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Deep Reinforcement Learning for Asset Allocation: Reward Clipping

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Listed:
  • Jiwon Kim
  • Moon-Ju Kang
  • KangHun Lee
  • HyungJun Moon
  • Bo-Kwan Jeon

Abstract

Recently, there are many trials to apply reinforcement learning in asset allocation for earning more stable profits. In this paper, we compare performance between several reinforcement learning algorithms - actor-only, actor-critic and PPO models. Furthermore, we analyze each models' character and then introduce the advanced algorithm, so called Reward clipping model. It seems that the Reward Clipping model is better than other existing models in finance domain, especially portfolio optimization - it has strength both in bull and bear markets. Finally, we compare the performance for these models with traditional investment strategies during decreasing and increasing markets.

Suggested Citation

  • Jiwon Kim & Moon-Ju Kang & KangHun Lee & HyungJun Moon & Bo-Kwan Jeon, 2023. "Deep Reinforcement Learning for Asset Allocation: Reward Clipping," Papers 2301.05300, arXiv.org.
    • Handle: RePEc:arx:papers:2301.05300
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    References listed on IDEAS

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    1. Amine Mohamed Aboussalah & Ziyun Xu & Chi-Guhn Lee, 2022. "What is the value of the cross-sectional approach to deep reinforcement learning?," Quantitative Finance, Taylor & Francis Journals, vol. 22(6), pages 1091-1111, June.
    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.
    3. Liyan Yang, 2019. "Loss Aversion in Financial Markets," The Journal of Mechanism and Institution Design, Society for the Promotion of Mechanism and Institution Design, University of York, vol. 4(1), pages 119-137, November.
    4. Fischer, Thomas G., 2018. "Reinforcement learning in financial markets - a survey," FAU Discussion Papers in Economics 12/2018, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    5. Ricard Durall, 2022. "Asset Allocation: From Markowitz to Deep Reinforcement Learning," Papers 2208.07158, arXiv.org.
    6. Miquel Noguer i Alonso & Sonam Srivastava, 2020. "Deep Reinforcement Learning for Asset Allocation in US Equities," Papers 2010.04404, arXiv.org.
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