IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2022i1p19-d1009624.html
   My bibliography  Save this article

A Portfolio Model with Risk Control Policy Based on Deep Reinforcement Learning

Author

Listed:
  • Caiyu Jiang

    (School of Science, Wuhan University of Technology, Wuhan 430070, China)

  • Jianhua Wang

    (School of Science, Wuhan University of Technology, Wuhan 430070, China)

Abstract

It was shown that deep reinforcement learning (DRL) has the potential to solve portfolio management problems in recent years. The Twin Delayed Deep Deterministic policy gradient algorithm (TD3) is an actor-critic method, a typical DRL method in continuous action space. Despite the success of DRL in financial trading, surprisingly, most of the literature ignores the element of risk control. The research is proposed to combine long- and short-term risk (LSTR) control with the TD3 algorithm to build a portfolio model with risk management capabilities. Using Chinese stock data from the Shanghai Stock Exchange, we train and validate the proposed portfolio model. Performances were compared to the TD3 model without risk control. The results indicated that our proposal offers better risk control and investment returns.

Suggested Citation

  • Caiyu Jiang & Jianhua Wang, 2022. "A Portfolio Model with Risk Control Policy Based on Deep Reinforcement Learning," Mathematics, MDPI, vol. 11(1), pages 1-16, December.
    • Handle: RePEc:gam:jmathe:v:11:y:2022:i:1:p:19-:d:1009624
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/1/19/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/1/19/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Huang, Xiaoxia, 2008. "Portfolio selection with a new definition of risk," European Journal of Operational Research, Elsevier, vol. 186(1), pages 351-357, April.
    2. David P. Helmbold & Robert E. Schapire & Yoram Singer & Manfred K. Warmuth, 1998. "On‐Line Portfolio Selection Using Multiplicative Updates," Mathematical Finance, Wiley Blackwell, vol. 8(4), pages 325-347, October.
    3. Esther Mohr & Robert Dochow, 2017. "Risk management strategies for finding universal portfolios," Annals of Operations Research, Springer, vol. 256(1), pages 129-147, September.
    4. Francesco Bertoluzzo & Marco Corazza, 2012. "Reinforcement Learning for automatic financial trading: Introduction and some applications," Working Papers 2012:33, Department of Economics, University of Venice "Ca' Foscari", revised 2012.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yuling Huang & Kai Cui & Yunlin Song & Zongren Chen, 2023. "A Multi-Scaling Reinforcement Learning Trading System Based on Multi-Scaling Convolutional Neural Networks," Mathematics, MDPI, vol. 11(11), pages 1-19, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Roujia Li & Jia Liu, 2022. "Online Portfolio Selection with Long-Short Term Forecasting," SN Operations Research Forum, Springer, vol. 3(4), pages 1-15, December.
    2. Esther Mohr & Robert Dochow, 2017. "Risk management strategies for finding universal portfolios," Annals of Operations Research, Springer, vol. 256(1), pages 129-147, September.
    3. Jin’an He & Shicheng Yin & Fangping Peng, 2024. "Weak aggregating specialist algorithm for online portfolio selection," Computational Economics, Springer;Society for Computational Economics, vol. 63(6), pages 2405-2434, June.
    4. Seung-Hyun Moon & Yong-Hyuk Kim & Byung-Ro Moon, 2019. "Empirical investigation of state-of-the-art mean reversion strategies for equity markets," Papers 1909.04327, arXiv.org.
    5. Man Yiu Tsang & Tony Sit & Hoi Ying Wong, 2022. "Adaptive Robust Online Portfolio Selection," Papers 2206.01064, arXiv.org.
    6. 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.
    7. R'emi J'ez'equel & Dmitrii M. Ostrovskii & Pierre Gaillard, 2022. "Efficient and Near-Optimal Online Portfolio Selection," Papers 2209.13932, arXiv.org.
    8. James Chok & Geoffrey M. Vasil, 2023. "Convex optimization over a probability simplex," Papers 2305.09046, arXiv.org.
    9. Eyal Even-Dar & Sham. M. Kakade & Yishay Mansour, 2009. "Online Markov Decision Processes," Mathematics of Operations Research, INFORMS, vol. 34(3), pages 726-736, August.
    10. Mohamed El Hedi Arouri & Christophe Rault & Ana Maria Sova & Robert Sova & Frédéric Teulon, 2013. "Market Structure and the Cost of Capital," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00798048, HAL.
    11. Lord Mensah, 2016. "Asset Allocation Brewed Accross African Stock Markets," Proceedings of Economics and Finance Conferences 3205757, International Institute of Social and Economic Sciences.
    12. Theodoros Tsagaris & Ajay Jasra & Niall Adams, 2012. "Robust and adaptive algorithms for online portfolio selection," Quantitative Finance, Taylor & Francis Journals, vol. 12(11), pages 1651-1662, November.
    13. Smimou, K., 2014. "International portfolio choice and political instability risk: A multi-objective approach," European Journal of Operational Research, Elsevier, vol. 234(2), pages 546-560.
    14. Bin Li & Steven C. H. Hoi, 2012. "On-Line Portfolio Selection with Moving Average Reversion," Papers 1206.4626, arXiv.org.
    15. Ha, Youngmin & Zhang, Hai, 2020. "Algorithmic trading for online portfolio selection under limited market liquidity," European Journal of Operational Research, Elsevier, vol. 286(3), pages 1033-1051.
    16. Seung-Hyun Moon & Yourim Yoon, 2022. "Genetic Mean Reversion Strategy for Online Portfolio Selection with Transaction Costs," Mathematics, MDPI, vol. 10(7), pages 1-20, March.
    17. Ting-Kam Leonard Wong, 2015. "Universal portfolios in stochastic portfolio theory," Papers 1510.02808, arXiv.org, revised Dec 2016.
    18. Yong Zhang & Xingyu Yang, 2017. "Online Portfolio Selection Strategy Based on Combining Experts’ Advice," Computational Economics, Springer;Society for Computational Economics, vol. 50(1), pages 141-159, June.
    19. Zihao Zhang & Stefan Zohren & Stephen Roberts, 2019. "Deep Reinforcement Learning for Trading," Papers 1911.10107, arXiv.org.
    20. Sévi, Benoît, 2014. "Forecasting the volatility of crude oil futures using intraday data," European Journal of Operational Research, Elsevier, vol. 235(3), pages 643-659.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2022:i:1:p:19-:d:1009624. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.