Data-Driven Measures of High-Frequency Trading

Gbenga Ibikunle; Ben Moews; Dmitriy Muravyev; Khaladdin Rzayev

Data-Driven Measures of High-Frequency Trading

General Finance 2026-08-01 v1 Trading and Market Microstructure

Authors: Gbenga Ibikunle , Ben Moews , Dmitriy Muravyev , Khaladdin Rzayev

Abstract

We introduce data-driven measures of high-frequency trading (HFT) that distinguish between liquidity-supplying and liquidity-demanding strategies. We train machine learning models on a proprietary dataset with observed HFT activity, then apply these models to public intraday data to generate HFT measures across all U.S. stocks during 2010-2023. Our measures outperform conventional proxies, which struggle to capture the temporal dynamics of HFT. Consistent with theory, our measures respond to a quasi-exogenous speed bump introduction and a data feed upgrade. The measures help uncover the differential impact of HFT on information acquisition. Liquidity-supplying HFT improves price informativeness around earnings announcements, while liquidity-demanding HFT impedes it.

Cite

@article{arxiv.2608.00858,
  title  = {Data-Driven Measures of High-Frequency Trading},
  author = {Gbenga Ibikunle and Ben Moews and Dmitriy Muravyev and Khaladdin Rzayev},
  journal= {arXiv preprint arXiv:2608.00858},
  year   = {2026}
}