Interpretable Machine Learning Models for Predicting the Next Targets of Activist Funds

TL;DR

This paper develops an interpretable machine learning model to predict potential targets of activist funds using data from 2016-2022, achieving an AUC-ROC of 0.782 and identifying key influencing factors.

Contribution

It introduces a novel predictive framework combining diverse ML techniques and interpretability methods to forecast activist fund targets, enhancing strategic decision-making.

Findings

Best model achieved AUC-ROC of 0.782

Shapley values identified key target factors

Model effectively predicts activist targets

Abstract

This research presents a predictive model to identify potential targets of activist investment funds--entities that acquire significant corporate stakes to influence strategic and operational decisions, ultimately enhancing shareholder value. Predicting such targets is crucial for companies aiming to mitigate intervention risks, activist funds seeking optimal investments, and investors looking to leverage potential stock price gains. Using data from the Russell 3000 index from 2016 to 2022, we evaluated 123 model configurations incorporating diverse imputation, oversampling, and machine learning techniques. Our best model achieved an AUC-ROC of 0.782, demonstrating its capability to effectively predict activist fund targets. To enhance interpretability, we employed the Shapley value method to identify key factors influencing a company's likelihood of being targeted, highlighting the dynamic mechanisms underlying activist fund target selection. These insights offer a powerful tool for proactive corporate governance and informed investment strategies, advancing understanding of the mechanisms driving activist investment decisions.