The Role of Financial News Sentiment Bias in Stock Market Information Asymmetry

Abstract

The growth of digital financial media has led to the faster and larger dissemination of market information while varying levels of emotionality in the news reporting from different sources can create conflicting public signals and add to information asymmetry. While there is some existing literature on the connection between aggregate news sentiment and stock returns, volatility, and trading volume, the phenomenon of sentiment bias in cross-media news reporting has not been adequately studied yet. In order to fill the gap, financial news about S&P 100 firms published during 2021–2025 is gathered from GDELT and matched against the daily market data from Yahoo Finance. Headlines are analyzed using FinBERT to find the level of their sentiment while Sentence-BERT and HDBSCAN determine whether the news item refers to the same event. Event-level median sentiment becomes a reference point while deviations from the consensus sentiment measure the cross-media bias in public sentiment. The impact of sentiment biases on the market is assessed using the Corwin-Schultz bid-ask spread, Amihud illiquidity, and two-way fixed effects regressions. It was found that greater cross-media divergence in news sentiments causes the widening of next day bid-ask spread and increased illiquidity; moreover, negative deviations have a stronger impact than positive. These correlations were consistent after controlling for return, volatility, trading volume, and news attention as well as exclusion of bias outliers.