Topological Clustering of Agents in Hidden Information Contagions: Application to Financial Markets
Anubha Goel, Henri Hansen, Juho Kanniainen

TL;DR
This paper presents a Mapper-based topological clustering method to identify agents in financial markets influenced by insider information, effectively reducing false positives and verified through synthetic and real data.
Contribution
It introduces a novel topological data analysis approach for clustering agents based on information influence, tailored for financial market surveillance.
Findings
Outperforms existing methods on synthetic data with known ground truth.
Successfully identifies agents exploiting insider information in empirical data.
Verifies results using persistence homology for statistical validation.
Abstract
Building on topological data analysis and expert knowledge, this study introduces a Mapper-based approach to cluster agents based on their tendency to be influenced by information spread. The context of our paper is financial markets with an aim to identify agents trading opportunistically on insider information while minimizing false positives, a critical challenge in financial market surveillance. We verify and demonstrate our methods using both synthetic and empirical data on insider networks and investor-level transactions in a stock market. Recognizing the sensitive nature of insider trading cases, we design a conservative approach to minimize false positives, ensuring that innocent agents are not wrongfully implicated. We find that the mapper-based method systematically outperforms other methods on synthetic data with ground truth. We also apply the method to empirical data and…
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Taxonomy
TopicsComplex Systems and Time Series Analysis
