Mergers and Acquisitions in the Data Economy

Abstract

In research, public, and policy debate, there is increasing interest in data intensive firms like Google, Facebook, and Amazon. As business models of data firms are often characterized by high scale economies and network externalities, they are expected to have a particularly large incentive to grow, among others through mergers and acqui- sitions (M&A). Adding to the up to now mainly theoretical or anecdotal discussion on data intensive firms, this study empirically analyzes the relationship between firms’ data intensity and M&A activity. Using text-based measures to identify data inten- sive firms, I find that data generators are more likely to become acquirers, whereas data analysis and storage companies are more likely to become targets. Transactions by data firms, on average, do not create value as abnormal announcement returns are zero. There is evidence for pre-emptive merger activity, i.e., data intensive firms acquiring particularly often rather small, non-public companies that are not (yet) on the radar of competition authorities.