2022 年 2022 巻 BI-020 号 p. 07-
We conduct an empirical analysis of the relationship between innovation activity and mergers and acquisitions (M&A) by public companies in the pharmaceutical and materials industries in Japan, the U.S., and Germany. The analysis uses large-scale patent data to measure indicators of innovation activity. In addition, we attempt to quantitatively measure the technological distance between firms by analyzing patent document data using natural language processing, and apply it to the evaluation of M&A. As a result, we find a trend in the relationship between the technological distance of the acquirer and the target firm and the post-merger innovation activity. One of the novelties of this study is the application of unstructured data and information technology methods to the research field of M&A and innovation activities. This research contributes to the quantitative evaluation of intangible assets in companies.