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Fraud Detection and Expected Returns

Messod D. Beneish

出版	SSRN, 2014
URL	http://books.google.com.hk/books?id=EF4mzwEACAAJ&hl=&source=gbs_api

註釋

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.