What Drives Mergers and Acquisitions? Empirical Evidence from the U.S. Software Industry

Abstract

This thesis investigates whether and how micro- and macroeconomic factors explain mergers and acquisitions (M&A) activity in the U.S. software industry from 1991–2019. This focus is motivated by the rising dominance of the industry combined with the limited attention in empirical research. Following an ample examination, the micro- and macroeconomic elements are disentangled to encompass eight theories within both neoclassical and behavioral economics: (1) efficiency, (2) market power, (3) industry shock, (4) economic prosperity, (5) agency, (6) misvaluation, (7) managerial hubris, and (8) herding. Based on 2,314 transactions, the thesis examines M&A activity in terms of deal value and volume. Through ARIMA and MS-AR modelling, the research investigates the structure of the observed M&A activity and confirms that, similar to general transaction activity, deals within the U.S. software industry occur in patterns, also referred to as merger waves. Here, 9 and 14 shifts were identified for deal value and volume, respectively. Building on this, the determinants of the individual high activity periods were analyzed in a logistic regression analysis, suggesting the importance of firm-specific, industry-specific, and macroeconomic factors in explaining merger wave patterns. Multivariate regression analyses were then applied to scrutinize the drivers of the general M&A activity. Here, the results indicated support for the efficiency, market power, economic prosperity, agency, misvaluation, and herding theories, with insignificant support for the industry shock and managerial hubris theories. Overall, the neoclassical paradigm proved to be a more consistent and effective prognosticator compared to behavioral economics. This empirical research conclusively determines that both micro- and macroeconomic factors are significant in explaining the observed M&A activity within the U.S. software industry, ultimately offering implications relevant for decision-makers at three levels: governmental, firm, and investor.