Empirical Asset Pricing via Machine Learning: The Role of Research Design Choices
研究了机器学习投资策略中八种研究设计选择（如训练窗口长度、数据过滤和组合构建方法）对策略收益的影响，发现设计选择导致收益显著变化，非标准误差高达标准误差的五倍，但约三分之一的组合在扣除交易成本后仍有显著收益。
ABSTRACT We explore the impact of research design choices on the profitability of machine learning (ML) investment strategies. Specifically, we consider eight choices, including training window length, data filters and portfolio construction methods, across seven ML models. Based on 5376 portfolios, we find that design choices cause significant variation in strategy returns. The nonstandard errors (NSEs) of ML strategies are up to five times the standard errors (SEs) and remain large even after controlling for high‐impact decisions such as eliminating micro‐caps and using value‐weighted portfolios. Nonetheless, ML still generates significant returns for about one‐third of the portfolios even after transaction costs.