arXiv：cs.LG· G. Ibikunle, B. Moews, D. Muravyev, K. Rzayev·· 7 小时前AI 评分33
基于机器学习的高频交易测度：用 Nasdaq 专有数据构建美股 2010-2023 年日度指标
Data-driven measures of high-frequency trading
AI 导读
研究团队用机器学习模型在 Nasdaq 专有数据上训练，将可观测的高频交易活动映射到公开日内变量，从而为 2010 至 2023 年全部美股生成区分供给流动性与需求流动性的日度 HFT 指标。
正文
Abstract: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.
| Comments: | 86 pages, 10 figures, 22 tables |
| Subjects: | Computational Finance (q-fin.CP); Machine Learning (cs.LG) |
| MSC classes: | 91G15, 62P20 |
| Cite as: | arXiv:2405.08101 [q-fin.CP] |
| (or arXiv:2405.08101v4 [q-fin.CP] for this version) | |
| https://doi.org/10.48550/arXiv.2405.08101 arXiv-issued DOI via DataCite |
Submission history
From: Ben Moews [view email]
[v1]
Mon, 13 May 2024 18:28:39 UTC (849 KB)
[v2]
Fri, 17 Jan 2025 15:57:52 UTC (714 KB)
[v3]
Fri, 21 Mar 2025 17:31:44 UTC (1,676 KB)
[v4]
Mon, 5 Oct 2026 17:07:23 UTC (2,702 KB)
来源：arXiv：cs.LG · arxiv.org