Prediction and visualization of Mergers and Acquisitions using Economic Complexity.

ABSTRACT: Mergers and Acquisitions represent important forms of business deals, both because of the volumes involved in the transactions and because of the role of the innovation activity of companies. Nevertheless, Economic Complexity methods have not been applied to the study of this field. By considering the patent activity of about one thousand companies, we develop a method to predict future acquisitions by assuming that companies deal more frequently with technologically related ones. We address both the problem of predicting a pair of companies for a future deal and that of finding a target company given an acquirer. We compare different forecasting methodologies, including machine learning and network-based algorithms, showing that a simple angular distance with the addition of the industry

SUBMITTER: Arsini L 

PROVIDER: S-EPMC10069768 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature