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Quantifying the High-Frequency Trading “Arms Race†: A Simple New Methodology and Estimates

Author

Listed:
  • Matteo Aquilina

    (Financial Conduct Authority)

  • Eric Budish

    (University of Chicago - Booth School of Business; NBER)

Abstract

We use stock exchange message data to quantify the negative aspect of high-frequency trading, known as “latency arbitrage.†The key difference between message data and widely-familiar limit order book data is that message data contain attempts to trade or cancel that fail. This allows the researcher to observe both winners and losers in a race, whereas in limit order book data you cannot see the losers, so you cannot directly see the races. We find that latency-arbitrage races are very frequent (about one per minute per symbol for FTSE 100 stocks), extremely fast (the modal race lasts 5-10 millionths of a second), and account for a large portion of overall trading volume (about 20%). Race participation is concentrated, with the top 6 firms accounting for over 80% of all race wins and losses. Most races (about 90%) are won by an aggressive order as opposed to a cancel attempt; market participants outside the top 6 firms disproportionately provide the liquidity that gets taken in races (about 60%). Our main estimates suggest that eliminating latency arbitrage would reduce the market’s cost of liquidity by 17% and that the total sums at stake are on the order of $5 billion annually in global equity markets.

Suggested Citation

  • Matteo Aquilina & Eric Budish, 2020. "Quantifying the High-Frequency Trading “Arms Race†: A Simple New Methodology and Estimates," Working Papers 2020-86, Becker Friedman Institute for Research In Economics.
    • Handle: RePEc:bfi:wpaper:2020-86
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    References listed on IDEAS

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    Cited by:

    1. Haeringer, Guillaume & Melton, Hayden, 2020. "High Frequency Fairness," MPRA Paper 103907, University Library of Munich, Germany.
    2. Li, Sida & Wang, Xin & Ye, Mao, 2021. "Who provides liquidity, and when?," Journal of Financial Economics, Elsevier, vol. 141(3), pages 968-980.
    3. Jose S. Penalva & Mikel Tapia, 2021. "Heterogeneity and Competition in Fragmented Markets: Fees Vs Speed," Applied Mathematical Finance, Taylor & Francis Journals, vol. 28(2), pages 143-177, March.
    4. Zheng, Jiayi & Zhu, Yushu, 2023. "Algorithmic trading and block ownership initiation: An information perspective," The British Accounting Review, Elsevier, vol. 55(4).
    5. Mark Marner-Hausen, 2022. "Developing a Framework for Real-Time Trading in a Laboratory Financial Market," ECONtribute Discussion Papers Series 172, University of Bonn and University of Cologne, Germany.
    6. Wolfgang Kuhle, 2021. "On Market Design and Latency Arbitrage," Papers 2202.00127, arXiv.org.
    7. Khairul Zharif Zaharudin & Martin R. Young & Wei‐Huei Hsu, 2022. "High‐frequency trading: Definition, implications, and controversies," Journal of Economic Surveys, Wiley Blackwell, vol. 36(1), pages 75-107, February.
    8. Dodd, Olga & Frijns, Bart & Indriawan, Ivan & Pascual, Roberto, 2023. "US cross-listing and domestic high-frequency trading: Evidence from Canadian stocks," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 301-320.
    9. Baldauf, Markus & Mollner, Joshua, 2022. "Fast traders make a quick buck: The role of speed in liquidity provision," Journal of Financial Markets, Elsevier, vol. 58(C).
    10. Khapko, Mariana & Zoican, Marius, 2021. "Do speed bumps curb low-latency investment? Evidence from a laboratory market," Journal of Financial Markets, Elsevier, vol. 55(C).
    11. Yan Chen & Peter Cramton & John A. List & Axel Ockenfels, 2021. "Market Design, Human Behavior, and Management," Management Science, INFORMS, vol. 67(9), pages 5317-5348, September.
    12. Joffrey Derchu & Philippe Guillot & Thibaut Mastrolia & Mathieu Rosenbaum, 2020. "AHEAD : Ad-Hoc Electronic Auction Design," Papers 2010.02827, arXiv.org.

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