Detecting Repeatable Performance

• 成果类型: Article
• 署名作者: Harvey, Campbell R.; Liu, Yan
• 署名单位: Duke University; Texas A&M University System; Texas A&M University College Station
• 刊物名称: REVIEW OF FINANCIAL STUDIES
• ISSN/ISSBN: 0893-9454
• DOI: 10.1093/rfs/hhy014
• 发表日期: 2018
• 页码: 2499
• 关键词: MUTUAL FUND PERFORMANCE; maximum-likelihood; cross-section; Hedge funds; RISK; selection; mixture; ALPHAS; persistence; returns

摘要

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.