Quantifying the High-Frequency Trading “Arms Raceâ€: A Simple New Methodology and Estimates

Matteo Aquilina and Eric Budish ()
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Eric Budish: University of Chicago - Booth School of Business; NBER

No 2020-86, Working Papers from Becker Friedman Institute for Research In Economics

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.

Pages: 93 pages
Date: 2020
New Economics Papers: this item is included in nep-cwa and nep-mst
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Citations: View citations in EconPapers (13)

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