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

## TL;DR

This paper introduces MASS, an interpretable algorithm that uses patent data to predict M&A deals and outperforms complex models in many cases.

## Contribution

MASS is a novel, interpretable algorithm for M&A prediction based on a simplified machine learning approach and patent similarity.

## Key findings

- MASS outperforms graph convolutional networks in predicting M&A deals for companies with overlapping patenting activities.
- Graph convolutional networks perform better when companies have disjoint patenting activities.
- MASS provides a simple yet powerful tool for modeling M&A deals using patent data.

## 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, which adapts a patent-based measure of similarity between companies 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 a more “black box” graph convolutional network algorithm. The latter, however, turns out to be the most effective algorithm when considering companies with disjoint patenting activities. This study provides a simple and powerful tool to model and predict M&A deals between companies active in patenting, offering valuable insights to managers and practitioners for informed decision-making.