The Review of Financial Studies, Volume 31, Issue 7

检测可重复基金绩效

作者：Campbell R Harvey (Duke University), Yan Liu（Texas A&M University）

摘要：过去的基金业绩在预测未来收益方面做得不好。原因是噪声。使用一个随机效应框架，我们通过汇集来自横截面alpha分布的信息来对每个基金的alpha进行密度预测，从而减少噪声。仿真结果表明，由我们的方法生成的参数估计，在总体和单个基金级别上都优于其他方法。样本外的预测还表明，我们的方法生成了改进的alpha预测。

Detecting Repeatable Performance

Campbell R Harvey (Duke University)

Yan Liu（Texas A&M University）

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.

原文链接：https://academic.oup.com/rfs/article-abstract/31/7/2499/4841739?redirectedFrom=fulltext

翻译：黄涛