Quantifying the High-Frequency Trading “Arms Race”

Coauthors: Matteo Aquilina and Peter O'Neill
The Quarterly Journal of Economics, (2022): 137, no. 1, 493-564. [PDF]

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.

Awards

Winner — WFA 2020 Two Sigma Award for Best Paper on Investment Management

Appendices

Data and Code Appendix

Matteo Aquilina and Peter O'Neill
The Quarterly Journal of Economics, [PDF]

Code

Exchange Message Data Codebook

Code and Detailed Guidebook for researchers, academics and regulators to conduct their own studies using exchange message data.

Slides

Seminar slides

[PDF]

Video