A Clustering-Based Framework for Identifying Suspicious Trading Patterns in Capital Market
Abstract
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.
Cite
@article{arxiv.2607.04184,
title = {A Clustering-Based Framework for Identifying Suspicious Trading Patterns in Capital Market},
author = {Asif Zaman and Romona Magdalene Sarkar and Sabiha Khair Ohi and Iftekharul Mobin},
journal= {arXiv preprint arXiv:2607.04184},
year = {2026}
}
Comments
Accepted for publication in IEEE 2nd International Conference on Quantum Photonics, Artificial Intelligence & Networking (QPAIN), 2026. This is the authors preprint version. The final version is available via IEEE Xplore