Towards a better measure of business proximity: topic modeling for analyzing M As

In this article, we propose a new measure of firms' dyadic business proximity. Specifically, we analyze the unstructured texts that describe firms' businesses using the natural language processing technique of topic modeling, and develop a novel business proximity measure based on the output. When compared with the existent methods, our approach provides finer granularity on quantifying firms' similarity in the spaces of product, market, and technology. We then show our measure's effectiveness through an empirical analysis using a unique dataset of recent mergers and acquisitions in the U.S. high technology industry. Building upon the literature, our model relates the likelihood of matching of two firms in a merger or acquisition transaction to their business proximity and other characteristics. We particularly employ a class of statistical network analysis methods called exponential random graph models to accommodate the relational nature of the data.