Prediction and visualization of Mergers and Acquisitions using Economic Complexity

Area of Science:

• Business and Economics
• Innovation Studies
• Data Science

Background:

• Mergers and Acquisitions (M&A) are significant business transactions impacting company innovation.
• Economic Complexity methods have not been extensively applied to M&A studies.
• Understanding M&A drivers is crucial for strategic business decisions and policy making.

Purpose of the Study:

• To develop and evaluate a novel method for predicting future M&A activities using company patent data.
• To identify technologically related companies likely to engage in M&A.
• To provide tools for companies and policymakers to strategize M&A and innovation.

Main Methods:

• Analysis of patent activity for approximately one thousand companies.
• Development of a predictive model based on the assumption of frequent deals between technologically related firms.
• Comparison of machine learning and network-based forecasting algorithms.
• Introduction of the Continuous Company Space for visualizing technological proximity.

Main Results:

• A simple angular distance metric, augmented with industry sector data, demonstrated superior performance in predicting M&A compared to other tested methodologies.
• The developed method can predict pairs of companies likely to engage in future deals.
• The approach can identify potential target companies for a given acquirer.

Conclusions:

• Technological relatedness is a key predictor of M&A activity.
• The Continuous Company Space offers a valuable visualization tool for strategic M&A and innovation planning.
• This approach provides actionable insights for corporate strategy and economic policy.