Using Deep Reinforcement Learning with Hierarchical Risk Parity for Portfolio Optimization

Adrian Millea () and Abbas Edalat
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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

IJFS, 2022, vol. 11, issue 1, 1-16

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.

Keywords: Deep Reinforcement Learning; Hierarchical Risk Parity; Hierarchical Equal Risk Contribution; portfolio optimization; cryptocurrencies; stocks; foreign exchange (search for similar items in EconPapers)
JEL-codes: F2 F3 F41 F42 G1 G2 G3 (search for similar items in EconPapers)
Date: 2022
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