MAPS: Multi-agent reinforcement learning-based portfolio management system

Jinho Lee, Raehyun Kim, Seok Won Yi, Jaewoo Kang

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    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 languageEnglish
    Title of host publicationProceedings of the 29th International Joint Conference on Artificial Intelligence, IJCAI 2020
    EditorsChristian Bessiere
    PublisherInternational Joint Conferences on Artificial Intelligence
    Pages4520-4526
    Number of pages7
    ISBN (Electronic)9780999241165
    Publication statusPublished - 2020
    Event29th International Joint Conference on Artificial Intelligence, IJCAI 2020 - Yokohama, Japan
    Duration: 2021 Jan 1 → …

    Publication series

    NameIJCAI International Joint Conference on Artificial Intelligence
    Volume2021-January
    ISSN (Print)1045-0823

    Conference

    Conference29th International Joint Conference on Artificial Intelligence, IJCAI 2020
    Country/TerritoryJapan
    CityYokohama
    Period21/1/1 → …

    ASJC Scopus subject areas

    • Artificial Intelligence

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