Working Paper: NBER ID: w29723
Authors: Ron Kaniel; Zihan Lin; Markus Pelger; Stijn van Nieuwerburgh
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.
Keywords: mutual funds; machine learning; fund performance; investment strategy
JEL Codes: G00; G11; G23; G5
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| fund flow (E50) | mutual fund performance (G23) |
| fund momentum (G31) | mutual fund performance (G23) |
| investor sentiment (G41) | mutual fund performance (G23) |
| fund flow and fund momentum (E50) | mutual fund performance (G23) |