Fraud Detection and Expected Returns

53 Pages Posted: 5 Feb 2012  

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

Date Written: February 2, 2012

Abstract

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.

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, G20

Suggested Citation

Beneish, Messod Daniel and Lee, Charles M.C. and Nichols, D. Craig, Fraud Detection and Expected Returns (February 2, 2012). Available at SSRN: https://ssrn.com/abstract=1998387 or http://dx.doi.org/10.2139/ssrn.1998387

Messod Daniel Beneish

Indiana University - Kelley School of Business - Department of Accounting ( email )

1309 E. 10th Street
Bloomington, IN 47405
United States
812-855-2628 (Phone)
812-855-4985 (Fax)

Charles M.C. Lee (Contact Author)

Stanford University - Graduate School of Business ( email )

Stanford Graduate School of Business
655 Knight Way
Stanford, CA 94305-5015
United States
650-721-1295 (Phone)

D. Craig Nichols

affiliation not provided to SSRN

No Address Available

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