{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T06:22:15Z","timestamp":1793686935180,"version":"4.5.1"},"reference-count":86,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T00:00:00Z","timestamp":1631232000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,12,24]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>We use stock exchange message data to quantify the negative aspect of high-frequency trading, known as \u201clatency arbitrage.\u201d 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\u201310\u2009millionths of a second), and account for a remarkably large portion of overall trading volume (about 20%). Race participation is concentrated, with the top six 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\u2019s 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.<\/jats:p>","DOI":"10.1093\/qje\/qjab032","type":"journal-article","created":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T07:28:41Z","timestamp":1631086121000},"page":"493-564","source":"Crossref","is-referenced-by-count":149,"title":["Quantifying the High-Frequency Trading \u201cArms Race\u201d"],"prefix":"10.1093","volume":"137","author":[{"given":"Matteo","family":"Aquilina","sequence":"first","affiliation":[{"name":"Financial Conduct Authority, United Kingdom and Financial Stability Board, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric","family":"Budish","sequence":"additional","affiliation":[{"name":"University of Chicago Booth School of Business and National Bureau of Economic Research, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"O\u2019Neill","sequence":"additional","affiliation":[{"name":"Financial Conduct Authority, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,9,10]]},"reference":[{"key":"2021122504421900800_bib1","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/j.jfineco.2004.06.007","article-title":"\u201cAsset Pricing with Liquidity Risk,\u201d","volume":"77","author":"Acharya","year":"2005","journal-title":"Journal of Financial Economics"},{"key":"2021122504421900800_bib2","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/S1386-4181(01)00024-6","article-title":"\u201cIlliquidity and Stock Returns: Cross-Section and Time-Series Effects,\u201d","volume":"5","author":"Amihud","year":"2002","journal-title":"Journal of Financial Markets"},{"key":"2021122504421900800_bib3","doi-asserted-by":"crossref","first-page":"1550002","DOI":"10.1142\/S2010139215500020","article-title":"\u201cEquity Trading in the 21st Century: An Update,\u201d","volume":"5","author":"Angel","year":"2015","journal-title":"Quarterly Journal of Finance"},{"key":"2021122504421900800_bib4","doi-asserted-by":"crossref","DOI":"10.2139\/ssrn.3636323","article-title":"\u201cQuantifying the High-Frequency Trading \u2018Arms Race\u2019: A New Methodology and Estimates,\u201d","author":"Aquilina","year":"2020"},{"key":"2021122504421900800_bib5","article-title":"\u201cReplication Data for: \u2018Quantifying the High-Frequency Trading \u201cArms Race\u201d\u2019,\u201d","author":"\u2014\u2014\u2014","year":"2021"},{"key":"2021122504421900800_bib6","article-title":"\u201cAsymmetries in Dark Pool Reference Prices,\u201d","author":"Aquilina","year":"2016"},{"key":"2021122504421900800_bib7","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1017\/S0022109018001096","article-title":"\u201cRisk and Return in High-Frequency Trading,\u201d","volume":"54","author":"Baron","year":"2019","journal-title":"Journal of Financial and Quantitative Analysis"},{"key":"2021122504421900800_bib8","doi-asserted-by":"crossref","first-page":"2193","DOI":"10.1111\/jofi.12422","article-title":"\u201cCan Brokers Have It All? 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