ABSTRACT I examine whether a reversal of an abnormal cut in discretionary investments is associated with the degree to which the cut is reflective of real earnings management (REM) and whether and how it predicts future operating performance. I define a reversal as occurring when a firm cuts discretionary investments to a below-expected level in one period and reverts back to at least the expected level of investment during the next period. Unlike accrual earnings management, REM involves deliberately altering the operations of the firm to influence reported accounting numbers. To the extent that such interventions diverge from optimality, they can expose the firm to real economic costs. I find that a reversal of an abnormal cut in discretionary investments in the year after the cut has taken place is indicative of REM. I further find that, on average, reversing cuts are associated with lower future operating performance, but that such results vary significantly depending on the various incentives to engage in REM, as well as other factors that affect its associated costs and benefits. These findings are of interest to investors, regulators, and academics with respect to the identification and consequences of REM.

ABSTRACT We compare seven fraud prediction models with a cost-based measure that nets the benefits of correctly anticipating instances of fraud against the costs borne by incorrectly flagging non-fraud firms. We find that even the best models trade off false to true positives at rates exceeding 100:1. Indeed, the high number of false positives makes all seven models considered too costly for auditors to implement, even in subsamples where misreporting is more likely. For investors, M-Score and, at higher cut-offs, the F-Score, are the only models providing a net benefit. For regulators, several models are economically viable as false positive costs are limited by the number of investigations regulators can initiate, and by the relatively low market value loss a “falsely accused” firm would bear in denials of requests under the Freedom of Information Act (FOIA). Our results are similar whether we consider fraud or two alternative restatement samples. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: G31; G32; G34; M40.

ABSTRACT Mean reversion in profitability and growth is a well-documented phenomenon; however, comparatively less is known about the level of analysis that best captures mean reversion. In this study, we find that analyzing firms by life cycle stage improves the out-of-sample accuracy of profitability and growth forecasts over analyzing firms pooled across the economy and analyzing firms by industry. The improved accuracy is robust to both short-term and long-term forecasts of profitability and growth. We also find that the improvement in accuracy of forecasts from life cycle analyses is greatest for firms in the introduction and decline stages and for firms with greater uncertainty. Finally, we examine market participants' use of life cycle information in forming expectations. We find inefficient use of life cycle information in analyst forecasts, but not in management forecasts. We also find that life cycle forecasts are associated with year-ahead abnormal stock returns.