Predict industry merger waves utilizing supply network information.

Predicting merger waves has been a classical yet challenging problem. In this paper, we propose approaches to predict industry merger waves relying on an integrated dataset including financial statements and supply data, as well as more than 60 thousand firm-level mergers and acquisitions records. We utilize 1000-dimension features—including common-used industry characteristics and novel supply network information—for predictions and train classifiers based on different machine learning methods. The experiments demonstrate the usefulness of our prediction approach, as the predicting precision reaches 91% on acquirers and 96% on targets. By further analysis, some patterns are well explained by financial theories, such as the well-known Tobin's Q measurement. Especially, new influential factors on merger waves are revealed by the empirical analysis on micro-structure network features. To the best of our knowledge, this paper is one of the first attempts to explore merger waves prediction, and our approaches and findings introduce a new viewpoint for this field. [ABSTRACT FROM AUTHOR]