数字经济前沿文献讨论会第1期：Empirical Asset Pricing via Machine Learning
892 时间：2020年6月25日（周四）晚上6:30
形式：钉钉群视频会议
题目：Empirical Asset Pricing via Machine Learning
期刊：The Review of Financial Studies
作者：Shihao Gu，Bryan Kelly，Dacheng Xiu
摘要：We perform a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premiums.We demonstrate large economic gains to investors using machine learning forecasts, in some cases doubling the performance of leading regression-based strategies from the literature. We identify the best-performing methods (trees and neural networks) and trace their predictive gains to allowing nonlinear predictor interactions missed by other methods. All methods agree on the same set of dominant predictive signals, a set that includes variations on momentum, liquidity, and volatility.
主讲人：方朴一 博士生
