On modeling acquirer delisting post-merger using machine learning techniques

Ephraim Kwashie Thompson, Changki Kim and So-Yeun Kim

Journal of Management Analytics, 2024, vol. 11, issue 2, 247-275

Abstract: We test the comparative ability of representative machine-learning algorithms – Logistic Regression, Random Forest Classifier, Adaboost Classifier and Multi-Layer Perceptron Classifier – to predict the likelihood that an acquirer will be forcibly delisted for performance reasons after the close of a deal. We find that the Multi-Layer Perceptron Classifier, Adaboost and Random Forest have similar performance in terms of performance but the Logistic Regression is the poorest performing among the models we study. For feature importance, the results suggest that firm size, leverage, and profitability are the most informative features for the models in predicting the likelihood of performance-induced delisting. Deal-related characteristics and agency problems do not drive performance-induced involuntary delisting of acquirers. The results taken together suggest that acquirers delisted within five years post-merger for performance-induced reasons were already poor-performing firms pre-merger, their state likely worsened by undertaking a merger they were originally not supposed to undertake.

Date: 2024
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DOI: 10.1080/23270012.2024.2348475

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