Working Paper: NBER ID: w22134
Authors: Campbell R. Harvey; Yan Liu
Abstract: We show that the standard equation-by-equation OLS used in performance evaluation ignores information in the alpha population and leads to severely biased estimates for the alpha population. We propose a new framework that treats fund alphas as random effects. Our framework allows us to make inference on the alpha population while controlling for various sources of estimation risk. At the individual fund level, our method pools information from the entire alpha distribution to make density forecast for the fund's alpha, offering a new way to think about performance evaluation. In simulations, we show that our method generates parameter estimates that universally dominate the OLS estimates, both at the population and at the individual fund level. While it is generally accepted that few if any mutual funds outperform, we find that the fraction of funds that generate positive alphas is accurately estimated at over 10%. An out-of-sample forecasting exercise also shows that our method generates superior alpha forecasts.
Keywords: No keywords provided
JEL Codes: G10; G11; G12; G14; G23
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| OLS method (C67) | biased estimates of the alpha population (C51) |
| random alpha model (C59) | parameter estimates that dominate OLS estimates (C51) |
| random alpha model (C59) | accurate estimates of positive alphas (C51) |
| random alpha model (C59) | improved density forecasting of fund alphas (G17) |
| random alpha model (C59) | better estimates of fraction of funds with positive performance (G11) |
| random alpha model (C59) | more accurate reflection of alpha population (C46) |
| random alpha model (C59) | refined individual fund estimates (G23) |