Prediction and visualization of Mergers and Acquisitions using Economic Complexity

TL;DR

This paper introduces a novel approach using Economic Complexity and patent data to predict mergers and acquisitions, providing a visualization tool for technological proximity and strategic decision-making.

Contribution

It applies Economic Complexity methods to M&A prediction, developing a new visualization called Continuous Company Space for analyzing technological relationships.

Findings

Angular distance with industry info outperforms other models

The method predicts future M&A pairs effectively

Continuous Company Space visualizes firms' technological proximity

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 sector information outperforms the other approaches. Finally, we present the Continuous Company Space, a two-dimensional representation of firms to visualize their technological proximity and possible deals. Companies and policymakers can use this approach to identify companies most likely to pursue deals or to explore possible innovation strategies.