Are acquirer stock price reactions to M&A announcements in any way predictable? A Machine-Learning analysis.

We examine whether acquirer stock price reactions to M&A deal announcements can be forecasted based on ex ante acquirer, target, deal, and macroeconomic characteristics. We employ machine learning methodologies with out-of-sample testing and standard cross-validation procedures to assess the forecasting accuracy of various parametric and nonparametric models. While overall predictability is low, nonparametric models exhibit some ability to forecast acquirer stock price reactions to M&A announcements, whereas parametric models do not. Feature importance analyses reveal that a handful of predictors, including acquirer size and (relative) deal size, contribute most to the predictions. Our findings have practical implications for corporate managers and various corporate stakeholders.