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.

数以万计的公司之间发生了并购（M＆A）。并购的分类对于公司战略和学术研究都非常重要。先前的研究主要使用案例研究和计量经济数据分析对并购的动机和类型进行分类。在这里，我们建议使用大规模数据来理解并购，以产生更适用和更广义的结果。我们使用来自交易网络的交易关系来更好地了解并购。基于详细的预分析，包括匹配日本的并购和交易数据以及交易网络的聚类，我们选择了几种并购观察视角。我们使用交易网络的两个功能对并购案例进行分类：中间性和最短路径长度。中间性中心点提供了从宏观角度看整体业务情况的视图，而最短路径长度则有助于从微观角度了解相邻的业务环境。根据它们之间的中间性中心值和最短路径长度，我们发现了几个有意义的集中区域。最后，我们重新检查每个领域的并购案例，总结使用这种分类方法确定的趋势。这项研究为并购文献做出了贡献，因为它促进了并购案例的定量分类。我们重新检查了每个领域的并购案例，总结了使用这种分类方法确定的趋势。这项研究为并购文献做出了贡献，因为它促进了并购案例的定量分类。我们重新检查了每个领域的并购案例，总结了使用这种分类方法确定的趋势。这项研究为并购文献做出了贡献，因为它促进了并购案例的定量分类。