用于预测激进基金下一个目标的可解释机器学习模型
Interpretable Machine Learning Models for Predicting the Next Targets of Activist Funds
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中文总结 AI 辅助
本研究提出一种可解释的机器学习预测模型，基于2016-2022年Russell 3000指数数据评估了123种配置，最佳模型AUC-ROC达0.782，并利用Shapley value方法揭示了影响激进基金目标选择的关键因素，为公司治理和投资策略提供工具。
中文摘要 AI 辅助
本研究提出了一个预测模型，用于识别激进投资基金的潜在目标——这些实体通过收购大量公司股份来影响战略和运营决策，最终提升股东价值。预测此类目标对于旨在降低干预风险的公司、寻求最佳投资的激进基金以及希望利用潜在股价上涨的投资者至关重要。使用2016年至2022年Russell 3000指数的数据，我们评估了123种模型配置，结合了多种插补、过采样和机器学习技术。我们的最佳模型达到了0.782的AUC-ROC，证明了其有效预测激进基金目标的能力。为了增强可解释性，我们采用Shapley value方法识别了影响公司成为目标可能性的关键因素，突显了激进基金目标选择的动态机制。这些见解为主动的公司治理和明智的投资策略提供了强大工具，推进了对驱动激进投资决策机制的理解。
英文摘要
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.
