Categorization of Mergers and Acquisitions in Japan Using Corporate Databases: A Fundamental Research for Prediction

Abstract

Mergers and Acquisitions (M&A) are recognized important strategy for corporate growth. In practice, M&A business consumes much energy and M&A success rate is not high. Hence, scientific M&A recommendation research is needed under such condition. This paper, focusing on M&A categorization, is a fundamental research for M&A prediction and recommendation. In this paper, we used M&A data, financial data and corporate data for M&A analysis. Based on them, we designed 13 features and used K-means clustering to separate M&A cases. The 13 features are of acquirer features, target features and their relationship features. We grouped M&A cases into 5 clusters and found different characteristics in these 5 clusters. Results in this paper show that these features will be effective for future M&A prediction and recommendation.