Machine-Learning the Skill of Mutual Fund Managers
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.
- In Machine-Learning the Skill of Mutual Fund Managers (NBER Working Paper 29723) Ron Kaniel, Zihan Lin, Markus Pelger, and Stijn Van...
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. citation courtesy of