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Improved Forecasting of Mutual Fund Alphas and Betas

  • Matthew Spiegel
  • Harry Mamaysky
  • Hong Zhang

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 mutual funds often, but not always, employ dynamic trading strategies their betas move over time in a ways that differ from fund to fund. 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 errors produces an out of sample risk adjusted return of over 4% per annum.

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File URL: http://icfpub.som.yale.edu/publications/2361
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Paper provided by Yale School of Management in its series Yale School of Management Working Papers with number amz2361.

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Date of creation: 01 Jul 2005
Date of revision: 01 Mar 2006
Handle: RePEc:ysm:somwrk:amz2361
Contact details of provider: Web page: http://icf.som.yale.edu/

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  1. Carhart, Mark M, 1997. " On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
  2. Goetzmann, William N. & Ivković, Zoran & Rouwenhorst, K. Geert, 2001. "Day Trading International Mutual Funds: Evidence and Policy Solutions," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 36(03), pages 287-309, September.
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  5. Harry Mamaysky & Matthew Spiegel & Hong Zhang, 2008. "Estimating the Dynamics of Mutual Fund Alphas and Betas," Review of Financial Studies, Society for Financial Studies, vol. 21(1), pages 233-264, January.
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  11. Luboš Pástor & Robert F. Stambaugh, . "Investing in Equity Mutual Funds," CRSP working papers 532, Center for Research in Security Prices, Graduate School of Business, University of Chicago.
  12. Avramov, Doron & Wermers, Russ, 2005. "Investing in mutual funds when returns are predictable," CFR Working Papers 05-13, University of Cologne, Centre for Financial Research (CFR).
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  18. Wayne Ferson & Kenneth Khang, 2002. "Conditional Performance Measurement Using Portfolio Weights: Evidence for Pension Funds," NBER Working Papers 8790, National Bureau of Economic Research, Inc.
  19. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
  20. Berk, Jonathan B, 1995. "A Critique of Size-Related Anomalies," Review of Financial Studies, Society for Financial Studies, vol. 8(2), pages 275-86.
  21. Ferson, Wayne E & Schadt, Rudi W, 1996. " Measuring Fund Strategy and Performance in Changing Economic Conditions," Journal of Finance, American Finance Association, vol. 51(2), pages 425-61, June.
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  23. Bossaerts, Peter & Hillion, Pierre, 1999. "Implementing Statistical Criteria to Select Return Forecasting Models: What Do We Learn?," Review of Financial Studies, Society for Financial Studies, vol. 12(2), pages 405-28.
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