Mergers and Acquisitions in the Data Economy

43 Pages Posted: 18 Sep 2020 Last revised: 8 Oct 2020

See all articles by Daniela Stephanie Schoch

Daniela Stephanie Schoch

EMLYON Business School; Ludwig Maximilian University of Munich (LMU)

Date Written: August 28, 2020


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.

Keywords: mergers and acquisitions, data intensive firms, textual analysis, antitrust

JEL Classification: G34, G38, L40, L80

Suggested Citation

Schoch, Daniela Stephanie, Mergers and Acquisitions in the Data Economy (August 28, 2020). Available at SSRN: or

Daniela Stephanie Schoch (Contact Author)

EMLYON Business School ( email )

23 Avenue Guy de Collongue
Ecully, 69132

Ludwig Maximilian University of Munich (LMU) ( email )

Geschwister-Scholl-Platz 1
Munich, DE Bavaria 80539

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