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Reinforcement learning in financial markets - a survey

Citations

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Cited by:

  1. Weiguang Han & Boyi Zhang & Qianqian Xie & Min Peng & Yanzhao Lai & Jimin Huang, 2023. "Select and Trade: Towards Unified Pair Trading with Hierarchical Reinforcement Learning," Papers 2301.10724, arXiv.org, revised Feb 2023.
  2. Ben Hambly & Renyuan Xu & Huining Yang, 2021. "Recent Advances in Reinforcement Learning in Finance," Papers 2112.04553, arXiv.org, revised Feb 2023.
  3. Xiao-Yang Liu & Hongyang Yang & Jiechao Gao & Christina Dan Wang, 2021. "FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance," Papers 2111.09395, arXiv.org.
  4. Jonas Hanetho, 2023. "Commodities Trading through Deep Policy Gradient Methods," Papers 2309.00630, arXiv.org.
  5. Maximilian Wehrmann & Nico Zengeler & Uwe Handmann, 2021. "Observation Time Effects in Reinforcement Learning on Contracts for Difference," JRFM, MDPI, vol. 14(2), pages 1-15, January.
  6. Kropiński, Paweł & Bosek, Bartłomiej & Pudo, Mikołaj, 2024. "State ownership, probability of informed trading, and profitability potential: Evidence from the Warsaw Stock Exchange," International Review of Financial Analysis, Elsevier, vol. 95(PB).
  7. Kruthof, Garvin & Müller, Sebastian, 2025. "Can deep reinforcement learning beat 1N," Finance Research Letters, Elsevier, vol. 75(C).
  8. Yangyang Yu & Haohang Li & Zhi Chen & Yuechen Jiang & Yang Li & Denghui Zhang & Rong Liu & Jordan W. Suchow & Khaldoun Khashanah, 2023. "FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design," Papers 2311.13743, arXiv.org, revised Dec 2023.
  9. Schnaubelt, Matthias, 2022. "Deep reinforcement learning for the optimal placement of cryptocurrency limit orders," European Journal of Operational Research, Elsevier, vol. 296(3), pages 993-1006.
  10. Xiao-Yang Liu & Hongyang Yang & Qian Chen & Runjia Zhang & Liuqing Yang & Bowen Xiao & Christina Dan Wang, 2020. "FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance," Papers 2011.09607, arXiv.org, revised Mar 2022.
  11. Shuo Sun & Rundong Wang & Bo An, 2021. "Reinforcement Learning for Quantitative Trading," Papers 2109.13851, arXiv.org.
  12. Eric Benhamou & David Saltiel & Sandrine Ungari & Abhishek Mukhopadhyay, 2020. "Bridging the gap between Markowitz planning and deep reinforcement learning," Papers 2010.09108, arXiv.org.
  13. Johann Lussange & Stefano Vrizzi & Stefano Palminteri & Boris Gutkin, 2024. "Mesoscale effects of trader learning behaviors in financial markets: A multi-agent reinforcement learning study," PLOS ONE, Public Library of Science, vol. 19(4), pages 1-40, April.
  14. Charl Maree & Christian W. Omlin, 2022. "Balancing Profit, Risk, and Sustainability for Portfolio Management," Papers 2207.02134, arXiv.org.
  15. Tidor-Vlad Pricope, 2021. "Deep Reinforcement Learning in Quantitative Algorithmic Trading: A Review," Papers 2106.00123, arXiv.org.
  16. Philippe Bergault & Olivier Gu'eant & Hamza Bodor, 2025. "To Hedge or Not to Hedge: Optimal Strategies for Stochastic Trade Flow Management," Papers 2503.02496, arXiv.org.
  17. Johann Lussange & Stefano Vrizzi & Stefano Palminteri & Boris Gutkin, 2024. "Mesoscale effects of trader learning behaviors in financial markets: A multi-agent reinforcement learning study," Post-Print hal-04790290, HAL.
  18. Adrian Millea, 2021. "Deep Reinforcement Learning for Trading—A Critical Survey," Data, MDPI, vol. 6(11), pages 1-25, November.
  19. Jiwon Kim & Moon-Ju Kang & KangHun Lee & HyungJun Moon & Bo-Kwan Jeon, 2023. "Deep Reinforcement Learning for Asset Allocation: Reward Clipping," Papers 2301.05300, arXiv.org.
  20. MohammadAmin Fazli & Mahdi Lashkari & Hamed Taherkhani & Jafar Habibi, 2022. "A Novel Experts Advice Aggregation Framework Using Deep Reinforcement Learning for Portfolio Management," Papers 2212.14477, arXiv.org.
  21. Zihao Zhang & Stefan Zohren & Stephen Roberts, 2019. "Deep Reinforcement Learning for Trading," Papers 1911.10107, arXiv.org.
  22. Longbing Cao, 2021. "AI in Finance: Challenges, Techniques and Opportunities," Papers 2107.09051, arXiv.org.
  23. Federico Cornalba & Constantin Disselkamp & Davide Scassola & Christopher Helf, 2022. "Multi-Objective reward generalization: Improving performance of Deep Reinforcement Learning for applications in single-asset trading," Papers 2203.04579, arXiv.org, revised Feb 2023.
  24. Jingyuan Wang & Yang Zhang & Ke Tang & Junjie Wu & Zhang Xiong, 2019. "AlphaStock: A Buying-Winners-and-Selling-Losers Investment Strategy using Interpretable Deep Reinforcement Attention Networks," Papers 1908.02646, arXiv.org.
  25. Suyeol Yun, 2024. "Pretrained LLM Adapted with LoRA as a Decision Transformer for Offline RL in Quantitative Trading," Papers 2411.17900, arXiv.org.
  26. Weiguang Han & Jimin Huang & Qianqian Xie & Boyi Zhang & Yanzhao Lai & Min Peng, 2023. "Mastering Pair Trading with Risk-Aware Recurrent Reinforcement Learning," Papers 2304.00364, arXiv.org.
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