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Quantifying the High-Frequency Trading “Arms Race”

Author

Listed:
  • Matteo Aquilina
  • Eric Budish
  • Peter O’Neill
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 remarkably large portion of overall trading volume (about 20%). Race participation is concentrated, with the top six firms accounting for over 80% of all race wins and losses. The average race is worth just a small amount (about half a price tick), but because of the large volumes the stakes add up. Our main estimates suggest that races constitute roughly one-third of price impact and the effective spread (key microstructure measures of the cost of liquidity), that latency arbitrage imposes a roughly 0.5 basis point tax on trading, that market designs that eliminate 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 per year in global equity markets alone.

Suggested Citation

  • Matteo Aquilina & Eric Budish & Peter O’Neill, 2022. "Quantifying the High-Frequency Trading “Arms Race”," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 137(1), pages 493-564.
    • Handle: RePEc:oup:qjecon:v:137:y:2022:i:1:p:493-564.
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    File URL: http://hdl.handle.net/10.1093/qje/qjab032
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    References listed on IDEAS

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    4. Sagade, Satchit & Scharnowski, Stefan & Theissen, Erik & Westheide, Christian, 2024. "A tale of two cities: Inter-market latency and fast-trader competition," SAFE Working Paper Series 430, Leibniz Institute for Financial Research SAFE.
    5. Giuliano Graziani & Barbara Rindi, 2023. "Optimal Tick Size," Working Papers 688, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

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