We show, using machine learning, that fund characteristics can consistently differentiate high from low-performing mutual funds, as well as identify funds with net-of-fees abnormal returns. Fund momentum and fund flow are the most important predictors of future risk-adjusted fund performance, while characteristics of the stocks that funds hold are not predictive. Returns of predictive long-short portfolios are higher following a period of high sentiment or a good state of the macro-economy. Our estimation with neural networks enables us to uncover novel and substantial interaction effects between sentiment and both fund flow and fund momentum.

R. Kaniel, M. Z. Lin, M. Pelger, and Stijn Van Nieuwerburgh
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Kaniel, R., M. Z. Lin, M. Pelger, and Stijn Van Nieuwerburgh
. Machine-Learning the Skill of Mutual Fund Managers. February 01, 2022.