The Japanese Society for Artificial Intelligence

Mergers and acquisitions (M&A) are one of the most important management strategies. However, the execution of M&A does not always lead to post-acquisition success, and it is difficult for even experts to realize such success. Therefore, a research on the success and recommendation of M&A is needed. In this study, we proposed an index to evaluate M&A quantitatively from two perspectives, and showed the possibility to recommend M&A by analyzing the trend of the index. First, the expected synergy realization was evaluated using the existence of goodwill impairment, and a significant difference was found for the capital adequacy ratio. Next, as for the synergy of name recognition, the index was calculated by capturing the change in Google Trends before and after the acquisition, and the difference in the name recognition of the pair before the acquisition was shown to be a possible explanatory variable.