D. Craig Nichols

Earnings Manipulation and Expected Returns

An accounting-based earnings manipulation detection model has strong out-of-sample power to predict cross-sectional returns. Companies with a higher probability of manipulation (M-score) earn lower returns on every decile portfolio sorted by size, book-to-market, momentum, accruals, and short interest. The predictive power of M-score stems from its ability to forecast changes in accruals and is most pronounced among low-accrual (ostensibly “high-earnings-quality”) stocks. These findings support the investment value of careful fundamental and forensic analyses of public companies. In our study, we investigated the investment value of a particular form of financial analysis associated with the detection of earnings manipulation. The statistical model we examined (the Beneish model) represents a systematic distillation of forensic accounting principles described in the practitioner literature. Specifically, we investigated a potential link between the probability of manipulation (M-score) generated by the Beneish model and subsequent returns. Although relatively few companies are indicted for accounting fraud, the incidence of earnings manipulation among public companies is likely much higher. We posited that the M-score is informative about a company’s expected returns because the “typical earnings manipulator” is a company that is growing quickly, experiencing deteriorating fundamentals, and adopting aggressive accounting practices. Our main hypothesis was that companies that share traits with past earnings manipulators (i.e., those that “look like manipulators”) represent a particularly vulnerable type of growth stock. Although the accounting games they engage in might not be serious enough to warrant regulatory action, we posited that their earnings trajectory is more likely to disappoint investors (i.e., they have lower “earnings quality”). To the extent that the pricing implications of these accounting-based indicators are not fully transparent to investors, companies that “look like” past earnings manipulators will also earn lower future returns. We found that companies with a higher probability of manipulation (M-score) earn lower returns in every decile portfolio sorted by size, book-to-market, momentum, accruals, and short-interest ratio. These returns are economically significant (averaging just below 1% a month on a risk-adjusted basis) and survive a host of risk controls. We further found that a large proportion of the abnormal return is earned in the short three-day windows centered on the next four quarterly earnings releases, suggesting that our results are due to a delayed reaction to earnings-related news rather than risk-based factors. The robustness of these results, even among highly liquid companies, implies that they are unlikely to be fully explained by transaction costs. We performed three sets of analyses to better understand the nature of the information conveyed by M-score. First, we conducted detailed tests on the joint ability of accruals and M-score to predict returns. We found that the dominance of M-score over accruals is evident in both independent sorts and nested sorts. When companies are sorted on these two variables independently, M-score is particularly effective in predicting returns among low-accrual companies (i.e., companies that have “high earnings quality” according to their accruals ranking). For example, in the lowest-accrual quintile—companies typically viewed as “buys”—the spread in size-adjusted returns between high- M-score companies and low- M-score companies is –19.8% over the next 12 months. Second, we used a difference-in-difference test to examine which individual components of the model contributed the most to its incremental predictive power. Our results show that variables related to a predisposition to commit fraud (sales growth, asset quality index, and leverage) are more important than variables associated with the level of aggressive accounting (accruals, days in receivables, and depreciation expense). Third, we found that the Beneish model’s efficacy is associated with its ability to predict the directional change in current-year accruals (i.e., whether the accruals component of current-year earnings will continue into next year or disappear). Specifically, we found that high- M-score companies have income-increasing (-decreasing) accruals that are more likely to disappear (persist) next year; we observed the exact opposite among low- M-score companies. In other words, M-score provides useful information about the future persistence of current-year accruals. Our study adds to the literature on the effective use of financial information by documenting the usefulness of earnings manipulation detection techniques for earnings quality assessment and return prediction. Our evidence on how and why such techniques work suggests new directions for earnings quality analysis and should enhance future efforts to identify potential over- and undervaluations. Overall, our analyses provide substantial support for the use of forensic accounting in equity investing.

Does Conservative Stock Option Accounting Lead to Aggressive Cash Flow Reporting

In this paper, we examine whether recognizing higher option-based compensation expense leads to lower quality operating cash flows when options are exercised. FAS 123(R) changes the classification of the tax benefit from employee stock options in the statement of cash flows by splitting it into two parts: one reported in the operating section and the other in the financing section. Moreover, the proportion reported in the operating section increases with the amount of compensation expense previously recognized. We show that the tax benefit differs from other items of operating cash flows because it has no reliable association with future earnings. We also show that the predictive ability of current period earnings components declines when the tax benefit is classified as an operating cash flow. Finally, we find evidence that investors overprice the tax benefit, suggesting investors do not distinguish it from other operating cash flows. Taken together, our results suggest that the revised treatment of the tax benefit under FAS 123(R) gives rise to a curious situation where more conservative reporting at the grant date increases the proportion of future tax benefits that is included in the operating section, thereby lowering the quality of future reported cash flows.

The Long and Short of the Accrual Anomaly

The paper provides evidence that the relation between accruals and future returns is not symmetric. We find that firms with low accruals generate insignificant abnormal returns in asset pricing regressions that control for either earnings quality or operating volatility. In contrast, we find that accrual hedge returns are driven by firms with large positive accruals and firms with high probabilities of earnings overstatement. This asymmetry is consistent with our view that upwards rather than downwards earnings management is an important contributor to accrual mispricing. We also find that firms with high accruals are smaller and have higher arbitrage risk (residual return volatility), suggesting that short sellers are unlikely to arbitrage away these negative abnormal returns. We conclude that an omitted risk factor explains results for low accruals and that transaction costs/limits to arbitrage explain the persistence of mispricing for high accruals.