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Order scoring, bandit learning and order cancellations

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  • Gao, Xuefeng
  • Xu, Tianrun

Abstract

This paper develops an order scoring model that quantifies the performance of a limit order before execution. Our dynamic stochastic model takes into account of the bid-ask queue imbalance, the queue position of an order and price dynamics. We calibrate and validate the model using the historical order book data and backtesting simulations, and show that our model can perform well empirically. We also combine our model with multi-armed bandit learning to guide order cancellation decisions. We illustrate the empirical performances of various bandit algorithms and show that the Upper Confidence Bound algorithm generally performs the best.

Suggested Citation

  • Gao, Xuefeng & Xu, Tianrun, 2022. "Order scoring, bandit learning and order cancellations," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
    • Handle: RePEc:eee:dyncon:v:134:y:2022:i:c:s0165188921002220
    DOI: 10.1016/j.jedc.2021.104287
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    References listed on IDEAS

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    1. Long, Yunshen & Yan, Jingzhou & Wu, Liang & Long, Xingchen, 2024. "Market price determination: Interpreting quote order imbalance under zero-profit equilibrium," Economic Modelling, Elsevier, vol. 134(C).

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