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Factor Representation and Decision Making in Stock Markets Using Deep Reinforcement Learning

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  • Zhaolu Dong
  • Shan Huang
  • Simiao Ma
  • Yining Qian

Abstract

Deep Reinforcement learning is a branch of unsupervised learning in which an agent learns to act based on environment state in order to maximize its total reward. Deep reinforcement learning provides good opportunity to model the complexity of portfolio choice in high-dimensional and data-driven environment by leveraging the powerful representation of deep neural networks. In this paper, we build a portfolio management system using direct deep reinforcement learning to make optimal portfolio choice periodically among S\&P500 underlying stocks by learning a good factor representation (as input). The result shows that an effective learning of market conditions and optimal portfolio allocations can significantly outperform the average market.

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  • Zhaolu Dong & Shan Huang & Simiao Ma & Yining Qian, 2021. "Factor Representation and Decision Making in Stock Markets Using Deep Reinforcement Learning," Papers 2108.01758, arXiv.org.
    • Handle: RePEc:arx:papers:2108.01758
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    References listed on IDEAS

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    1. Zhengyao Jiang & Dixing Xu & Jinjun Liang, 2017. "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem," Papers 1706.10059, arXiv.org, revised Jul 2017.
    2. Zhipeng Liang & Hao Chen & Junhao Zhu & Kangkang Jiang & Yanran Li, 2018. "Adversarial Deep Reinforcement Learning in Portfolio Management," Papers 1808.09940, arXiv.org, revised Nov 2018.
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