To Catch A Thief: Can Forensic Accounting Help Predict Stock Returns? via CSInvesting
Messod D. Beneish, Charles M.C. Lee, D. Craig Nichols**
August 15, 2011
Abstract
An earnings manipulation detection model based on forensic accounting principles (Beneish 1999) has substantial out-of-sample ability to predict crosssectional returns. We show that the model correctly identified, ahead of time, 12 of the 17 highest profile fraud cases in the period 1998-2002. Moreover, the probability of manipulation estimated from this model (PROBM) consistently predicts returns over 1993-2007, even after controlling for size, book-to-market, momentum, accruals and the level of open short interest. Separating high PROBM from low PROBM firms within each of these characteristic deciles greatly improves long/short hedge returns. Further analyses show that PROBM
also helps predict future earnings because of its ability to anticipate the persistence of current years’ reported accruals. Overall, our findings offer significant empirical support for the investment approach advocated by forensic accountants.
[Archives] Can Forensic Accounting Help Predict Stock Returns? – Description
In an ideal world, companies’ financial statements always convey a concise, but representatively faithful, portrait of the corporation’s state of financial affairs. Unfortunately, due either to limitations inherent in the language of accounting, or divergent incentives between a firm’s managers and its capital providers, published financial statements often fall short of this ideal. Sometimes one must look deeper into a firm’s financial reports to extract important elements of its true economic conditions.
In recent years, the term “Forensic Accounting” has acquired currency as a moniker for the art and science of carefully investigating company financial records with a view toward forecasting its future prospects. Closely related to the “Quality of Earnings” analysis popularized by O’Glove (1987), Kellogg and. Kellogg (1991) and Siegel (1991), forensic accountants pour over company’s financial statements looking for inconsistencies, irregularities, and other signs of trouble. While these efforts have resulted in individual success stories (see Schilit (2002) for a number of case studies), the evidence to date has been largely anecdotal.
In this study, we provide new evidence on the overall efficacy of “forensic accounting” in detecting corporate fraud and predicting stock returns. Our focus is on a statistical model built by Beneish (1999) that relies on financial statement information to detect accounting manipulation. This model was estimated using data from the period 1982-1988 and its holdout sample performance was assessed in the period 1989-1992. Since the publication of the original study, this model has attracted attention after flagging Enron well in advance of its eventual demise.1 It has been featured in financial statement analysis textbooks (e.g., Fridson 2002, Stickney et al. 2003) and in articles directed at auditors, certified fraud examiners, and investment professionals (e.g., Cieselski 1998, Merrill Lynch 2000, Wells 2001, DKW 2003, Harrington 2005). However, direct evidence of its out-of-sample performance is, once again, ad hoc and anecdotal.
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