Cross-sectional learning and short-run persistence in mutual fund performance
Using monthly return data of more than 6400 US equity mutual funds we investigate short-run performance persistence over the period 1984–2003. We sort funds into rank portfolios based on past performance, and evaluate the portfolios’ out-of-sample performance. To cope with short ranking periods, we employ an empirical Bayes approach to measure past performance more efficiently. Our main finding is that when funds are sorted into decile portfolios based on 12-month ranking periods, the top decile of funds earns a statistically significant, abnormal return of 0.26 percent per month. This effect persists beyond load fees, and is mainly concentrated in relatively young, small cap/growth funds.
|Keywords||Bayesian analysis, mutual funds, performance persistence|
|JEL||Portfolio Choice; Investment Decisions (jel G11), Information and Market Efficiency; Event Studies (jel G14), General Financial Markets: Other (jel G19)|
|Persistent URL||dx.doi.org/10.1016/j.jbankfin.2006.08.002, hdl.handle.net/1765/12681|
|Series||ERIM Top-Core Articles|
|Journal||Journal of Banking & Finance|
Huij, J.J, & Verbeek, M.J.C.M. (2007). Cross-sectional learning and short-run persistence in mutual fund performance. Journal of Banking & Finance, 31(3), 973–997. doi:10.1016/j.jbankfin.2006.08.002