RT Journal Article
SR Electronic
T1 How Can Machine Learning Advance Quantitative Asset Management?
JF The Journal of Portfolio Management
FD Institutional Investor Journals
SP jpm.2023.1.460
DO 10.3905/jpm.2023.1.460
A1 David Blitz
A1 Tobias Hoogteijling
A1 Harald Lohre
A1 Philip Messow
YR 2023
UL https://pm-research.com/content/early/2023/01/11/jpm.2023.1.460.abstract
AB 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.