  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.
