Mutual Funds’ Conditional Performance Free of Data Snooping Bias

Po-Hsuan Hsu, Ioannis Kyriakou, Tren Ma, and Georgios Sermpinis

♦ 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.

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