Mutual Funds’ Conditional Performance Free of Data Snooping Bias

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

Journal of Financial and Quantitative Analysis, 2025, vol. 60, issue 3, 1373-1400

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” ( $ {\mathrm{fFDR}}^{+} $ ), incorporates fund characteristics in estimating fund performance free of data snooping bias. Simulations suggest that the $ {\mathrm{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 $ {\mathrm{fFDR}}^{+} $ outperform other tests not accounting for information updating, highlighting the importance of evaluating mutual funds from a conditional perspective.

Date: 2025
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