Classifying the industrial organization of corporate mergers and testing theories: the Human Eye versus machine readable methods

I examine wealth effects of merger announcements using a sample of 223 (U.S.) domestic mergers during a 3 year period (2011 through 2013) after the Great Recession. Specifically, I partition the mergers in my sample into their horizontal, vertical, and conglomerate industrial organization types using a document-based, Human Eye method of classification and then calculate the equity-wealth effects for each merger type. I also perform similar event study analysis for the rivals of my sample of merging firms. Overall, my results provide evidence that recent corporate diversification activity via mergers has not been value-destroying and that the synergy and collusion hypotheses cannot fully explain merger returns for my sample. A comparison of results achieved under two different methods of classifying merger industrial organization also reveals evidence suggesting that significant differences exist between document-based methods of industrial organization classification and the popular SIC/IO method that is based on fixed industry codes.