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
Online-Zugang:	Volltext
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