How can machine learning advance quantitative asset management

Blitz, David and Hoogteijling, Tobias and Lohre, Harald and Messow, Philip (2023) How can machine learning advance quantitative asset management. Journal of Portfolio Management, 49 (7). ISSN 0095-4918

Text (BlitzHoogteijlingLohreMessow2023_SSRN)
BlitzHoogteijlingLohreMessow2023_SSRN.pdf - Accepted Version
Restricted to Repository staff only until 11 January 2024.
Available under License Creative Commons Attribution.

Download (478kB)


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.

Item Type:
Journal Article
Journal or Publication Title:
Journal of Portfolio Management
Uncontrolled Keywords:
ID Code:
Deposited By:
Deposited On:
20 Jul 2023 13:40
Last Modified:
20 Sep 2023 02:01