Leveraging Machine Learning for Predictive Analytics in Mergers and Acquisitions: Valuation, Risk Assessment, and Post-Merger Performance

Abstract

This study investigates machine learning (ML) applications to enhance predictive accuracy across three critical M&A dimensions: valuation, risk assessment, and post-merger performance. Using 8,347 U.S. transactions from 2005–2022, we compare Random Forest, XGBoost, Neural Networks, and Support Vector Machines against traditional regression methods. XGBoost achieves 62% higher R² than OLS for premium prediction (0.676 vs. 0.415), 87.2% accuracy for deal completion (vs. 73.1% for logistic regression), and substantially outperforms analyst estimates for post-merger returns. SHAP value analysis reveals that deal structure features relative size, payment method, tender offers dominate traditional financial metrics. Trading strategies based on ML predictions generate 11.8% annual returns with Sharpe ratio 0.825, demonstrating economic significance. Our findings show that ML captures non-linear relationships invisible to traditional models, providing actionable insights for practitioners while advancing computational corporate finance theory.