IDEAS home Printed from https://ideas.repec.org/a/eee/glofin/v62y2024ics1044028324000887.html
   My bibliography  Save this article

Deep reinforcement learning for portfolio selection

Author

Listed:
  • Jiang, Yifu
  • Olmo, Jose
  • Atwi, Majed

Abstract

This study proposes an advanced model-free deep reinforcement learning (DRL) framework to construct optimal portfolio strategies in dynamic, complex, and large-dimensional financial markets. Investors' risk aversion and transaction cost constraints are embedded in an extended Markowitz's mean-variance reward function by employing a twin-delayed deep deterministic policy gradient (TD3) algorithm. This study designs a DRL-TD3-based risk and transaction cost-sensitive portfolio that combines advanced exploration strategies and dynamic policy updates. The proposed portfolio method effectively addresses the challenges posed by high-dimensional state and action spaces in complex financial markets. This methodology provides two optimal portfolios by flexibly controlling transaction and risk costs with (i) the constituents of the Dow Jones Industrial Average and (ii) the constituents of the S&P100 index. Results demonstrate a strong portfolio performance of the proposed DRL portfolio compared to those of several competitors from the traditional and DRL literatures.

Suggested Citation

  • Jiang, Yifu & Olmo, Jose & Atwi, Majed, 2024. "Deep reinforcement learning for portfolio selection," Global Finance Journal, Elsevier, vol. 62(C).
  • Handle: RePEc:eee:glofin:v:62:y:2024:i:c:s1044028324000887
    DOI: 10.1016/j.gfj.2024.101016
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1044028324000887
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.gfj.2024.101016?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yuze Li & Shangrong Jiang & Yunjie Wei & Shouyang Wang, 2021. "Take Bitcoin into your portfolio: a novel ensemble portfolio optimization framework for broad commodity assets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-26, December.
    2. Choi, Jin Hyuk & Larsen, Kasper & Seppi, Duane J., 2019. "Information and trading targets in a dynamic market equilibrium," Journal of Financial Economics, Elsevier, vol. 132(3), pages 22-49.
    3. Kircher, Felix & Rösch, Daniel, 2021. "A shrinkage approach for Sharpe ratio optimal portfolios with estimation risks," Journal of Banking & Finance, Elsevier, vol. 133(C).
    4. Henriques, Irene & Sadorsky, Perry, 2023. "Forecasting NFT coin prices using machine learning: Insights into feature significance and portfolio strategies," Global Finance Journal, Elsevier, vol. 58(C).
    5. Robert G. Chambers & John Quiggin, 2007. "Dual Approaches to the Analysis of Risk Aversion," Economica, London School of Economics and Political Science, vol. 74(294), pages 189-213, May.
    6. 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.
    7. Zechu Li & Xiao-Yang Liu & Jiahao Zheng & Zhaoran Wang & Anwar Walid & Jian Guo, 2021. "FinRL-Podracer: High Performance and Scalable Deep Reinforcement Learning for Quantitative Finance," Papers 2111.05188, arXiv.org.
    8. Rubesam, Alexandre, 2022. "Machine learning portfolios with equal risk contributions: Evidence from the Brazilian market," Emerging Markets Review, Elsevier, vol. 51(PB).
    9. Zhang, Wei-Guo & Zhang, Xili & Chen, Yunxia, 2011. "Portfolio adjusting optimization with added assets and transaction costs based on credibility measures," Insurance: Mathematics and Economics, Elsevier, vol. 49(3), pages 353-360.
    10. 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.
