Using network science and machine learning, we studied how companies move within the technology space during mergers and acquisitions. We validated this methodology by predicting which pairs of companies will enter into an agreement and, given an acquirer, which company will be its target. Our approach can be used to decide which company to invest in based on their respective technological activities.
Using our technology, companies can find the best partners for a deal (merger, acquisition, etc.). This recommendation stems from the construction of a space of companies, whose relative distance is computed using network science and machine learning. In this space, two companies are "close" from a technological point of view, so if the two have similar patenting activities. However, a target firm could be recommended to an acquiring company because of its distance, if the acquirer is looking for a complementary patenting activity, or wants to enter a new market.