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

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 verify the results using a statistical validation method based on persistence homology. Our findings indicate that the proposed Mapper-based technique effectively identifies a subset of agents who tend to take advantage of inside information they have received. This method is highly adaptable to various applications involving the spread of information or diseases, where agents exhibit only indirect evidence of their carrier status (symptoms) through their behavior.