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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, 2021. "Quantifying the high-frequency trading "arms race"," BIS Working Papers 955, Bank for International Settlements.
    • Handle: RePEc:bis:biswps:955
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    References listed on IDEAS

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    Cited by:

    1. Cipriani, Marco & Guarino, Antonio & Uthemann, Andreas, 2022. "Financial transaction taxes and the informational efficiency of financial markets: A structural estimation," Journal of Financial Economics, Elsevier, vol. 146(3), pages 1044-1072.
    2. Wolfgang Kuhle, 2023. "Latency arbitrage and the synchronized placement of orders," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-18, December.
    3. Nikhil Agarwal & Eric Budish, 2021. "Market Design," NBER Working Papers 29367, National Bureau of Economic Research, Inc.
    4. Arifovic, Jasmina & He, Xue-zhong & Wei, Lijian, 2022. "Machine learning and speed in high-frequency trading," Journal of Economic Dynamics and Control, Elsevier, vol. 139(C).

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    More about this item

    Keywords

    market design; high-frequency trading; financial exchanges; liquidity; latency arbitrage; trading volume; message data;
    All these keywords.

    JEL classification:

    • D47 - Microeconomics - - Market Structure, Pricing, and Design - - - Market Design
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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