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

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

No 300, Working Papers from The University of Chicago Booth School of Business, George J. Stigler Center for the Study of the Economy and the State

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.

Date: 2020
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Citations: View citations in EconPapers (2)

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