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Model-Free Reinforcement Learning for Financial Portfolios: A Brief Survey

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  • Yoshiharu Sato

Abstract

Financial portfolio management is one of the problems that are most frequently encountered in the investment industry. Nevertheless, it is not widely recognized that both Kelly Criterion and Risk Parity collapse into Mean Variance under some conditions, which implies that a universal solution to the portfolio optimization problem could potentially exist. In fact, the process of sequential computation of optimal component weights that maximize the portfolio's expected return subject to a certain risk budget can be reformulated as a discrete-time Markov Decision Process (MDP) and hence as a stochastic optimal control, where the system being controlled is a portfolio consisting of multiple investment components, and the control is its component weights. Consequently, the problem could be solved using model-free Reinforcement Learning (RL) without knowing specific component dynamics. By examining existing methods of both value-based and policy-based model-free RL for the portfolio optimization problem, we identify some of the key unresolved questions and difficulties facing today's portfolio managers of applying model-free RL to their investment portfolios.

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  • Yoshiharu Sato, 2019. "Model-Free Reinforcement Learning for Financial Portfolios: A Brief Survey," Papers 1904.04973, arXiv.org, revised May 2019.
    • Handle: RePEc:arx:papers:1904.04973
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    References listed on IDEAS

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    Cited by:

    1. Longbing Cao, 2021. "AI in Finance: Challenges, Techniques and Opportunities," Papers 2107.09051, arXiv.org.
    2. Reilly Pickard & Yuri Lawryshyn, 2023. "Deep Reinforcement Learning for Dynamic Stock Option Hedging: A Review," Mathematics, MDPI, vol. 11(24), pages 1-19, December.
    3. Ben Hambly & Renyuan Xu & Huining Yang, 2021. "Recent Advances in Reinforcement Learning in Finance," Papers 2112.04553, arXiv.org, revised Feb 2023.
    4. Ali Al-Ameer & Khaled Alshehri, 2021. "Conditional Value-at-Risk for Quantitative Trading: A Direct Reinforcement Learning Approach," Papers 2109.14438, arXiv.org.
    5. Zhang, Hanwen & Dang, Duy-Minh, 2024. "A monotone numerical integration method for mean–variance portfolio optimization under jump-diffusion models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 219(C), pages 112-140.
    6. Hanwen Zhang & Duy-Minh Dang, 2023. "A monotone numerical integration method for mean-variance portfolio optimization under jump-diffusion models," Papers 2309.05977, arXiv.org.
    7. Adrian Millea, 2021. "Deep Reinforcement Learning for Trading—A Critical Survey," Data, MDPI, vol. 6(11), pages 1-25, November.
    8. Xiangyu Cui & Xun Li & Yun Shi & Si Zhao, 2023. "Discrete-Time Mean-Variance Strategy Based on Reinforcement Learning," Papers 2312.15385, arXiv.org.
    9. Eric Andr'e & Guillaume Coqueret, 2020. "Dirichlet policies for reinforced factor portfolios," Papers 2011.05381, arXiv.org, revised Jun 2021.
    10. van Staden, Pieter M. & Dang, Duy-Minh & Forsyth, Peter A., 2021. "The surprising robustness of dynamic Mean-Variance portfolio optimization to model misspecification errors," European Journal of Operational Research, Elsevier, vol. 289(2), pages 774-792.

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