.

  26. Relevant value range: from 1st percentile to 99th percentile.

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Acknowledgments

We are thankful to the discussants and participants at the concurrent sessions at the 2012 AAA annual meeting in D.C. and 2012 FMA annual meeting in Atlanta, GA. Usual caveats apply. This research has begun as Chapter 2 of Lingxiang Li’s dissertation submitted in partial fulfillment of the requirements for a Ph.D. at Rensselaer Polytechnic Institute.

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Authors and Affiliations

  1. Lally School of Management, Rensselaer Polytechnic Institute, 110 8th St., Troy, NY, 12180, USA

    Bill Francis

  2. School of Business, Fordham University, 1790 Broadway, New York, NY, 10019, USA

    Iftekhar Hasan

  3. Bank of Finland, P.O. Box 160, Helsinki, FI-00101, Finland

    Iftekhar Hasan

  4. School of Business, State University of New York-Old Westbury, 223 Store Hill Rd, Old Westbury, NY, 11568, USA

    Lingxiang Li

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  1. Bill Francis
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Correspondence to Iftekhar Hasan or Lingxiang Li.

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Francis, B., Hasan, I. & Li, L. Abnormal real operations, real earnings management, and subsequent crashes in stock prices. Rev Quant Finan Acc 46, 217–260 (2016). https://doi.org/10.1007/s11156-014-0468-y

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