Prediction and visualization of Mergers and Acquisitions using Economic Complexity
Lorenzo Arsini, Matteo Straccamore, Andrea Zaccaria
PLOS ONE, April 3, 2023
Mergers and Acquisitions are popular forms of business deals. Aside from involving large volumes of money, they also play a role in the innovation activity of companies. Nevertheless, statistical evidence is lacking for their actual economic impact on the affected companies.
Researchers Lorenzo Arsini (Sapienza, Roma), Matteo Straccamore, and Andrea Zaccaria applied Economic Complexity methods to the field.
By considering the patent activity of about one thousand companies, they developed a method to predict future acquisitions by assuming that companies deal more frequently with technologically related ones. They addressed two questions: predicting the best pair of companies for a future deal and finding a target company given an acquirer. They compared 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.
Eventually, they presented 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 explore possible innovation strategies.