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 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’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.

中文翻译：

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量化高频交易“军备竞赛”*

摘要

我们使用证券交易所消息数据来量化高频交易的负面影响，即“延迟套利”。消息数据与广为人知的限价订单簿数据之间的主要区别在于，消息数据包含失败的交易或取消尝试. 这允许研究人员观察比赛中的赢家和输家，而在限价订单簿数据中，您看不到输家，因此您无法直接看到比赛。我们发现延迟套利竞赛非常频繁（对于 FTSE 100 股票，每个品种大约每分钟一次），速度极快（模式竞赛持续百万分之 5-10 秒），并且占整体交易量的很大一部分（约 20%）。赛事参与集中，前六名企业占所有赛事输赢的80%以上。平均比赛只值一小笔钱（大约是价格的一半），但由于数量庞大，赌注加起来。我们的主要估计表明，种族构成了大约三分之一的价格影响和有效价差（流动性成本的关键微观结构衡量指标），