---
title: Identifying High Frequency Trading Activity without Proprietary Data
url: https://www.ml-quant.com/papers/ssrn/4551238/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 4551238
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4551238
featured: 2023-08-30
citations: 4
topic: Trading, Microstructure & Execution
---


# Identifying High Frequency Trading Activity without Proprietary Data

The article evaluates the reliability of commonly used indicators to identify high frequency traders, showing variations in performance and suggesting that unscaled proxies are more effective at indicating true HFT activity.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4551238
- Identifier: SSRN 4551238
- Released: 2023-08-24
- First featured: Quant Letter No. 14 (2023-08-30): https://www.ml-quant.com/issues/2023-08-30/
- Citations (Semantic Scholar): 4
- Published in: not yet
- Topic: Trading, Microstructure & Execution

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