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Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States

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  • Yunan Ye
  • Hengzhi Pei
  • Boxin Wang
  • Pin-Yu Chen
  • Yada Zhu
  • Jun Xiao
  • Bo Li

Abstract

Portfolio management (PM) is a fundamental financial planning task that aims to achieve investment goals such as maximal profits or minimal risks. Its decision process involves continuous derivation of valuable information from various data sources and sequential decision optimization, which is a prospective research direction for reinforcement learning (RL). In this paper, we propose SARL, a novel State-Augmented RL framework for PM. Our framework aims to address two unique challenges in financial PM: (1) data heterogeneity -- the collected information for each asset is usually diverse, noisy and imbalanced (e.g., news articles); and (2) environment uncertainty -- the financial market is versatile and non-stationary. To incorporate heterogeneous data and enhance robustness against environment uncertainty, our SARL augments the asset information with their price movement prediction as additional states, where the prediction can be solely based on financial data (e.g., asset prices) or derived from alternative sources such as news. Experiments on two real-world datasets, (i) Bitcoin market and (ii) HighTech stock market with 7-year Reuters news articles, validate the effectiveness of SARL over existing PM approaches, both in terms of accumulated profits and risk-adjusted profits. Moreover, extensive simulations are conducted to demonstrate the importance of our proposed state augmentation, providing new insights and boosting performance significantly over standard RL-based PM method and other baselines.

Suggested Citation

  • Yunan Ye & Hengzhi Pei & Boxin Wang & Pin-Yu Chen & Yada Zhu & Jun Xiao & Bo Li, 2020. "Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States," Papers 2002.05780, arXiv.org.
    • Handle: RePEc:arx:papers:2002.05780
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Eric Benhamou & David Saltiel & Serge Tabachnik & Sui Kai Wong & François Chareyron, 2021. "Distinguish the indistinguishable: a Deep Reinforcement Learning approach for volatility targeting models," Working Papers hal-03202431, HAL.
    2. Zhenhan Huang & Fumihide Tanaka, 2021. "MSPM: A Modularized and Scalable Multi-Agent Reinforcement Learning-based System for Financial Portfolio Management," Papers 2102.03502, arXiv.org, revised Feb 2022.
    3. Francisco Caio Lima Paiva & Leonardo Kanashiro Felizardo & Reinaldo Augusto da Costa Bianchi & Anna Helena Reali Costa, 2021. "Intelligent Trading Systems: A Sentiment-Aware Reinforcement Learning Approach," Papers 2112.02095, arXiv.org.
    4. Woosung Koh & Insu Choi & Yuntae Jang & Gimin Kang & Woo Chang Kim, 2023. "Curriculum Learning and Imitation Learning for Model-free Control on Financial Time-series," Papers 2311.13326, arXiv.org, revised Jan 2024.
    5. Eric Benhamou & David Saltiel & Sandrine Ungari & Abhishek Mukhopadhyay & Jamal Atif, 2020. "AAMDRL: Augmented Asset Management with Deep Reinforcement Learning," Papers 2010.08497, arXiv.org.
    6. Chung I Lu, 2023. "Evaluation of Deep Reinforcement Learning Algorithms for Portfolio Optimisation," Papers 2307.07694, arXiv.org, revised Jul 2023.
    7. Frensi Zejnullahu & Maurice Moser & Joerg Osterrieder, 2022. "Applications of Reinforcement Learning in Finance -- Trading with a Double Deep Q-Network," Papers 2206.14267, arXiv.org.
    8. Shuo Sun & Molei Qin & Xinrun Wang & Bo An, 2023. "PRUDEX-Compass: Towards Systematic Evaluation of Reinforcement Learning in Financial Markets," Papers 2302.00586, arXiv.org, revised Mar 2023.
    9. Shuyang Wang & Diego Klabjan, 2023. "An Ensemble Method of Deep Reinforcement Learning for Automated Cryptocurrency Trading," Papers 2309.00626, arXiv.org.
    10. Kumar Yashaswi, 2021. "Deep Reinforcement Learning for Portfolio Optimization using Latent Feature State Space (LFSS) Module," Papers 2102.06233, arXiv.org.
    11. Yuchen Fang & Kan Ren & Weiqing Liu & Dong Zhou & Weinan Zhang & Jiang Bian & Yong Yu & Tie-Yan Liu, 2021. "Universal Trading for Order Execution with Oracle Policy Distillation," Papers 2103.10860, arXiv.org.
    12. Wentao Zhang & Lingxuan Zhao & Haochong Xia & Shuo Sun & Jiaze Sun & Molei Qin & Xinyi Li & Yuqing Zhao & Yilei Zhao & Xinyu Cai & Longtao Zheng & Xinrun Wang & Bo An, 2024. "A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist," Papers 2402.18485, arXiv.org, revised Feb 2024.
    13. Alexandre Carbonneau & Frédéric Godin, 2023. "Deep Equal Risk Pricing of Financial Derivatives with Non-Translation Invariant Risk Measures," Risks, MDPI, vol. 11(8), pages 1-27, August.
    14. Hui Niu & Siyuan Li & Jian Li, 2022. "MetaTrader: An Reinforcement Learning Approach Integrating Diverse Policies for Portfolio Optimization," Papers 2210.01774, arXiv.org.
    15. Shuo Sun & Rundong Wang & Bo An, 2021. "Reinforcement Learning for Quantitative Trading," Papers 2109.13851, arXiv.org.
    16. Eric Benhamou & David Saltiel & Serge Tabachnik & Sui Kai Wong & Franc{c}ois Chareyron, 2021. "Adaptive learning for financial markets mixing model-based and model-free RL for volatility targeting," Papers 2104.10483, arXiv.org, revised Apr 2021.
    17. Eric Benhamou & David Saltiel & Sandrine Ungari & Abhishek Mukhopadhyay, 2020. "Bridging the gap between Markowitz planning and deep reinforcement learning," Papers 2010.09108, arXiv.org.
    18. Eric Benhamou & David Saltiel & Sandrine Ungari & Abhishek Mukhopadhyay, 2020. "Time your hedge with Deep Reinforcement Learning," Papers 2009.14136, arXiv.org, revised Nov 2020.
    19. Ricard Durall, 2022. "Asset Allocation: From Markowitz to Deep Reinforcement Learning," Papers 2208.07158, arXiv.org.

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