How Can Machine Learning Advance Quantitative Asset Management?
从审慎从业者视角回顾机器学习在资产管理中的应用文献，重点讨论影响预测结果的方法设计选择，并评估ML是否真能带来显著绩效提升。
The emerging literature suggests that machine learning (ML) is beneficial in many asset pricing applications because of its ability to detect and exploit nonlinearities and interaction effects that tend to go unnoticed with simpler modelling approaches. In this article, the authors discuss the promises and pitfalls of applying machine learning to asset management by reviewing the existing ML literature from the perspective of a prudent practitioner. The focus is on the methodological design choices that can critically affect predictive outcomes and on an evaluation of the frequent claim that ML gives spectacular performance improvements. In light of the practical considerations, the apparent advantage of ML is reduced, but still likely to make a difference for investors who adhere to a sound research protocol to navigate the intrinsic pitfalls of ML.