Aquilina, Matteo
[Verfasser:in]
;
Budish, Eric B.
[Sonstige Person, Familie und Körperschaft];
O'Neill, Peter
[Sonstige Person, Familie und Körperschaft]
Quantifying the High-Frequency Trading 'Arms Race'
Erschienen in:Chicago Booth Research Paper ; No. 20-16, Chicago Booth: George J. Stigler Center for the Study of the Economy & the State Working Paper No. 45
Umfang:
1 Online-Ressource (95 p)
Sprache:
Englisch
DOI:
10.2139/ssrn.3636323
Identifikator:
Entstehung:
Anmerkungen:
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments June 25, 2020 erstellt
Beschreibung:
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