Machine learning-based similarity measure to forecast M&A from patent data

TL;DR

This paper introduces the MASS algorithm, a simple, interpretable similarity measure based on machine learning principles, applied to patent data to forecast mergers and acquisitions, outperforming complex models in certain scenarios.

Contribution

The paper presents the MASS algorithm, a novel, interpretable similarity measure for predicting M&A deals from patent data, outperforming complex graph neural networks in most cases.

Findings

MASS outperforms LightGCN in predicting M&A from patent data.

LightGCN is more effective when companies have disjoint patenting activities.

MASS provides a simple, interpretable tool for M&A prediction.

Abstract

Defining and finalizing Mergers and Acquisitions (M&A) requires complex human skills, which makes it very hard to automatically find the best partner or predict which firms will make a deal. In this work, we propose the MASS algorithm, a specifically designed measure of similarity between companies and we apply it to patenting activity data to forecast M&A deals. MASS is based on an extreme simplification of tree-based machine learning algorithms and naturally incorporates intuitive criteria for deals; as such, it is fully interpretable and explainable. By applying MASS to the Zephyr and Crunchbase datasets, we show that it outperforms LightGCN, a "black box" graph convolutional network algorithm. When similar companies have disjoint patenting activities, on the contrary, LightGCN turns out to be the most effective algorithm. This study provides a simple and powerful tool to model and predict M&A deals, offering valuable insights to managers and practitioners for informed decision-making.