MSc.Thesis Defense:Ayşegül Rana Erdemli

QUANTIFYING EFFECTS OF COMPANY MERGERS AND

ACQUISITIONS ON ONLINE SOCIAL NETWORKS

Ayşegül Rana Erdemli
Computer Science and Enginering, MSc. Thesis, 2024

Thesis Jury

Asst. Prof. Onur Varol (Thesis Advisor), Prof. Şerif Aziz Şimşir,

Assoc. Prof. Arhan S. Ertan

Date & Time: December 16th, 2024 –  13 PM

Place: FASS 1050

Keywords : social media, finance, mergers and acquisitions, causal inference, social

networks

Abstract

Mergers and Acquisitions (M&As), as transformative corporate events, provide companies opportunities to enhance operational efficiency, achieve growth, and strengthen their competitive edge, while affecting a broad range of stakeholders, including employees, executives, shareholders and investors. Considering its reach and influence, social media serves as a powerful tool for investigating events of importance. While previous research utilized social media data in various financial settings, there remains a significant gap in understanding how M&As resonate on the social media accounts of acquirer and target companies and their executives. This study bridges this gap by combining extensive datasets from Thomson Reuters, X (formerly Twitter), and Crunchbase to analyze the impact of M&A events. Employing a Difference-in-Differences methodology, we examine post-announcement activity and engagement on the X accounts of companies and executives. Our findings reveal a significant and measurable influence of M&As, reflected in follower counts, statuses, and engagement metrics of treatment and control groups. Notably, company accounts are more affected than executive accounts. Additionally, target companies and executives experience a pronounced increase in followers and engagement compared to acquirers, while acquirer companies show a significant upward trend in statuses following the event. By uncovering the distinct impacts of M&As on X accounts of key stakeholders—companies and executives—this study offers comparative insights into the dynamics of acquirer and target groups. Moreover, it highlights the effectiveness of causal inference methods, such as the Difference-in-Differences approach, for analyzing the impact of significant events on social media data.