Bartram, Söhnke M., Branke, Jürgen, De Rossi, Giuliano and Motahari, Mehrshad (2021) Machine learning for active portfolio management. Journal of Financial Data Science, 3 (3). pp. 9-30. doi:10.3905/jfds.2021.1.071

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Abstract

Machine learning (ML) methods are attracting considerable attention among academics in the field of finance. However, it is commonly believed that ML has not transformed the asset management industry to the same extent as other sectors. This survey focuses on the ML methods and empirical results available in the literature that matter most for active portfolio management. ML has asset management applications for signal generation, portfolio construction, and trade execution, and promising findings have been reported. Reinforcement learning (RL), in particular, is expected to play a more significant role in the industry. Nevertheless, the performance of a sample of active exchange-traded funds (ETF) that use ML in their investments tends to be mixed. Overall, ML techniques show great promise for active portfolio management, but investors should be cautioned against their main potential pitfalls.

Item Type:	Journal Article
Subjects:	H Social Sciences > HG Finance Q Science > Q Science (General)
Divisions:	Faculty of Social Sciences > Warwick Business School > Finance Group Faculty of Social Sciences > Warwick Business School
Library of Congress Subject Headings (LCSH):	Portfolio management, Portfolio management -- Computer simulation , Machine learning , Exchange traded funds
Journal or Publication Title:	Journal of Financial Data Science
Publisher:	Portfolio Management Research
ISSN:	2640-3943
Official Date:	21 June 2021
Dates:	Date Event 21 June 2021 Published 2 August 2021 Available 12 July 2021 Modified 29 May 2021 Accepted
Volume:	3
Number:	3
Page Range:	pp. 9-30
DOI:	10.3905/jfds.2021.1.071
Status:	Peer Reviewed
Publication Status:	Published
Publisher Statement:	© 2021, Portfolio Management Research. This is the author's accepted manuscript of the work. It is posted here by permission of Portfolio Management Research. The Version of Record was published in 'Bartram, Söhnke M., Branke, Jürgen, De Rossi, Giuliano and Motahari, Mehrshad (2021) Machine learning for active portfolio management. Journal of Financial Data Science, 3 (3) 9-30 http://doi.org/10.3905/jfds.2021.1.071
Access rights to Published version:	Restricted or Subscription Access
Copyright Holders:	© 2021 Pageant Media Ltd
Related URLs:	Publisher

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