Abstract
Generating an investment strategy using advanced deep learning methods in stock markets has recently been a topic of interest. Most existing deep learning methods focus on proposing an optimal model or network architecture by maximizing return. However, these models often fail to consider and adapt to the continuously changing market conditions. In this paper, we propose the Multi-Agent reinforcement learning-based Portfolio management System (MAPS). MAPS is a cooperative system in which each agent is an independent”investor” creating its own portfolio. In the training procedure, each agent is guided to act as diversely as possible while maximizing its own return with a carefully designed loss function. As a result, MAPS as a system ends up with a diversified portfolio. Experiment results with 12 years of US market data show that MAPS outperforms most of the baselines in terms of Sharpe ratio. Furthermore, our results show that adding more agents to our system would allow us to get a higher Sharpe ratio by lowering risk with a more diversified portfolio.
Original language | English |
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Title of host publication | Proceedings of the 29th International Joint Conference on Artificial Intelligence, IJCAI 2020 |
Editors | Christian Bessiere |
Publisher | International Joint Conferences on Artificial Intelligence |
Pages | 4520-4526 |
Number of pages | 7 |
ISBN (Electronic) | 9780999241165 |
Publication status | Published - 2020 |
Event | 29th International Joint Conference on Artificial Intelligence, IJCAI 2020 - Yokohama, Japan Duration: 2021 Jan 1 → … |
Publication series
Name | IJCAI International Joint Conference on Artificial Intelligence |
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Volume | 2021-January |
ISSN (Print) | 1045-0823 |
Conference
Conference | 29th International Joint Conference on Artificial Intelligence, IJCAI 2020 |
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Country/Territory | Japan |
City | Yokohama |
Period | 21/1/1 → … |
Bibliographical note
Funding Information:This work was supported by the National Research Foundation of Korea (NRF-2017R1A2A1A17069645, NRF-2017M3C4A7065887).
Publisher Copyright:
© 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.
ASJC Scopus subject areas
- Artificial Intelligence