Beyond the headlines: Sentiment divergence and financial distress
Year: 2025
Type: article
Abstract: In the age of social media, corporate financial health is increasingly influenced by diverse and often conflicting information signals. This study introduces sentiment divergence, the difference in sentiment expressed on social media (X, formerly Twitter) versus traditional news media, as a novel predictor of financial distress. Analyzing 1,823 U.S. firms from Q1 2015 to Q1 2021, the results reveal that a one standard deviation increase in sentiment divergence decreases the one-year probability of default by 7 basis points, supporting theories positing that diverse information, even when contradictory, enhances market efficiency. Conversely, a one standard deviation increase in the volatility of this divergence increases default probability by 46 basis points, highlighting the destabilizing influence of fluctuating sentiment, consistent with noise trading theories. Furthermore, heightened institutional investor attention dramatically amplifies financial distress, as a one standard deviation increase in news attention corresponds to an 869 basis point increase in default probability, underscoring the risks of herding behavior and informational cascades in the presence of divergent signals. This research contributes to behavioral finance by demonstrating the complex interplay between information diversity, sentiment volatility, and investor behavior in shaping corporate financial outcomes, offering crucial implications for investors, managers, and regulators.
Source: Global Finance Journal
Author: John Garcia
Institution: California Lutheran University
Language: English
Subfield: Accounting
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $2,860
OpenAlex ID: W4410529792