---
title: Deep Unsupervised Anomaly Detection in High-Frequency Markets
url: https://www.ml-quant.com/papers/ssrn/4502662/
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 4502662
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4502662
featured: 2023-07-12
citations: 0
topic: Trading, Microstructure & Execution
---


# Deep Unsupervised Anomaly Detection in High-Frequency Markets

A new anomaly detection framework for stock trading data uses a modified Transformer autoencoder to spot fraudulent time series.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4502662
- Identifier: SSRN 4502662
- Released: 2023-07-06
- First featured: Quant Letter No. 7 (2023-07-12): https://www.ml-quant.com/issues/2023-07-12/
- Citations (Semantic Scholar): 0
- Published in: not yet
- Topic: Trading, Microstructure & Execution

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