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Machine Learning for Active Portfolio Management

Bartram, S. M., Branke, J., Rossi, G. D. and Motahari, M. ORCID: 0000-0003-3245-8545 (2021). Machine Learning for Active Portfolio Management. The Journal of Financial Data Science, 3(3), pp. 9-30. doi: 10.3905/jfds.2021.1.071

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.

Publication Type: Article
Additional Information: © 2021 Portfolio Management Research. All rights reserved.
Publisher Keywords: Big data/machine learning, portfolio construction, exchange-traded funds and applications, performance measurement
Subjects: H Social Sciences > HG Finance
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: Bayes Business School > Finance
Date available in CRO: 11 Nov 2021 11:16
Date deposited: 11 November 2021
Date of first online publication: 12 July 2021
URI: https://openaccess.city.ac.uk/id/eprint/27086
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