Abstract
Using large-scale patent data, we analyze the impact of mergers and acquisitions (M&A) on innovation activities in companies. The analysis classifies M&A according to the trend of innovation output through their index clustering. In addition, we measure the technological distances among companies using patent document data, then apply the distances to the evaluation of M&A. As a result, we find two types of clusters: 1) one with the tendency to increase the postmerger innovation outputs; 2) the other with a trend to decrease innovation output around M&A deals. The former cluster tends to have a smaller innovation output scale around deals than the other clusters. Furthermore, compared to the other groups, the group whose postmerger innovation output decreases has the largest increase in stock price return after the deal announcement. This study’s major findings indicate that the combination of unstructured text data and machine learning methods applies to M&A and innovation.
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Notes
- 1.
Some of the patents in the data used include patents that the applicant does not yet license.
- 2.
The standard code is assigned to the subsidiaries as well.
- 3.
If both the closing price of the company name before and after the change exists in the database, we use the new company name to conduct the analysis.
- 4.
Among the analyses using other numbers of clusters, we describe the results when the number of clusters is set to 3 for interpretability reasons.
- 5.
In the case of the Komatsu Electronic Metals Co Ltd acquisition by SUMCO Corp announced in 2006, SUMCO Corp became public in 2005, and it was impossible to obtain the closing price data from Nikkei NEEDS from ayr-3 to ayr-1 before the deal announcement.
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Tamagawa, N., Takahashi, H. (2023). Empirical Analysis of the Impact of Mergers and Acquisitions on Innovation Activities Through Clustering. In: Yada, K., Takama, Y., Mineshima, K., Satoh, K. (eds) New Frontiers in Artificial Intelligence. JSAI-isAI 2021. Lecture Notes in Computer Science(), vol 13856. Springer, Cham. https://doi.org/10.1007/978-3-031-36190-6_25
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