Quantifying the High-Frequency Trading "Arms Race"
Initiative on Global Markets Paper No. 173
57 Pages Posted: 26 Jun 2020 Last revised: 15 Jul 2021
Date Written: July 2021
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 6 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.
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