Asset Pricing and Machine Learning: A critical review
批判性回顾了机器学习方法在资产定价中应对因子动物园问题的最新文献，按正则化、降维、随机森林、神经网络等五类方法总结实证发现，并关注经济含义。
Abstract The latest development in empirical Asset Pricing is the use of Machine Learning methods to address the problem of the factor zoo. These techniques offer great flexibility and prediction accuracy but require special care as they strongly depart from traditional Econometrics. We review and critically assess the most recent and relevant contributions in the literature grouping them into five categories defined by the Machine Learning (ML) approach they employ: regularization, dimension reduction, regression trees/random forest (RF), neural networks (NNs), and comparative analyses. We summarize the empirical findings with particular attention to their economic interpretation providing hints for future developments.