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Market Making via Reinforcement Learning

Citations

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Cited by:

  1. Jiafa He & Cong Zheng & Can Yang, 2023. "Integrating Tick-level Data and Periodical Signal for High-frequency Market Making," Papers 2306.17179, arXiv.org.
  2. Johann Lussange & Ivan Lazarevich & Sacha Bourgeois-Gironde & Stefano Palminteri & Boris Gutkin, 2021. "Modelling Stock Markets by Multi-agent Reinforcement Learning," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 113-147, January.
  3. Abbas Haider & Hui Wang & Bryan Scotney & Glenn Hawe, 2022. "Predictive Market Making via Machine Learning," SN Operations Research Forum, Springer, vol. 3(1), pages 1-21, March.
  4. Aymeric Vie & J. Doyne Farmer, 2023. "Towards Evology: a Market Ecology Agent-Based Model of US Equity Mutual Funds II," Papers 2302.01216, arXiv.org.
  5. Christian Janiesch & Patrick Zschech & Kai Heinrich, 2021. "Machine learning and deep learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 685-695, September.
  6. Johann Lussange & Boris Gutkin, 2023. "Order book regulatory impact on stock market quality: a multi-agent reinforcement learning perspective," Papers 2302.04184, arXiv.org.
  7. Nelson Vadori & Leo Ardon & Sumitra Ganesh & Thomas Spooner & Selim Amrouni & Jared Vann & Mengda Xu & Zeyu Zheng & Tucker Balch & Manuela Veloso, 2022. "Towards Multi-Agent Reinforcement Learning driven Over-The-Counter Market Simulations," Papers 2210.07184, arXiv.org, revised Aug 2023.
  8. Bruno Gav{s}perov & Zvonko Kostanjv{c}ar, 2022. "Deep Reinforcement Learning for Market Making Under a Hawkes Process-Based Limit Order Book Model," Papers 2207.09951, arXiv.org.
  9. Arthur Charpentier & Romuald Elie & Carl Remlinger, 2020. "Reinforcement Learning in Economics and Finance," Papers 2003.10014, arXiv.org.
  10. Svitlana Vyetrenko & David Byrd & Nick Petosa & Mahmoud Mahfouz & Danial Dervovic & Manuela Veloso & Tucker Hybinette Balch, 2019. "Get Real: Realism Metrics for Robust Limit Order Book Market Simulations," Papers 1912.04941, arXiv.org.
  11. Yuanrong Wang & Yinsen Miao & Alexander CY Wong & Nikita P Granger & Christian Michler, 2023. "Domain-adapted Learning and Interpretability: DRL for Gas Trading," Papers 2301.08359, arXiv.org, revised Sep 2023.
  12. Ben Hambly & Renyuan Xu & Huining Yang, 2021. "Recent Advances in Reinforcement Learning in Finance," Papers 2112.04553, arXiv.org, revised Feb 2023.
  13. Bruno Gašperov & Stjepan Begušić & Petra Posedel Šimović & Zvonko Kostanjčar, 2021. "Reinforcement Learning Approaches to Optimal Market Making," Mathematics, MDPI, vol. 9(21), pages 1-22, October.
  14. Jonathan Sadighian, 2019. "Deep Reinforcement Learning in Cryptocurrency Market Making," Papers 1911.08647, arXiv.org.
  15. Ben Hambly & Renyuan Xu & Huining Yang, 2023. "Recent advances in reinforcement learning in finance," Mathematical Finance, Wiley Blackwell, vol. 33(3), pages 437-503, July.
  16. Amit Milstein & Haoran Deng & Guy Revach & Hai Morgenstern & Nir Shlezinger, 2022. "Neural Augmented Kalman Filtering with Bollinger Bands for Pairs Trading," Papers 2210.15448, arXiv.org, revised Sep 2023.
  17. Michael Karpe & Jin Fang & Zhongyao Ma & Chen Wang, 2020. "Multi-Agent Reinforcement Learning in a Realistic Limit Order Book Market Simulation," Papers 2006.05574, arXiv.org, revised Sep 2020.
  18. Michael Karpe, 2020. "An overall view of key problems in algorithmic trading and recent progress," Papers 2006.05515, arXiv.org.
  19. Thomas Spooner & Rahul Savani, 2020. "Robust Market Making via Adversarial Reinforcement Learning," Papers 2003.01820, arXiv.org, revised Jul 2020.
  20. Tristan Lim, 2022. "Predictive Crypto-Asset Automated Market Making Architecture for Decentralized Finance using Deep Reinforcement Learning," Papers 2211.01346, arXiv.org, revised Jan 2023.
  21. Andrea Coletta & Matteo Prata & Michele Conti & Emanuele Mercanti & Novella Bartolini & Aymeric Moulin & Svitlana Vyetrenko & Tucker Balch, 2021. "Towards Realistic Market Simulations: a Generative Adversarial Networks Approach," Papers 2110.13287, arXiv.org.
  22. Pankaj Kumar, 2021. "Deep Hawkes Process for High-Frequency Market Making," Papers 2109.15110, arXiv.org.
  23. Johann Lussange & Stefano Vrizzi & Sacha Bourgeois-Gironde & Stefano Palminteri & Boris Gutkin, 2023. "Stock Price Formation: Precepts from a Multi-Agent Reinforcement Learning Model," Computational Economics, Springer;Society for Computational Economics, vol. 61(4), pages 1523-1544, April.
  24. Shuo Sun & Molei Qin & Xinrun Wang & Bo An, 2023. "PRUDEX-Compass: Towards Systematic Evaluation of Reinforcement Learning in Financial Markets," Papers 2302.00586, arXiv.org, revised Mar 2023.
  25. Iwao Maeda & David deGraw & Michiharu Kitano & Hiroyasu Matsushima & Hiroki Sakaji & Kiyoshi Izumi & Atsuo Kato, 2020. "Deep Reinforcement Learning in Agent Based Financial Market Simulation," JRFM, MDPI, vol. 13(4), pages 1-17, April.
  26. Bingyan Han, 2022. "Can maker-taker fees prevent algorithmic cooperation in market making?," Papers 2211.00496, arXiv.org.
  27. Jonathan Sadighian, 2020. "Extending Deep Reinforcement Learning Frameworks in Cryptocurrency Market Making," Papers 2004.06985, arXiv.org.
  28. Arthur Charpentier & Romuald Élie & Carl Remlinger, 2023. "Reinforcement Learning in Economics and Finance," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 425-462, June.
  29. Gao, Xuefeng & Xu, Tianrun, 2022. "Order scoring, bandit learning and order cancellations," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
  30. Hong Guo & Jianwu Lin & Fanlin Huang, 2023. "Market Making with Deep Reinforcement Learning from Limit Order Books," Papers 2305.15821, arXiv.org.
  31. Penghang Liu & Kshama Dwarakanath & Svitlana S Vyetrenko & Tucker Balch, 2022. "Limited or Biased: Modeling Sub-Rational Human Investors in Financial Markets," Papers 2210.08569, arXiv.org, revised Mar 2024.
  32. Sumitra Ganesh & Nelson Vadori & Mengda Xu & Hua Zheng & Prashant Reddy & Manuela Veloso, 2019. "Reinforcement Learning for Market Making in a Multi-agent Dealer Market," Papers 1911.05892, arXiv.org.
  33. Shuo Sun & Rundong Wang & Bo An, 2021. "Reinforcement Learning for Quantitative Trading," Papers 2109.13851, arXiv.org.
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