Quantifying the High-Frequency Trading "Arms Race": A Simple New Methodology and Estimates
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.
Year of publication: |
2020
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Authors: | Aquilina, Matteo ; Budish, Eric B. ; O'Neill, Peter |
Publisher: |
Chicago, IL : University of Chicago Booth School of Business, Stigler Center for the Study of the Economy and the State |
Saved in:
freely available
Series: | Working Paper ; 300 |
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Type of publication: | Book / Working Paper |
Type of publication (narrower categories): | Working Paper |
Language: | English |
Other identifiers: | 1815200324 [GVK] hdl:10419/262702 [Handle] RePEc:zbw:cbscwp:300 [RePEc] |
Source: |
Persistent link: https://www.econbiz.de/10013342572
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