Sentiment Analysis of Financial News and Social Media for Early Market Volatility Prediction

Abstract

Market volatility forecasting is a central challenge in financial economics, with implications for risk management, portfolio optimization, and regulatory oversight. Traditional volatility models rely primarily on historical price dynamics, often lagging behind real-time information dissemination occurring through textual data streams such as financial news and social media. This paper explores how sentiment analysis of textual information can serve as an early warning system for market volatility. We present a comprehensive framework that integrates sentiment features from financial news and social media with econometric and deep learning models. Leveraging methods such as FinBERT-based sentiment scoring, GARCH-X modeling, and transformer-based temporal aggregation, we show how textual signals can anticipate volatility shocks. The proposed hybrid modeling framework demonstrates enhanced predictive power, interpretability, and robustness. Furthermore, this study discusses implications for algorithmic trading, financial surveillance, and behavioral finance.