AI-Driven M&A Target Selection and Synergy Prediction: A Machine Learning-Based Approach

Abstract

This study presents an innovative AI-based approach to M&A target selection and synergy prediction using a hybrid machine learning model combining gradient boosting, support vector machines, and neural networks. The model aims to identify acquisition targets with high potential for achieving synergistic benefits. Utilizing a comprehensive dataset of 10,000 M&A deals from 2010 to 2023, the model demonstrates superior predictive performance in identifying successful synergistic combinations compared to traditional target selection methods. With AUC-ROC of 0.937 and AUC-PR of 0.912, the proposed model significantly outperforms conventional techniques. Feature importance analysis reveals critical factors influencing successful combinations, including Revenue Growth Rate, Market Cap / EBITDA ratio, and Debt to Equity Ratio. The inclusion of text-based features improves the model's ability to capture qualitative aspects of potential target compatibility. Case studies demonstrate the model's effectiveness in identifying promising acquisition targets, showing a 47% higher success rate in post-merger integration compared to traditional methods.