Needles in a haystack: using forensic network science to uncover insider trading

TL;DR

This paper introduces a network science approach to detect insider trading by analyzing trading patterns of corporate insiders over a decade, identifying suspicious clusters and behaviors indicative of illicit activity.

Contribution

It presents a novel data-driven network method that leverages temporal trading similarities to uncover insider trading, addressing the challenge of limited labeled data.

Findings

Effective detection of insider trading clusters

Validation against null models confirms approach's robustness

Identifies suspicious insider groups with potential market manipulation

Abstract

Although the automation and digitisation of anti-financial crime investigation has made significant progress in recent years, detecting insider trading remains a unique challenge, partly due to the limited availability of labelled data. To address this challenge, we propose using a data-driven networks approach that flags groups of corporate insiders who report coordinated transactions that are indicative of insider trading. Specifically, we leverage data on 2.9 million trades reported to the U.S. Securities and Exchange Commission (SEC) by company insiders (C-suite executives, board members and major shareholders) between 2014 and 2024. Our proposed algorithm constructs weighted edges between insiders based on the temporal similarity of their trades over the 10-year timeframe. Within this network we then uncover trends that indicate insider trading by focusing on central nodes and anomalous subgraphs. To highlight the validity of our approach we evaluate our findings with reference to two null models, generated by running our algorithm on synthetic empirically calibrated and shuffled datasets. The results indicate that our approach can be used to detect pairs or clusters of insiders whose behaviour suggests insider trading and/or market manipulation.