Media Sentiment and Volatility Index Futures Returns: Evidence from Textual Analysis of News, Blogs, and Online Discussions

ABSTRACT This study investigates the predictive relationship between media sentiment and daily VIX futures returns through a comprehensive textual analysis of financial news articles, blogs, and online discussions. Employing the Loughran and McDonald lexicon‐based methodology, we construct sentiment indices derived from an extensive data set of over 700,000 media posts, with a particular emphasis on overnight sentiment effects. Our empirical results demonstrate that negative sentiment extracted from news articles and online discussions exhibits significant predictive power for subsequent VIX futures returns, whereas blog‐based sentiment demonstrates comparatively limited efficacy. Notably, this robust predictive relationship persists even during periods of macroeconomic announcements, suggesting that media sentiment captures information beyond traditional economic indicators. Furthermore, we develop sentiment‐based trading strategies that yield exceptional performance metrics, generating annualized risk‐adjusted returns of 46.32% for news‐derived strategies and 104.58% for online discussion‐based approaches—substantially outperforming conventional benchmark strategies.