A reinforcement learning extension to the Almgren-Chriss model for optimal trade execution

Citations

Cited by:

1. Thomas P. McAuliffe & Samuel Liew & Yuchao Li & Andrey Ushenin & Chihang Wang & Alexandros Tasos & Jack Pearce & Dimitris Tasoulis & Dimitri P. Bertsekas & Theodoros Tsagaris, 2026. "Model Predictive Control For Trade Execution," Papers 2603.28898, arXiv.org.
2. Brian Ning & Franco Ho Ting Lin & Sebastian Jaimungal, 2018. "Double Deep Q-Learning for Optimal Execution," Papers 1812.06600, arXiv.org, revised Jun 2020.
3. Alexandre Carbonneau & Frédéric Godin, 2023. "Deep Equal Risk Pricing of Financial Derivatives with Non-Translation Invariant Risk Measures," Risks, MDPI, vol. 11(8), pages 1-27, August.
4. Schnaubelt, Matthias, 2020. "Deep reinforcement learning for the optimal placement of cryptocurrency limit orders," FAU Discussion Papers in Economics 05/2020, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
5. Ben Hambly & Renyuan Xu & Huining Yang, 2021. "Recent Advances in Reinforcement Learning in Finance," Papers 2112.04553, arXiv.org, revised Feb 2023.
6. Soohan Kim & Jimyeong Kim & Hong Kee Sul & Youngjoon Hong, 2023. "An Adaptive Dual-level Reinforcement Learning Approach for Optimal Trade Execution," Papers 2307.10649, arXiv.org.
7. Söhnke M. Bartram & Jürgen Branke & Mehrshad Motahari, 2020. "Artificial intelligence in asset management," Working Papers 20202001, Cambridge Judge Business School, University of Cambridge.
   • Bartram, Söhnke & Branke, Jürgen & Motahari, Mehrshad, 2020. "Artificial Intelligence in Asset Management," CEPR Discussion Papers 14525, Centre for Economic Policy Research.
8. Ivan Jericevich & Patrick Chang & Tim Gebbie, 2020. "Comparing the market microstructure between two South African exchanges," Papers 2011.04367, arXiv.org.
9. Chau, Huy & Nguyen, Duy & Nguyen, Thai, 2026. "Continuous-time optimal investment with portfolio constraints: A reinforcement learning approach," European Journal of Operational Research, Elsevier, vol. 328(3), pages 1068-1092.
10. Shuo Sun & Rundong Wang & Bo An, 2021. "Reinforcement Learning for Quantitative Trading," Papers 2109.13851, arXiv.org.
11. Matthew Dicks & Andrew Paskaramoorthy & Tim Gebbie, 2023. "Many learning agents interacting with an agent-based market model," Papers 2303.07393, arXiv.org, revised Aug 2024.
12. Dicks, Matthew & Paskaramoorthy, Andrew & Gebbie, Tim, 2024. "A simple learning agent interacting with an agent-based market model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
13. Patrick Chang & Etienne Pienaar & Tim Gebbie, 2020. "Malliavin-Mancino estimators implemented with non-uniform fast Fourier transforms," Papers 2003.02842, arXiv.org, revised Nov 2020.
14. Donovan Platt & Tim Gebbie, 2016. "The Problem of Calibrating an Agent-Based Model of High-Frequency Trading," Papers 1606.01495, arXiv.org, revised Mar 2017.
15. Xiaodong Li & Pangjing Wu & Chenxin Zou & Qing Li, 2022. "Hierarchical Deep Reinforcement Learning for VWAP Strategy Optimization," Papers 2212.14670, arXiv.org.
16. Woo Jae Byun & Bumkyu Choi & Seongmin Kim & Joohyun Jo, 2023. "Practical Application of Deep Reinforcement Learning to Optimal Trade Execution," FinTech, MDPI, vol. 2(3), pages 1-16, June.
17. Robert de Witt & Mikko S. Pakkanen, 2026. "Diverse Approaches to Optimal Execution Schedule Generation," Papers 2601.22113, arXiv.org, revised Jan 2026.
18. Yuchao Dong, 2022. "Randomized Optimal Stopping Problem in Continuous time and Reinforcement Learning Algorithm," Papers 2208.02409, arXiv.org, revised Sep 2023.
19. Haoran Wang & Xun Yu Zhou, 2019. "Continuous-Time Mean-Variance Portfolio Selection: A Reinforcement Learning Framework," Papers 1904.11392, arXiv.org, revised May 2019.
20. Andrew Paskaramoorthy & Terence van Zyl & Tim Gebbie, 2020. "A Framework for Online Investment Algorithms," Papers 2003.13360, arXiv.org.
21. 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.
22. Alexandre Carbonneau & Fr'ed'eric Godin, 2021. "Deep equal risk pricing of financial derivatives with non-translation invariant risk measures," Papers 2107.11340, arXiv.org.
23. Dieter Hendricks, 2016. "Using real-time cluster configurations of streaming asynchronous features as online state descriptors in financial markets," Papers 1603.06805, arXiv.org, revised May 2017.
24. Wenhang Bao & Xiao-yang Liu, 2019. "Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis," Papers 1906.11046, arXiv.org.
25. Bokai Cao & Saizhuo Wang & Xinyi Lin & Xiaojun Wu & Haohan Zhang & Lionel M. Ni & Jian Guo, 2025. "From Deep Learning to LLMs: A survey of AI in Quantitative Investment," Papers 2503.21422, arXiv.org.
26. Ben Hambly & Renyuan Xu & Huining Yang, 2020. "Policy Gradient Methods for the Noisy Linear Quadratic Regulator over a Finite Horizon," Papers 2011.10300, arXiv.org, revised Jun 2021.
27. Yuchen Fang & Kan Ren & Weiqing Liu & Dong Zhou & Weinan Zhang & Jiang Bian & Yong Yu & Tie-Yan Liu, 2021. "Universal Trading for Order Execution with Oracle Policy Distillation," Papers 2103.10860, arXiv.org.
28. Harvey, M. & Hendricks, D. & Gebbie, T. & Wilcox, D., 2017. "Deviations in expected price impact for small transaction volumes under fee restructuring," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 416-426.
29. 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.
30. Ioanna-Yvonni Tsaknaki & Andrea Macr`i & Fabrizio Lillo, 2026. "Can Reinforcement Learning Efficiently Discover Price Manipulation?," Papers 2607.06121, arXiv.org.
31. Feiyang Pan & Tongzhe Zhang & Ling Luo & Jia He & Shuoling Liu, 2022. "Learn Continuously, Act Discretely: Hybrid Action-Space Reinforcement Learning For Optimal Execution," Papers 2207.11152, arXiv.org.