Cost-Sensitive Portfolio Selection via Deep Reinforcement Learning

Portfolio Selection is an important real-world financial task and has attracted extensive attention in artificial intelligence communities. This task, however, has two main difficulties: (i) the non-stationary price series and complex asset correlations make the learning of feature representation ve...

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Veröffentlicht in:IEEE transactions on knowledge and data engineering Jg. 34; H. 1; S. 236 - 248
Hauptverfasser: Zhang, Yifan, Zhao, Peilin, Wu, Qingyao, Li, Bin, Huang, Junzhou, Tan, Mingkui
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.01.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Artificial intelligence
Correlation
Cost analysis
Costs
Decision making
Deep learning
Feature extraction
Investment
Learning (artificial intelligence)
Optimization
Portfolio selection
Portfolios
Profitability
reinforcement learning
Representations
Risk management
Task analysis
transaction cost
ISSN:1041-4347, 1558-2191
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Zusammenfassung:Portfolio Selection is an important real-world financial task and has attracted extensive attention in artificial intelligence communities. This task, however, has two main difficulties: (i) the non-stationary price series and complex asset correlations make the learning of feature representation very hard; (ii) the practicality principle in financial markets requires controlling both transaction and risk costs. Most existing methods adopt handcraft features and/or consider no constraints for the costs, which may make them perform unsatisfactorily and fail to control both costs in practice. In this paper, we propose a cost-sensitive portfolio selection method with deep reinforcement learning. Specifically, a novel two-stream portfolio policy network is devised to extract both price series patterns and asset correlations, while a new cost-sensitive reward function is developed to maximize the accumulated return and constrain both costs via reinforcement learning. We theoretically analyze the near-optimality of the proposed reward, which shows that the growth rate of the policy regarding this reward function can approach the theoretical optimum. We also empirically evaluate the proposed method on real-world datasets. Promising results demonstrate the effectiveness and superiority of the proposed method in terms of profitability, cost-sensitivity and representation abilities.
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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2020.2979700