Improved Forecasting of Mutual Fund Alphas and Betas

Posted: 14 Jul 2008

See all articles by Harry Mamaysky

Harry Mamaysky

Columbia University - Columbia Business School

Matthew I. Spiegel

Yale University - Yale School of Management, International Center for Finance

Hong Zhang

Tsinghua University - PBC School of Finance

Multiple version iconThere are 2 versions of this paper

Abstract

This paper proposes a simple back testing procedure that is shown to dramatically improve a panel data model's ability to produce out of sample forecasts. Here the procedure is used to forecast mutual fund alphas. Using monthly data with an OLS model it has been difficult to consistently predict which portfolio managers will produce above market returns for their investors. This paper provides empirical evidence that sorting on the estimated alphas populates the top and bottom deciles not with the best and worst funds, but with those having the greatest estimation error. This problem can be attenuated by back testing the statistical model fund by fund. The back test used here requires a statistical model to exhibit some past predictive success for a particular fund before it is allowed to make predictions about that fund in the current period. Another estimation problem concerns the use of a single statistical model for all available mutual funds. Since no one statistical model is likely to fit every fund, the result is a great deal of misspecification error. This paper shows that the combined use of an OLS and Kalman filter model increases the number of funds with predictable out of sample alphas by about 60%. Overall, a strategy that uses very modest ex-ante filters to eliminate funds whose parameters likely derive primarily from estimation error produces an out of sample risk-adjusted return of over 4% per annum.

Keywords: G12, G13

Suggested Citation

Mamaysky, Harry and Spiegel, Matthew I. and Zhang, Hong, Improved Forecasting of Mutual Fund Alphas and Betas. Review of Finance, Vol. 11, Issue 3, pp. 359-400, 2007. Available at SSRN: https://ssrn.com/abstract=1159306 or http://dx.doi.org/10.1093/rof/rfm018

Harry Mamaysky

Columbia University - Columbia Business School ( email )

3022 Broadway
New York, NY 10027
United States

Matthew I. Spiegel

Yale University - Yale School of Management, International Center for Finance ( email )

135 Prospect Street
P.O. Box 208200
New Haven, CT 06520-8200
United States
203-432-6017 (Phone)
203-432-8931 (Fax)

HOME PAGE: http://som.yale.edu/~spiegel

Hong Zhang

Tsinghua University - PBC School of Finance ( email )

No. 43, Chengdu Road
Haidian District
Beijing 100083
China

HOME PAGE: http://eng.pbcsf.tsinghua.edu.cn/content/details167_7995_x.html

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