Problem
Research questions and friction points this paper is trying to address.
media bias
stock price dynamics
firm-specific analysis
financial news
structural breaks
Quantitative Analysis of Media Bias and Stock Price Dynamics: The 2020 Shock
🤖 AI Summary
This study investigates whether financial news drives stock price movements or merely reflects already priced-in information, focusing on firm-level dynamics. Leveraging 6.28 million news headlines from 2015 to 2025 covering 26 large U.S. firms, the authors construct a daily media sentiment index and employ panel regressions alongside a data-driven structural-break vector autoregression model to examine how the relationship between news and stock prices evolved before and after the onset of the COVID-19 pandemic. Moving beyond conventional aggregate sentiment frameworks, the analysis underscores the importance of examining media–market interactions at the individual firm level. While no significant persistent market-wide effects are observed post-pandemic, pronounced dynamic linkages emerge around firm-specific structural breaks, thereby validating the necessity and efficacy of granular, firm-level analysis.
📝 Abstract
Whether financial news influences stock prices or simply reflects information already incorporated into them remains an open question in financial economics. The COVID-19 pandemic provides an opportunity to revisit this question, as it disrupted both news coverage and financial markets on an unprecedented scale. Existing studies have largely approached the problem through aggregate sentiment measures, leaving it unclear whether the observed relationships also hold at the level of individual firms. We study this question using 6.28 million news headlines covering 26 large United States firms between 2015 and 2025. After filtering the corpus to retain materially relevant firm-specific coverage, we construct daily stance measures and examine how their relationship with stock returns changed around the 2020 shock using panel regressions and vector autoregressions with data-driven structural breaks. Our findings indicate that the relationship between financial news and equity markets is more nuanced than aggregate analyses alone suggest. While we find little evidence of a persistent market-wide change in media stance or stock returns following the pandemic, dynamic relationships emerge for a subset of firms around their own structural breaks. Taken together, these results suggest that understanding media-market interactions requires firm specific analysis and provide a framework for studying how news and prices interact under changing market conditions.
Innovation
Methods, ideas, or system contributions that make the work stand out.
firm-specific analysis
data-driven structural breaks
media stance
vector autoregression
news-stock interaction
🔎 Similar Papers
No similar papers found.
💼 Related Jobs
