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Optimal order display in limit order markets with liquidity competition

Author

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  • Cebiroğlu, Gökhan
  • Horst, Ulrich

Abstract

Order display is associated with benefits and costs. Benefits arise from increased execution-priority, while costs are due to adverse market impact. We analyze a structural model of optimal order placement that captures trade-off between the costs and benefits of order display. For a benchmark model of pure liquidity competition, we give a closed-form solution for optimal display sizes. We show that competition in liquidity supply incentivizes the use of hidden orders to prevent losses due to over-bidding. Thus, because aggressive liquidity competition is more prevalent in liquid stocks, our model predicts that the proportion of hidden liquidity is higher in liquid markets. Our theoretical considerations ares supported by an empirical analysis using high-frequency order-message data from NASDAQ. We find that there are no benefits in hiding orders in il-liquid stocks, whereas the performance gains can be significant in liquid stocks.

Suggested Citation

  • Cebiroğlu, Gökhan & Horst, Ulrich, 2015. "Optimal order display in limit order markets with liquidity competition," Journal of Economic Dynamics and Control, Elsevier, vol. 58(C), pages 81-100.
    • Handle: RePEc:eee:dyncon:v:58:y:2015:i:c:p:81-100
    DOI: 10.1016/j.jedc.2015.05.004
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Qing-Qing Yang & Wai-Ki Ching & Jiawen Gu & Tak-Kuen Siu, 2020. "Trading strategy with stochastic volatility in a limit order book market," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 43(1), pages 277-301, June.
    2. Ulrich Horst & Michael Paulsen, 2017. "A Law of Large Numbers for Limit Order Books," Mathematics of Operations Research, INFORMS, vol. 42(4), pages 1280-1312, November.
    3. Du, Bian & Zhu, Hongliang & Zhao, Jingdong, 2016. "Optimal execution in high-frequency trading with Bayesian learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 767-777.
    4. Gao, Xuefeng & Xu, Tianrun, 2022. "Order scoring, bandit learning and order cancellations," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
    5. Chen, Yuanyuan & Gao, Xuefeng & Li, Duan, 2018. "Optimal order execution using hidden orders," Journal of Economic Dynamics and Control, Elsevier, vol. 94(C), pages 89-116.
    6. Ulrich Horst & Dorte Kreher, 2015. "A weak law of large numbers for a limit order book model with fully state dependent order dynamics," Papers 1502.04359, arXiv.org, revised May 2016.
    7. Ulrich Horst & Wei Xu, 2017. "A Scaling Limit for Limit Order Books Driven by Hawkes Processes," Papers 1709.01292, arXiv.org, revised Aug 2018.
    8. Ulrich Horst & Dorte Kreher & Konstantins Starovoitovs, 2023. "Second-Order Approximation of Limit Order Books in a Single-Scale Regime," Papers 2308.00805, arXiv.org, revised Sep 2024.

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    More about this item

    Keywords

    Hidden liquidity; Liquidity competition; Limit order book; Market impact; Order flow dynamics; High-frequency trading;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • D01 - Microeconomics - - General - - - Microeconomic Behavior: Underlying Principles
    • D4 - Microeconomics - - Market Structure, Pricing, and Design
    • G1 - Financial Economics - - General Financial Markets

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