Quantitative model of multilevel economic network synergy in corporate mergers and acquisitions based on deep learning

Abstract

This study explores synergies in multilevel economic networks within corporate mergers and acquisitions, introducing an innovative quantitative model. With the evolving global economy, mergers and acquisitions present challenges tied to synergies and economic networks. In contrast to conventional approaches, our focus is on establishing corporate economic networks based on complex network theory. Through a comprehensive exploration of synergies and the implementation of machine learning and deep learning methods, we propose the multi-DAG model, seamlessly integrating multilevel network structures with directed acyclic graph neural networks. Experimental results conclusively demonstrate Multi-DAG-NN’s superior performance in predicting synergy values. Furthermore, this study contributes by innovatively integrating multilevel network structures and neural network models, offering a practical quantitative methodology to study the synergetic effect of corporate mergers and acquisitions.