Detecting Repeatable Performance
105 Pages
Posted: 19 Nov 2015
Last revised: 15 Oct 2017
Duke University - Fuqua School of Business; National Bureau of Economic Research (NBER); Duke Innovation & Entrepreneurship Initiative
Texas A&M University, Department of Finance
There are 2 versions of this paper
Date Written: October 13, 2017
Abstract
Past fund performance does a poor job of predicting future outcomes. The reason is noise. Using a random effects framework, we reduce the noise by pooling information from the cross-sectional alpha distribution to make density forecasts for each individual fund's alpha. In simulations, we show that our method generates parameter estimates that outperform alternative methods, both at the population and at the individual fund level. An out-of-sample forecasting exercise also shows that our method generates improved alpha forecasts.
Keywords: Performance evaluation, Mutual funds, Hedge funds, EM algorithm, Fixed effects, Random effects, Regularization, Multiple testing, Bayesian, Rethinking Performance Evaluation
JEL Classification: G10, G11, G12, G14, G23, G24
Suggested Citation:
Suggested Citation
Harvey, Campbell R. and Liu, Yan, Detecting Repeatable Performance (October 13, 2017). Available at SSRN: https://ssrn.com/abstract=2691658 or http://dx.doi.org/10.2139/ssrn.2691658
