Machine-Learning the Skill of Mutual Fund Managers

Ron Kaniel, Zihan Lin, Markus Pelger and Stijn Van Nieuwerburgh

No 29723, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: 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.

JEL-codes: G0 G11 G23 G5 (search for similar items in EconPapers)
Date: 2022-02
New Economics Papers: this item is included in nep-big, nep-cmp, nep-cwa and nep-fmk
Note: AP
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Published as Ron Kaniel & Zihan Lin & Markus Pelger & Stijn Van Nieuwerburgh, 2023. "Machine-learning the skill of mutual fund managers," Journal of Financial Economics, vol 150(1), pages 94-138.

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Related works:
Journal Article: Machine-learning the skill of mutual fund managers (2023)
Working Paper: Machine-Learning the Skill of Mutual Fund Managers (2023)
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