Earnings Manipulation and Expected Returns

Posted: 31 Mar 2013

See all articles by Messod D. Beneish

Messod D. Beneish

Indiana University - Kelley School of Business - Department of Accounting

Charles M.C. Lee

Foster School of Business, University of Washington; Stanford University - Graduate School of Business

Craig Nichols

Syracuse University

Date Written: March 29, 2013

Abstract

An accounting-based earnings manipulation detection model has strong out-of-sample power to predict cross-sectional returns. Companies with a higher probability of manipulation (M-score) earn lower returns on every decile portfolio sorted by size, book-to-market, momentum, accruals, and short interest. The predictive power of M-score stems from its ability to forecast changes in accruals and is most pronounced among low-accrual (ostensibly “high-earnings-quality”) stocks. These findings support the investment value of careful fundamental and forensic analyses of public companies.

Keywords: Equity Investments, Fundamental Analysis (Sector, Industry, Company), Valuation of Individual Equity Securities, Company Analysis, Financial Statement Analysis, Financial Reporting Quality, Aggressive Financial Reporting Techniques

Suggested Citation

Beneish, Messod Daniel and Lee, Charles M.C. and Nichols, Craig, Earnings Manipulation and Expected Returns (March 29, 2013). Financial Analysts Journal, Vol. 69, No. 2, 2013, Available at SSRN: https://ssrn.com/abstract=2241717

Messod Daniel Beneish (Contact Author)

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

Foster School of Business, University of Washington ( email )

224 Mackenzie Hall, Box 353200
Seattle, WA 98195-3200
United States

Stanford University - Graduate School of Business

Stanford Graduate School of Business
655 Knight Way
Stanford, CA 94305-5015
United States

Craig Nichols

Syracuse University ( email )

900 S. Crouse Avenue
Syracuse, NY 13244-2130
United States

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