A multi-modal data fusion approach for evaluating the impact of extreme public sentiments on corporate credit ratings

Abstract

This paper introduces a novel multi-modal data fusion framework to examine the impact of extreme public sentiments on corporate credit ratings. Departing from traditional binary or ternary sentiment classifications, our approach leverages a fine-tuned bidirectional encoder representations from transformers (BERT) model to categorize 3,839,916 Twitter (now X) posts into five distinct sentiment groups—ranging from extremely negative to extremely positive. By integrating these refined sentiment signals with firm-specific financial data for target S&P 500 companies, we construct a comprehensive multi-modal dataset that enables a more granular investigation of the interplay between public opinions and credit changes. Employing a suite of econometric techniques—including two-way fixed-effects panel regressions, ordinary least squares, system generalized method of moments and generalized linear models—we demonstrate that extremely negative sentiment exerts a statistically significant detrimental effect on credit ratings, whereas the impact of extremely positive sentiment remains largely insignificant. Robustness checks, including sensitivity analyses, lag effect examinations, reverse causality checks, and nonlinear analyses, further corroborate that the adverse influence on credit ratings is primarily driven by the extreme facet of negative public sentiment. By fusing deep learning–based textual analysis with traditional financial metrics, our work not only refines the measurement of public sentiment but also provides robust evidence of its dynamic implications for corporate financial stability. This multi-modal data fusion approach paves the way for future research to incorporate additional social media streams and advanced language models, thereby enhancing predictive accuracy and deepening insights into the financial ramifications of public sentiments.