Zihan Lin (ICME): Machine-Learning the Skill of Mutual Fund Managers

Zihan Lin will present the paper “Machine-Learning the Skill of Mutual Fund Managers”. We will provide dinners for the attendees and send out dining options on Monday. Please see more details below:

Title: Machine-Learning the Skill of Mutual Fund Managers (link here)

Authors: Ron Kaniel, Zihan Lin, Markus Pelger, and 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.

Bio: Zihan is a sixth year Ph.D. student at Stanford ICME advised by Markus Pelger. His research interests are in institutional finance, machine learning, and asset pricing.

His personal website is here.