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Improved Forecasting of Mutual Fund Alphas and Betas

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  • Harry Mamaysky
  • Matthew Spiegel
  • Hong Zhang
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    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. Copyright 2007, Oxford University Press.

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    File URL: http://hdl.handle.net/10.1093/rof/rfm018
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    Bibliographic Info

    Article provided by European Finance Association in its journal Review of Finance.

    Volume (Year): 11 (2007)
    Issue (Month): 3 ()
    Pages: 359-400

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    Handle: RePEc:oup:revfin:v:11:y:2007:i:3:p:359-400

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    Cited by:
    1. Banegas, Ayelen & Gillen, Ben & Timmermann, Allan & Wermers, Russ, 2012. "The cross-section of conditional mutual fund performance in European stock markets," CFR Working Papers 09-03 [rev.], University of Cologne, Centre for Financial Research (CFR).
    2. Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2010. "Out-of-sample equity premium prediction: economic fundamentals vs. moving-average rules," Working Papers 2010-008, Federal Reserve Bank of St. Louis.
    3. Holmes, Kathryn A. & Faff, Robert, 2008. "Estimating the performance attributes of Australian multi-sector managed funds within a dynamic Kalman filter framework," International Review of Financial Analysis, Elsevier, vol. 17(5), pages 998-1011, December.
    4. Jennifer Huang & Clemens Sialm & Hanjiang Zhang, 2011. "Risk Shifting and Mutual Fund Performance," Review of Financial Studies, Society for Financial Studies, vol. 24(8), pages 2575-2616.
    5. Nik Tuzov & Frederi Viens, 2011. "Mutual fund performance: false discoveries, bias, and power," Annals of Finance, Springer, vol. 7(2), pages 137-169, May.
    6. Tony Chieh-Tse Hou, 2012. "Return persistence and investment timing decisions in Taiwanese domestic equity mutual funds," Managerial Finance, Emerald Group Publishing, vol. 38(9), pages 873-891, September.

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