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A deep learning approach to estimating fill probabilities in a limit order book

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  • Costis Maglaras
  • Ciamac C. Moallemi
  • Muye Wang

Abstract

Deciding between the use of market orders and limit orders is an important question in practical optimal trading problems. A key ingredient in making this decision is understanding the uncertainty of the execution of a limit order, that is, the fill probability or the probability that an order will be executed within a certain time horizon. Equivalently, one can estimate the distribution of the time-to-fill. We propose a data-driven approach based on a recurrent neural network to estimate the distribution of time-to-fill for a limit order conditional on the current market conditions. Using a historical data set, we demonstrate the superiority of this approach to several benchmark techniques. This approach also leads to significant cost reductions while implementing a trading strategy in a prototypical trading problem.

Suggested Citation

  • Costis Maglaras & Ciamac C. Moallemi & Muye Wang, 2022. "A deep learning approach to estimating fill probabilities in a limit order book," Quantitative Finance, Taylor & Francis Journals, vol. 22(11), pages 1989-2003, November.
    • Handle: RePEc:taf:quantf:v:22:y:2022:i:11:p:1989-2003
    DOI: 10.1080/14697688.2022.2124189
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    Cited by:

    1. Felix Lokin & Fenghui Yu, 2024. "Fill Probabilities in a Limit Order Book with State-Dependent Stochastic Order Flows," Papers 2403.02572, arXiv.org.
    2. Zhenglong Li & Vincent Tam & Kwan L. Yeung, 2024. "Developing A Multi-Agent and Self-Adaptive Framework with Deep Reinforcement Learning for Dynamic Portfolio Risk Management," Papers 2402.00515, arXiv.org, revised Feb 2024.
    3. Xianfeng Jiao & Zizhong Li & Chang Xu & Yang Liu & Weiqing Liu & Jiang Bian, 2023. "Microstructure-Empowered Stock Factor Extraction and Utilization," Papers 2308.08135, arXiv.org.

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