Media and Market Turbulence: A Review of Financial News Impact on Stock Volatility

Abstract The authors study how financial news has emerged as a critical factor influencing stock market volatility over the past decade. This review synthesizes literature from 2015 to 2025 on how news and investor sentiment drive market fluctuations. The authors observed a marked rise in research output during this period, reflecting growing scholarly interest. Key findings show that media tone and sentiment indices often predict future volatility and returns more effectively than historical price-based indicators. Advanced text analytics and machine learning (ML) techniques have been widely adopted to extract signals from news feeds, social media and macroeconomic announcements. While significant progress has been made – such as improving volatility forecasts via sentiment-based measures – gaps remain in cross-market generalizability, real-time trading applicability and the use of next-generation natural language processing (NLP). The authors highlight opportunities for future research, including multilingual news analysis, higher-frequency volatility forecasting and disentangling the causal impact of news versus investor actions.