A new approach for detecting high-frequency trading from order and trade data

Cumhur Ekinci and Oguz Ersan

Finance Research Letters, 2018, vol. 24, issue C, 313-320

Abstract: We suggest a two-step approach in detecting HFT activity from order and trade data. While the first step focuses on multiple actions of an order submitter in low latency, the second searches for the surroundings of these orders to link related orders. On a sample of 2015 data from Borsa Istanbul, we estimate that average HFT involvement is 1.23%. HFT activity is generally higher in large cap stocks (2.88%). Most HFT orders are in the form of very rapidly canceled order submissions. A robustness check reveals a mean accuracy rate of 97% in the linkage of orders.

Keywords: High-frequency trading (HFT); HFT detection; Low latency trading; Borsa Istanbul (search for similar items in EconPapers)
JEL-codes: G10 G12 G15 G23 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1544612317303926
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:24:y:2018:i:c:p:313-320

DOI: 10.1016/j.frl.2017.09.020

Access Statistics for this article

Finance Research Letters is currently edited by R. Gençay

More articles in Finance Research Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().