Topological Clustering of Agents in Hidden Information Contagions: Application to Financial Markets

This study proposes a strategy based on the Mapper algorithm, which utilizes topological data analysis to identify symptomatic agents in contagions by leveraging expert knowledge. The context of our paper is financial markets, where insiders may share private information through social links, and other agents may exhibit positive symptoms by opportunistically trading on this information. We verify and demonstrate our methods using both synthetic and empirical data on insider networks and stock market transactions. 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. The mapper-based method systematically outperforms other methods on synthetic data with ground truth. We also apply the method to empirical data and verify the results using a statistical validation method based on persistence homology. Our findings highlight that the proposed mapper-based technique successfully identifies a subpopulation of opportunistic agents within the information cascades. The adaptability of this method to diverse data types and sizes is demonstrated, with potential for tailoring for specific applications.