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Reducing transaction costs with low-latency trading algorithms

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  • Sasha Stoikov
  • Rolf Waeber

Abstract

We formulate a trade execution problem at the market microstructure level and solve it using dynamic programming. The objective is to sell a single lot of an asset in a short time horizon T, using the imbalance of the top of book bid and ask sizes as a price predictor. The optimization problem takes into account the latency L of the trading algorithm, which affects the prices at which the asset is traded. The solution divides the state space into a ‘trade’ and a ‘no-trade’ region. We calculate the cost of latency per lot traded and demonstrate that the advantage of observing the limit order book can dissipate quickly as execution latency increases. In the empirical section, we show that our optimal policy significantly outperforms a TWAP algorithm in liquidating on-the-run US treasury bonds, saving on average approximately 1/3 of the spread per share if trades are executed with low latency (≈$ \approx $1 ms).

Suggested Citation

  • Sasha Stoikov & Rolf Waeber, 2016. "Reducing transaction costs with low-latency trading algorithms," Quantitative Finance, Taylor & Francis Journals, vol. 16(9), pages 1445-1451, September.
    • Handle: RePEc:taf:quantf:v:16:y:2016:i:9:p:1445-1451
    DOI: 10.1080/14697688.2016.1151926
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    References listed on IDEAS

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    1. Rama Cont & Sasha Stoikov & Rishi Talreja, 2010. "A Stochastic Model for Order Book Dynamics," Operations Research, INFORMS, vol. 58(3), pages 549-563, June.
    2. Obizhaeva, Anna A. & Wang, Jiang, 2013. "Optimal trading strategy and supply/demand dynamics," Journal of Financial Markets, Elsevier, vol. 16(1), pages 1-32.
    3. Bertsimas, Dimitris & Lo, Andrew W., 1998. "Optimal control of execution costs," Journal of Financial Markets, Elsevier, vol. 1(1), pages 1-50, April.
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    Citations

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    Cited by:

    1. 'Alvaro Cartea & Sebastian Jaimungal & Leandro S'anchez-Betancourt, 2019. "Latency and Liquidity Risk," Papers 1908.03281, arXiv.org.
    2. Xuefeng Gao & Yunhan Wang, 2018. "Optimal Market Making in the Presence of Latency," Papers 1806.05849, arXiv.org, revised Mar 2020.
    3. Qixuan Luo & Yu Shi & Xuan Zhou & Handong Li, 2021. "Research on the Effects of Institutional Liquidation Strategies on the Market Based on Multi-agent Model," Computational Economics, Springer;Society for Computational Economics, vol. 58(4), pages 1025-1049, December.
    4. Daniel Fricke & Austin Gerig, 2018. "Too fast or too slow? Determining the optimal speed of financial markets," Quantitative Finance, Taylor & Francis Journals, vol. 18(4), pages 519-532, April.

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