Risk-Sensitive Portfolio Management by using Distributional Reinforcement Learning
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Publication Details
Author list: Harnpadungkij T., Chaisangmongkon W., Phunchongharn P.
Publisher: Hindawi
Publication year: 2019
ISBN: 9781728138213
ISSN: 0146-9428
eISSN: 1745-4557
Languages: English-Great Britain (EN-GB)
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Abstract
In recent years, many studies applied deep reinforcement learning in portfolio management. However, few studies have explored the use of value-based reinforcement learning as it is unclear how the risk of a portfolio can be incorporated. In this research, we proposed an agent called C21-SR by adapting the 21-bin categorical reinforcement learning and action-selection strategy based on Sharpe ratio to control the risk of investment and maximize profit. Our results revealed that a C21-SR agent could outperform buyhold and constant rebalance strategies, and the action-selection strategy based on the Sharpe ratio could enhance the performance of categorical reinforcement learning in the financial market. ฉ 2019 IEEE.
Keywords
Distributional Reinforcement Learning, Portfolio Management
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