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Analyzing Mergers and Acquisitions (M&A) in Japan Using AI methods
The result of this research shows feasibility of recommending M&A from big data by providing a clustering method with cash flow features and company relationship features, and analyzing the relationships between features from financial items.
Abstract
If potential M&A cases can be detected automatically, this technology will improve the efficiency of M&A target recommendation and effectiveness of in-process M&A cases. However, in the past, M&A recommendation was impossible due to insufficient data and complexity of M&A. In this research, we provided a clustering method with cash flow features and company relationship features. From M&A clustering, we observed that M&A tend to concentrate in specific clusters. In order to improve the precision of M&A recommendation, we also analyzed the relationships between features from financial items and we extracted important features for identifying company relationships. The result of this research shows feasibility of recommending M&A from big data. In the future, we will design and select more features for analyzing M&A and we will associate results from AI with Management Science.