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

Giambattista Albora; Matteo Straccamore; Andrea Zaccaria

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arXiv · 2404.07179

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

Giambattista Albora·Matteo Straccamore·Andrea Zaccaria

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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.

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Giambattista Albora, Matteo Straccamore, Andrea Zaccaria. 2024-04-10. Machine learning-based similarity measure to forecast M&A from patent data. https://arxiv.org/abs/2404.07179

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