The authors 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.
Occasional Paper No.50[1] (PDF)
Appendix[2] (PDF)
Addendum to Occasional Paper No.50[3] (PDF)
Summary
The authors use stock exchange message data to quantify the negative aspect of high-frequency trading, known as “latency arbitrage.” The main results show:
- races are frequent, fast and worth only small amounts per race
- a large proportion of daily trading volume is in races
- race participation is concentrated
- in aggregate, these small races make up a meaningful proportion of price impact
- in aggregate, these small races add up to meaningful harm to liquidity
- in aggregate, these small races add up to a meaningful total ‘size of the prize’
- The paper finds that while there is only a small detriment per transaction as a result, it adds up to a 17% reduction in the cost of liquidity and $5bn a year in tax on trading volume.
Authors
Matteo Aquilina, Financial Conduct Authority, Eric Budish, University of Chicago Booth School of Business and NBER and Peter O’Neill, Financial Conduct Authority
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