# A Clustering-Based Framework for Identifying Suspicious Trading Patterns in Capital Market

arXiv: https://arxiv.org/abs/2607.04184 · HTML: https://mlsanity.com/paper/2607.04184/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 5 Jul 2026.

## Builds on (most cited first, 2 of 2)

- [Measuring tail risk at high-frequency: An L1-regularized extreme value regression approach with unit-root predictors](https://mlsanity.com/paper/2301.01362/) (2023)
- [High-Frequency Market Manipulation Detection with a Markov-modulated Hawkes process](https://mlsanity.com/paper/2502.04027/) (2025)

## Abstract (arXiv)

Market manipulation is the dubious practice of manipulating stock prices in order to make a quick profit, which truly degrades confidence on trading platforms. We implemented an unsupervised fraud-detection toolkit that begins with K-Means++ clustering to address this issue. A dataset of roughly one million financial transactions from 2012 to 2024 is used. In order to identify fraudulent trades and categorize them using market practice heuristic thresholds, the study suggests a clustering-based pipeline. The method highlights 2.02% of trades as suspicious where 51.10% clearly indicate spoofing, 0.10% indicate pump and dump, 0.55% indicate insider trading, 1.43% indicate a fake breakout, and 46.83% are unclassified. Despite the lack of ground truth, the model's performance is confirmed by a Silhouette Score of 0.561.