Mutual Funds’ Conditional Performance Free of Data Snooping Bias

Abstract

We introduce a test to assess mutual funds’ “conditional” performance that is based on updated information and corrects data snooping bias. Our method, named the functional False Discovery Rate “plus” (fFDR+), incorporates fund characteristics in estimating fund performance free of data snooping bias. Simulations suggest that the fFDR+ controls well the ratio of false discoveries and gains considerable power over prior methods that do not account for extra information. Portfolios of funds selected by the fFDR+ outperform other tests not accounting for information updating, highlighting the importance of evaluating mutual funds from a conditional perspective.

Keywords: Multiple testing, Functional false discovery rate, Informative covariates, Mutual funds, Alphas

JEL Classification: C11, C12, G23