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Benchmarking deep reinforcement learning approaches to trade execution

Author

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  • Tonkin, Isaac
  • Gepp, Adrian
  • Harris, Geoff
  • Vanstone, Bruce

Abstract

Trade execution is an ongoing optimisation problem in finance, focussed on attaining the best prices through forecasting future price movements and liquidity. This is particularly relevant to institutional investors transacting large positions.

Suggested Citation

  • Tonkin, Isaac & Gepp, Adrian & Harris, Geoff & Vanstone, Bruce, 2025. "Benchmarking deep reinforcement learning approaches to trade execution," Pacific-Basin Finance Journal, Elsevier, vol. 94(C).
    • Handle: RePEc:eee:pacfin:v:94:y:2025:i:c:s0927538x25002136
    DOI: 10.1016/j.pacfin.2025.102876
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Park, Seongkyu Gilbert & Ryu, Doojin, 2019. "Speed and trading behavior in an order-driven market," Pacific-Basin Finance Journal, Elsevier, vol. 53(C), pages 145-164.
    4. Verhoeven, Peter & Ching, Simon & Guan Ng, Hock, 2004. "Determinants of the decision to submit market or limit orders on the ASX," Pacific-Basin Finance Journal, Elsevier, vol. 12(1), pages 1-18, January.
    5. Ben Hambly & Renyuan Xu & Huining Yang, 2021. "Recent Advances in Reinforcement Learning in Finance," Papers 2112.04553, arXiv.org, revised Feb 2023.
    6. 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.
    7. Xiaodong Li & Pangjing Wu & Chenxin Zou & Qing Li, 2022. "Hierarchical Deep Reinforcement Learning for VWAP Strategy Optimization," Papers 2212.14670, arXiv.org.
    Full references (including those not matched with items on IDEAS)

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