LAUSR

Abstract Mergers and acquisitions (M&A) have occurred among tens of thousands of companies. Categorization of M&A is important to both corporate strategy and academic research. Previous research largely uses case studies and econometric data analysis to classify the motivations and types of M&A. Here, we propose understanding M&A using large-scale data to generate more applicable and generalized results. We use transaction relationships from transaction networks to better understand M&A. Based on detailed pre-analysis, including matching M&A and transaction data from Japan and clustering of transaction networks, we select several M&A observation perspectives. We use two features of transaction networks to categorize M&A cases: betweenness centrality and shortest path length. Betweenness centrality provides a view of the overall business situation from a macro perspective, and shortest path length helps to understand neighboring business environments from a micro perspective. We find several meaningful areas of concentration based on their betweenness centrality values and shortest path lengths. Finally, we re-examine M&A cases in each area, summarizing the trends identified using this categorization method. This study contributes to the M&A literature because it advances quantitative categorization of M&A cases.