Working Paper: NBER ID: w29011
Authors: Matteo Aquilina; Eric Budish; Peter O'Neill
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 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.
Keywords: High-Frequency Trading; Latency Arbitrage; Market Design; Liquidity Costs
JEL Codes: D47; G1; G12; G14
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
Cause | Effect |
---|---|
latency arbitrage races (F16) | market liquidity (G10) |
latency arbitrage races (F16) | trading costs (F12) |
latency arbitrage (F16) | price impact (G14) |
latency arbitrage (F16) | effective spread (E43) |
market design adjustments (D49) | liquidity costs (G33) |
latency arbitrage races (F16) | trading volume (G15) |