Detecting Repeatable Performance
针对基金历史业绩预测未来效果差的问题，提出用随机效应框架从横截面alpha分布中提取信息，降低噪声，改进单个基金alpha的密度预测。
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. Received November 23, 2016; editorial decision November 1, 2017 by Editor Andrew Karolyi. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web Site next to the link to the final published paper online.