AI 中文总结
该研究提出区分HFT两类策略的数据驱动度量，其优于传统指标，可捕捉HFT时间动态，能响应相关市场事件，还揭示了HFT对信息获取的差异化影响。
AI 中文摘要
我们引入了区分流动性供给型与流动性需求型策略的高频交易（HFT）数据驱动度量。我们在包含观测到的HFT活动的专有数据集上训练机器学习模型，随后将这些模型应用于2010-2023年间所有美国股票的公开日内数据，生成HFT度量。我们的度量优于传统代理指标，传统代理指标难以捕捉HFT的时间动态。与理论一致，我们的度量会对准外生速度 bump 引入和数据馈送升级做出响应。这些度量有助于揭示HFT对信息获取的差异化影响：流动性供给型HFT在财报公告期间提升价格信息有效性，而流动性需求型HFT则阻碍这一过程。
英文摘要
Public data do not identify high-frequency trading (HFT), and standard proxies do not separate liquidity-supplying from liquidity-demanding strategies. We overcome this measurement challenge by training machine learning models on proprietary Nasdaq data to map observed HFT activity to public intraday variables. Applying this mapping, we generate daily measures of liquidity-supplying and liquidity-demanding HFT for all U.S. stocks from 2010 to 2023. The measures largely subsume standard proxies and capture time-series variation that those proxies miss. Using proprietary Euronext Paris data, we provide evidence that the approach generalizes across markets and remains predictive years after training. The 14-year panel lets us study HFT and market quality over time. Supply-side HFT is consistently associated with greater pre-announcement information acquisition, more informed trading, and lower bid-ask spreads, while demand-side HFT is associated with the opposite patterns. During COVID-19, HFT-supplied liquidity remained resilient and its association with lower spreads strengthened.
CommentsThis submission has been withdrawn by the author; its results are subsumed by arXiv:2405.08101
