资本资产定价模型
计算机科学
机器学习
人工神经网络
人工智能
波动性(金融)
市场流动性
计量经济学
资产(计算机安全)
跟踪(心理语言学)
集合(抽象数据类型)
经济
财务
语言学
哲学
计算机安全
程序设计语言
作者
Shihao Gu,Bryan Kelly,Dacheng Xiu
摘要
Abstract 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. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.
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