We investigate the impact of mergers on R&D incentives within a framework of R&D competition where effort can influence both the probability of innovation and the payoff conditional on success. Our framework nests the results of two classes of existing models and reveals assumptions that are...

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...