Paper: arXiv 2307.11571

Authors: Maxime L. D. Nicolas, Adrien Desroziers, Fabio Caccioli, Tomaso Aste

Abstract

We investigate the response of shareholders to Environmental, Social, and Governance-related reputational risk (ESG-risk), focusing exclusively on the impact of social media. Using a dataset of 114 million tweets about firms listed on the S&P100 index between 2016 and 2022, we extract conversations discussing ESG matters. In an event study design, we define events as unusual spikes in message posting activity linked to ESG-risk, and we then examine the corresponding changes in the returns of related assets. By focusing on social media, we gain insight into public opinion and investor sentiment, an aspect not captured through ESG controversies news alone. To the best of our knowledge, our approach is the first to distinctly separate the reputational impact on social media from the physical costs associated with negative ESG controversy news. Our results show that the occurrence of an ESG-risk event leads to a statistically significant average reduction of 0.29% in abnormal returns. Furthermore, our study suggests this effect is predominantly driven by Social and Governance categories, along with the “Environmental Opportunities” subcategory. Our research highlights the considerable impact of social media on financial markets, particularly in shaping shareholders’ perception of ESG reputation. We formulate several policy implications based on our findings.

Complexity vs Empirical Score

  • Math Complexity: 3.5/10
  • Empirical Rigor: 7.5/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper uses standard event study statistics rather than complex mathematics, but it demonstrates strong empirical rigor through its massive 114 million tweet dataset. It offers high novelty by uniquely isolating social media ESG reputation risk from standard physical controversy news and measuring its direct impact on S&P100 abnormal returns.

Research Flowchart

  flowchart TD
    A["Research Question: How does ESG reputational risk on social media impact shareholder returns?"] --> B["Data Collection: 114M Tweets on S&P 100 Firms (2016-2022)"]
    B --> C["Processing: Extract ESG Discussions & Identify Volume Spikes (Events)"]
    C --> D["Methodology: Event Study Analysis (Isolating Social Media from News)"]
    D --> E["Key Finding: ESG-risk events reduce abnormal returns by 0.29%"]
    E --> F["Category Drivers: Driven by Social, Governance, and Environmental Opportunities"]