Machine-learning the skill of mutual fund managers

• 成果类型: Article
• 署名作者: Kaniel, Ron; Lin, Zihan; Pelger, Markus; Van Nieuwerburgh, Stijn
• 署名单位: University of Rochester; Reichman University; Stanford University; Stanford University; Columbia University
• 刊物名称: JOURNAL OF FINANCIAL ECONOMICS
• ISSN/ISSBN: 0304-405X
• DOI: 10.1016/j.jfineco.2023.07.004
• 发表日期: 2023
• 页码: 94-138
• 关键词: MUTUAL FUND PERFORMANCE; Fund flow; momentum; Machine Learning; Sentiment; big data; Neural Networks

摘要

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.(c) 2023 Elsevier B.V. All rights reserved.