Deep Reinforcement Learning for Stock Trading with Behavioral Finance Strategy
MITD3 algorithm integrates momentum investment strategy with deep reinforcement learning for improved stock trading decisions.
What it examines
This paper proposes the Momentum Investment Twin Delay Deep Deterministic (MITD3) policy gradient algorithm for stock trading, integrating the momentum investment strategy with Deep Reinforcement Learning (DRL) to enhance trading decisions by leveraging historical performance data.
What it concludes
The research demonstrates that MITD3 can significantly improve stock trading strategies. Potential applications include automated trading systems and financial decision support tools. Future research could explore further enhancements and broader market applications.
Evidence objects
The research demonstrates that MITD3 can significantly improve stock trading strategies. Potential applications include automated trading systems and financial decision support tools. Future research could explore further enhancements and broader market applications.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
Deep Reinforcement Learning for?Stock Trading with?Behavioral Finance Strategy | SpringerLink Skip to main content Advertisement Log in Menu Find a journal Publish with us Track your research Search Cart Home Advanced Data Mining and Applications Conference paper Deep Reinforcement Learning for?Stock Trading with?Behavioral Finance Strategy Conference paper First Online: 05 November 2023 pp 535?549 Cite this conference paper Advanced Data Mining and Applications (ADMA 2023) Shilong Deng 15 , Zetao Zheng 15 , Hongcai He 15 & ? Jie Shao 15 , 16 Show authors Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14177)) Included in the following conference series: International Conference on Advanced Data Mining and Applications 699 Accesses 1 Altmetric Abstract Stock trading is a challenging task and has attracted extensive attention from artificial intelligence researchers. Deep Reinforcement Learning (DRL) approaches, which directly generate trading decisions by maxim
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