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Using Deep Reinforcement Learning with Hierarchical Risk Parity for Portfolio Optimization

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  • Adrian Millea

    (Department of Computing, Faculty of Engineering, Imperial College London, London SW7 2AZ, UK)

  • Abbas Edalat

    (Department of Computing, Faculty of Engineering, Imperial College London, London SW7 2AZ, UK)

Abstract

We devise a hierarchical decision-making architecture for portfolio optimization on multiple markets. At the highest level a Deep Reinforcement Learning (DRL) agent selects among a number of discrete actions, representing low-level agents. For the low-level agents, we use a set of Hierarchical Risk Parity (HRP) and Hierarchical Equal Risk Contribution (HERC) models with different hyperparameters, which all run in parallel, off-market (in a simulation). The information on which the DRL agent decides which of the low-level agents should act next is constituted by the stacking of the recent performances of all agents. Thus, the modelling resembles a statefull, non-stationary, multi-arm bandit, where the performance of the individual arms changes with time and is assumed to be dependent on the recent history. We perform experiments on the cryptocurrency market (117 assets), on the stock market (46 assets) and on the foreign exchange market (28 pairs) showing the excellent robustness and performance of the overall system. Moreover, we eliminate the need for retraining and are able to deal with large testing sets successfully.

Suggested Citation

  • Adrian Millea & Abbas Edalat, 2022. "Using Deep Reinforcement Learning with Hierarchical Risk Parity for Portfolio Optimization," IJFS, MDPI, vol. 11(1), pages 1-16, December.
    • Handle: RePEc:gam:jijfss:v:11:y:2022:i:1:p:10-:d:1019580
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    References listed on IDEAS

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    3. Kolm, Petter N. & Tütüncü, Reha & Fabozzi, Frank J., 2014. "60 Years of portfolio optimization: Practical challenges and current trends," European Journal of Operational Research, Elsevier, vol. 234(2), pages 356-371.
    4. 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.
    5. Amir Mosavi & Pedram Ghamisi & Yaser Faghan & Puhong Duan, 2020. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics," Papers 2004.01509, arXiv.org.
    6. Zhengyao Jiang & Dixing Xu & Jinjun Liang, 2017. "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem," Papers 1706.10059, arXiv.org, revised Jul 2017.
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