Detecting and Explaining Unlawful Insider Trading: A Shapley Value and Causal Forest Approach to Identifying Key Drivers and Causal Relationships

TL;DR

This paper presents a novel framework combining Shapley Values and Causal Forests to accurately detect unlawful insider trading and identify key causal features, emphasizing the importance of causality and high-dimensional analysis.

Contribution

It introduces an integrated architecture that leverages SHAP and CF for high-accuracy classification and causal explanation of insider trading, advancing interpretability in complex financial data.

Findings

Significant relationships between insider trading and features like director status and market beta.

Causal analysis reveals nuanced impacts of valuation and volatility on unlawful trades.

The framework effectively uncovers latent structures in high-dimensional financial data.

Abstract

Corporate insiders trade for diverse reasons, often possessing Material Non-Public Information (MNPI). Determining whether specific trades leverage MNPI is a significant challenge due to inherent complexity. This study focuses on two critical objectives: accurately detecting Unlawful Insider Trading (UIT) and identifying key features explaining classification. The analysis demonstrates how combining Shapley Values (SHAP) and Causal Forest (CF) reveals these explanatory drivers. The findings underscore the necessity of causality in identifying and interpreting UIT, requiring the consideration of alternative scenarios and potential outcomes. Within a high-dimensional feature space, the proposed architecture integrates state-of-the-art techniques to achieve high classification accuracy. The framework provides robust feature rankings via SHAP and causal significance assessments through CF, facilitating the discovery of unique causal relationships. Statistically significant relationships are documented between the outcome and several key features, including director status, price-to-book ratio, return, and market beta. These features significantly influence the likelihood of UIT, suggesting potential links between insider behavior and factors such as information asymmetry, valuation risk, market volatility, and stock performance. The analysis draws attention to the complexities of financial causality, noting that while initial descriptors offer intuitive insights, deeper examination is required to understand nuanced impacts. These findings reaffirm the architectural flexibility of decision tree models. By incorporating heterogeneity during tree construction, these models effectively uncover latent structures within trade, finance, and governance data, characterizing fraudulent behavior while maintaining reliable results.