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

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  • Aquilina, Matteo
  • Budish, Eric B.
  • O'Neill, Peter

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

  • Aquilina, Matteo & Budish, Eric B. & O'Neill, Peter, 2020. "Quantifying the High-Frequency Trading "Arms Race": A Simple New Methodology and Estimates," Working Papers 300, The University of Chicago Booth School of Business, George J. Stigler Center for the Study of the Economy and the State.
    • Handle: RePEc:zbw:cbscwp:300
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    References listed on IDEAS

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    Cited by:

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    2. 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.

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