SSRN-Improved Forecasting of Mutual Fund Alphas and Betas by Matthew Spiegel, Harry Mamaysky, Hong Zhang
Spiegel, Mamaysky, and Zhang look at measurement errors in measuring mutual fund performance and find that past researchers have not always gotten it right. In fact, when correct betas are used, it seems that find managers do better than we had thought.
A few quick quotes:
Defnitely an I^3 designation! (Insightful, Interesting, and Important)
"...(betas) are poorly estimated. This in turn results in a systematic bias in the estimated alphas. 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"
"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, this paper’s findings offers support for at least part of their thesis;
managerial skill exists but its benefit to mutual fund investors is short lived."
Spiegel, Matthew I., Mamaysky, Harry and Zhang, Hong , "Improved Forecasting of Mutual Fund Alphas and Betas" (January 4, 2005). Yale ICF Working Paper No. 04-23. http://ssrn.com/abstract=567284