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Large scale continuous-time mean-variance portfolio allocation via reinforcement learning

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  • Haoran Wang

Abstract

We propose to solve large scale Markowitz mean-variance (MV) portfolio allocation problem using reinforcement learning (RL). By adopting the recently developed continuous-time exploratory control framework, we formulate the exploratory MV problem in high dimensions. We further show the optimality of a multivariate Gaussian feedback policy, with time-decaying variance, in trading off exploration and exploitation. Based on a provable policy improvement theorem, we devise a scalable and data-efficient RL algorithm and conduct large scale empirical tests using data from the S&P 500 stocks. We found that our method consistently achieves over 10% annualized returns and it outperforms econometric methods and the deep RL method by large margins, for both long and medium terms of investment with monthly and daily trading.

Suggested Citation

  • Haoran Wang, 2019. "Large scale continuous-time mean-variance portfolio allocation via reinforcement learning," Papers 1907.11718, arXiv.org, revised Aug 2019.
    • Handle: RePEc:arx:papers:1907.11718
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    References listed on IDEAS

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    1. Ang, Andrew & Gorovyy, Sergiy & van Inwegen, Gregory B., 2011. "Hedge fund leverage," Journal of Financial Economics, Elsevier, vol. 102(1), pages 102-126, October.
    2. Haoran Wang & Xun Yu Zhou, 2019. "Continuous-Time Mean-Variance Portfolio Selection: A Reinforcement Learning Framework," Papers 1904.11392, arXiv.org, revised May 2019.
    3. Mannor, Shie & Tsitsiklis, John N., 2013. "Algorithmic aspects of mean–variance optimization in Markov decision processes," European Journal of Operational Research, Elsevier, vol. 231(3), pages 645-653.
    4. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
    5. David Silver & Julian Schrittwieser & Karen Simonyan & Ioannis Antonoglou & Aja Huang & Arthur Guez & Thomas Hubert & Lucas Baker & Matthew Lai & Adrian Bolton & Yutian Chen & Timothy Lillicrap & Fan , 2017. "Mastering the game of Go without human knowledge," Nature, Nature, vol. 550(7676), pages 354-359, October.
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    Cited by:

    1. Zhou Fang, 2023. "Continuous-Time Path-Dependent Exploratory Mean-Variance Portfolio Construction," Papers 2303.02298, arXiv.org.
    2. Haoran Wang & Shi Yu, 2021. "Robo-Advising: Enhancing Investment with Inverse Optimization and Deep Reinforcement Learning," Papers 2105.09264, arXiv.org.
    3. 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.
    4. Xiangyu Cui & Xun Li & Yun Shi & Si Zhao, 2023. "Discrete-Time Mean-Variance Strategy Based on Reinforcement Learning," Papers 2312.15385, arXiv.org.
    5. Zhou Fang, 2023. "Electricity Virtual Bidding Strategy Via Entropy-Regularized Stochastic Control Method," Papers 2303.02303, arXiv.org.
    6. Gang Huang & Xiaohua Zhou & Qingyang Song, 2020. "Deep reinforcement learning for portfolio management," Papers 2012.13773, arXiv.org, revised Apr 2022.

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