摘要
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.