RFS Advance Access published online on November 6, 2007
Review of Financial Studies, doi:10.1093/rfs/hhm049
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Estimating the Dynamics of Mutual Fund Alphas and Betas
Old Lane LP
Yale School of Management
INSEAD, Singapore
Address correspondence to Matthew Spiegel, Yale School of Management, P.O. Box 208200, New Haven, CT 06520, or e-mail: matthew.spiegel{at}yale.edu
JEL: G12, G14, G23, C01, C12, C13, C52, C53
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Abstract |
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This article develops a Kalman filter model to track dynamic mutual fund factor loadings. It then uses the estimates to analyze whether managers with market-timing ability can be identified ex ante. The primary findings are as follows: (i) Ordinary least squares (OLS) timing models produce false positives (nonzero alphas) at too high a rate with either daily or monthly data. In contrast, the Kalman filter model produces them at approximately the correct rate with monthly data; (ii) In monthly data, though the OLS models fail to detect any timing among fund managers, the Kalman filter does; (iii) The alpha and beta forecasts from the Kalman model are more accurate than those from the OLS timing models; (iv) The Kalman filter model tracks most fund alphas and betas better than OLS models that employ macroeconomic variables in addition to fund returns.
The authors thank Robert Engle, Wayne Ferson, Will Goetzmann, and Geert Rouwenhorst for their comments. We would like to thank participants at the Rutgers Conference honoring David Whitcomb, the 2004 Meetings of the American Finance Association, and seminar participants at Boston College and the University of Wisconsin-Madison. Hong Zhang thanks the INSEAD Alumni Fund (IAF) for financial support. We also thank two anonymous referees and the editor Cam Harvey for their comments that led to the article's investigation of market-timing measures.