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Detecting Repeatable Performance
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DOI：10.1093/rfs/hhy014.png)
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Abstract
En 中文
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:
MUTUAL FUND PERFORMANCE
MAXIMUM-LIKELIHOOD
CROSS-SECTION
HEDGE FUNDS
RISK
SELECTION
MIXTURE
ALPHAS
PERSISTENCE
RETURNS
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