Detecting repeatable performance


Journal Article

© The Author 2018. 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-ofsample forecasting exercise also shows that our method generates improved alpha forecasts.

Full Text

Cited Authors

  • Harvey, CR; Liu, Y

Published Date

  • July 1, 2018

Published In

Volume / Issue

  • 31 / 7

Start / End Page

  • 2499 - 2552

Electronic International Standard Serial Number (EISSN)

  • 1465-7368

International Standard Serial Number (ISSN)

  • 0893-9454

Digital Object Identifier (DOI)

  • 10.1093/rfs/hhy014

Citation Source

  • Scopus