    11. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    12. Goodell, John W. & Kumar, Satish & Lim, Weng Marc & Pattnaik, Debidutta, 2021. "Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
    13. Lucio Fernandez-Arjona & Damir Filipovi'c, 2020. "A machine learning approach to portfolio pricing and risk management for high-dimensional problems," Papers 2004.14149, arXiv.org, revised May 2022.
    14. Zhenhan Huang & Fumihide Tanaka, 2022. "MSPM: A modularized and scalable multi-agent reinforcement learning-based system for financial portfolio management," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-24, February.
    15. Seyoung Park & Hyunson Song & Sungchul Lee, 2019. "Linear programing models for portfolio optimization using a benchmark," The European Journal of Finance, Taylor & Francis Journals, vol. 25(5), pages 435-457, March.
    16. Ma, Guiyuan & Siu, Chi Chung & Zhu, Song-Ping, 2019. "Dynamic portfolio choice with return predictability and transaction costs," European Journal of Operational Research, Elsevier, vol. 278(3), pages 976-988.
    17. Haoran Wang & Xun Yu Zhou, 2020. "Continuous‐time mean–variance portfolio selection: A reinforcement learning framework," Mathematical Finance, Wiley Blackwell, vol. 30(4), pages 1273-1308, October.
    18. Lucio Fernandez‐Arjona & Damir Filipović, 2022. "A machine learning approach to portfolio pricing and risk management for high‐dimensional problems," Mathematical Finance, Wiley Blackwell, vol. 32(4), pages 982-1019, October.
    19. Bin Li & Jialei Wang & Dingjiang Huang & Steven C. H. Hoi, 2018. "Transaction cost optimization for online portfolio selection," Quantitative Finance, Taylor & Francis Journals, vol. 18(8), pages 1411-1424, August.
    20. Igor Halperin, 2019. "The QLBS Q-Learner goes NuQLear: fitted Q iteration, inverse RL, and option portfolios," Quantitative Finance, Taylor & Francis Journals, vol. 19(9), pages 1543-1553, September.
    21. Ngo, Vu Minh & Nguyen, Huan Huu & Van Nguyen, Phuc, 2023. "Does reinforcement learning outperform deep learning and traditional portfolio optimization models in frontier and developed financial markets?," Research in International Business and Finance, Elsevier, vol. 65(C).
    22. Qureshi, Fiza & Kutan, Ali M. & Ismail, Izlin & Gee, Chan Sok, 2017. "Mutual funds and stock market volatility: An empirical analysis of Asian emerging markets," Emerging Markets Review, Elsevier, vol. 31(C), pages 176-192.
    23. Mavruk, Taylan, 2022. "Analysis of herding behavior in individual investor portfolios using machine learning algorithms," Research in International Business and Finance, Elsevier, vol. 62(C).
    24. Jin Zhang & Dietmar Maringer, 2016. "Using a Genetic Algorithm to Improve Recurrent Reinforcement Learning for Equity Trading," Computational Economics, Springer;Society for Computational Economics, vol. 47(4), pages 551-567, April.
    25. Ayman Chaouki & Stephen Hardiman & Christian Schmidt & Emmanuel S'eri'e & Joachim de Lataillade, 2020. "Deep Deterministic Portfolio Optimization," Papers 2003.06497, arXiv.org, revised Apr 2020.
    26. Cui, Tianxiang & Ding, Shusheng & Jin, Huan & Zhang, Yongmin, 2023. "Portfolio constructions in cryptocurrency market: A CVaR-based deep reinforcement learning approach," Economic Modelling, Elsevier, vol. 119(C).
    27. Fereydooni, Ali & Mahootchi, Masoud, 2023. "An algorithmic trading system based on a stacked generalization model and hidden Markov model in the foreign exchange market," Global Finance Journal, Elsevier, vol. 56(C).
    Full references (including those not matched with items on IDEAS)

