The Application of Data Analytics for Understanding Patterns of Mergers and Acquisitions and CEO Characteristics in and between Crisis Times

We examine patterns and dynamics of M&A occurrence with attention to three crises or instances of economic disruption—the dot.com bubble, the global financial crisis, and, for future evaluation, the covid-19 global pandemic—while also taking potential CEO demographic (e.g., age) and outcome (e.g., compensation) factors into consideration. Specifically, we examine the frequency, size, and characteristics of M&A transactions across different industries, and we analyze the age, gender, education, experience, and compensation of CEOs across various industries. Drawing on the SDC Platinum and BoardEx databases, we examine US M&A valued over 250 million USD in the 11 global industry classification standard (GICS) sectors from 1999–2018 for n = 14,405 M&A and n = 20,745 CEOs. Using clustering analysis, we define groupings of patterns of M&A transactions before and after the global financial crisis (circa 2008–2009) to show that M&A decreased and then resumed across sectors, although to different degrees for each sector. Using both clustering and PCA, we determine groupings of CEO characteristics before and after the global financial crisis, demonstrating variation in selected demographics of individuals leading organizations before and after that crisis. In addition, initial analysis of our CEO-level data indicates merging to be a predominantly male CEO-driven activity with inferred egoistic, reputational, and power rewards as well as longer-term wealth-building effects, as direct non-contingent compensation does not markedly change for CEOs in the aggregate before and after M&A.