Fraud Detection and Expected Returns
Messod Daniel Beneish
Indiana University - Kelley School of Business - Department of Accounting
Charles M.C. Lee
Stanford University - Graduate School of Business
D. Craig Nichols
affiliation not provided to SSRN
February 2, 2012
An accounting-based model has strong out-of-sample power not only to detect fraud, but also to predict cross-sectional returns. Firms with a higher probability of manipulation (MSCORE) earn lower returns in every decile portfolio sorted by: Size, Book-to-Market, Momentum, Accruals, and Short-Interest. We show that the predictive power of MSCORE is related to its ability to forecast the persistence of current-year accruals, and is most pronounced among low-accrual (ostensibly high earnings-quality) stocks. Most of the incremental power derives from measures of firms’ predisposition to manipulate, rather than their level of aggressive accounting. Our evidence supports the investment value of careful fundamental analysis, even among public firms.
Number of Pages in PDF File: 53
Keywords: earnings manipulation detection, accounting fraud, returns prediction, market efficiency, financial statement analysis, market learning, information, behavioral finance, price discovery
JEL Classification: G14, M41, G12, G20working papers series
Date posted: February 5, 2012
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