    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. Gupta, Pankaj & Mittal, Garima & Mehlawat, Mukesh Kumar, 2013. "Expected value multiobjective portfolio rebalancing model with fuzzy parameters," Insurance: Mathematics and Economics, Elsevier, vol. 52(2), pages 190-203.
    2. Fereydooni, Ali & Barak, Sasan & Asaad Sajadi, Seyed Mehrzad, 2024. "A novel online portfolio selection approach based on pattern matching and ESG factors," Omega, Elsevier, vol. 123(C).
    3. Huang, Xiaoxia & Ying, Haiyao, 2013. "Risk index based models for portfolio adjusting problem with returns subject to experts' evaluations," Economic Modelling, Elsevier, vol. 30(C), pages 61-66.
    4. Yao, Haixiang & Li, Danping & Wu, Huiling, 2022. "Dynamic trading with uncertain exit time and transaction costs in a general Markov market," International Review of Financial Analysis, Elsevier, vol. 84(C).
    5. Brini, Alessio & Tantari, Daniele, 2023. "Deep reinforcement trading with predictable returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 622(C).
    6. Guo, Sini & Yu, Lean & Li, Xiang & Kar, Samarjit, 2016. "Fuzzy multi-period portfolio selection with different investment horizons," European Journal of Operational Research, Elsevier, vol. 254(3), pages 1026-1035.
    7. Yuji Shinozaki, 2024. "A Review of New Developments in Finance with Deep Learning: Deep Hedging and Deep Calibration," IMES Discussion Paper Series 24-E-02, Institute for Monetary and Economic Studies, Bank of Japan.
    8. Woodside-Oriakhi, M. & Lucas, C. & Beasley, J.E., 2013. "Portfolio rebalancing with an investment horizon and transaction costs," Omega, Elsevier, vol. 41(2), pages 406-420.
    9. Lyu, Benmeng & Wu, Boqian & Guo, Sini & Gu, Jia-Wen & Ching, Wai-Ki, 2024. "Robust online portfolio optimization with cash flows," Omega, Elsevier, vol. 129(C).
    10. Feng, Haoyuan & Liu, Yue & Wu, Jie & Guo, Kun, 2023. "Financial market spillovers and macroeconomic shocks: Evidence from China," Research in International Business and Finance, Elsevier, vol. 65(C).
    11. Trino-Manuel Niguez & Ivan Paya & David Peel & Javier Perote, 2013. "Higher-order moments in the theory of diversification and portfolio composition," Working Papers 18297128, Lancaster University Management School, Economics Department.
    12. Vecchi, Edoardo & Berra, Gabriele & Albrecht, Steffen & Gagliardini, Patrick & Horenko, Illia, 2023. "Entropic approximate learning for financial decision-making in the small data regime," Research in International Business and Finance, Elsevier, vol. 65(C).
    13. Kirkby, J. Lars & Mitra, Sovan & Nguyen, Duy, 2020. "An analysis of dollar cost averaging and market timing investment strategies," European Journal of Operational Research, Elsevier, vol. 286(3), pages 1168-1186.
    14. Akhtaruzzaman, Md & Banerjee, Ameet Kumar & Boubaker, Sabri & Moussa, Faten, 2023. "Does green improve portfolio optimisation?," Energy Economics, Elsevier, vol. 124(C).
    15. Xu, Qifa & Li, Mengting & Jiang, Cuixia, 2021. "Network-augmented time-varying parametric portfolio selection: Evidence from the Chinese stock market," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    16. Francisco Peñaranda & Enrique Sentana, 2024. "Portfolio management with big data," Working Papers wp2024_2411, CEMFI.
    17. Cui, Tianxiang & Ding, Shusheng & Jin, Huan & Zhang, Yongmin, 2023. "Portfolio constructions in cryptocurrency market: A CVaR-based deep reinforcement learning approach," Economic Modelling, Elsevier, vol. 119(C).
    18. Tu, Xueyong & Li, Bin, 2024. "Robust portfolio selection with smart return prediction," Economic Modelling, Elsevier, vol. 135(C).
    19. Liu, Yong-Jun & Zhang, Wei-Guo, 2013. "Fuzzy portfolio optimization model under real constraints," Insurance: Mathematics and Economics, Elsevier, vol. 53(3), pages 704-711.
    20. Ren, Yi-Shuai & Ma, Chao-Qun & Kong, Xiao-Lin & Baltas, Konstantinos & Zureigat, Qasim, 2022. "Past, present, and future of the application of machine learning in cryptocurrency research," Research in International Business and Finance, Elsevier, vol. 63(C).

    More about this item

    Keywords

    Portfolio trading; Portfolio risk awareness; Transaction cost; Deep reinforcement learning; Portfolio constraint;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    Statistics

    Access and download statistics

    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:eee:glofin:v:62:y:2024:i:c:s1044028324000887. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/620162 .

    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.