Machine-Learning the Skill of Mutual Fund Managers

Abstract

We show, using machine learning, that fund characteristics can consistently differentiate high from low-performing mutual funds, before and after fees. The outperformance persists for more than three years. 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. Our estimation with neural networks enables us to uncover novel and substantial interaction effects between sentiment and both fund flow and fund momentum.

Keywords: Mutual fund performance, machine learning, sentiment, big data, neural networks

JEL Classification: G11, G12, G17, G23, C45