Machine-learning the skill of mutual fund managers
利用机器学习发现基金特征能持续区分高、低绩效共同基金，基金动量和资金流是未来风险调整后绩效的最重要预测因子，而持股特征无预测力。
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.