Merger and acquisition prediction based on deep learning with attention mechanism

This study proposes a novel attention-based deep neural network (AttDNN) model specifically designed for predicting mergers and acquisitions (M&A). The model extends existing deep learning frameworks by incorporating M&A-specific features, regularization layers, and an attention mechanism that emulates human cognitive processes to structure M&A drivers and improve predictive performance. Empirical results demonstrate that the AttDNN model significantly outperforms traditional algorithms in forecasting M&A outcomes, achieving a 29.2 % improvement in predictive accuracy over conventional deep learning methods. This study provides valuable insights at the intersection of artificial intelligence and financial economics, offering practical implications for financial strategy and corporate M&A decision-making.