The Impact of Financial News Sentiment on Market Index Volatility through Event-Driven Analysis Using Random Forest and Linear Regression Models

This study investigates the impact of financial news sentiment on market index volatility using an event-driven analytical approach combined with machine learning models. Two predictive algorithms, Linear Regression and Random Forest Regressor, were employed to evaluate how sentiment polarity, market event type, trading volume, and sector classification influence short-term index fluctuations. The results demonstrate that both models have limited explanatory power, as reflected by low and negative R² values (−0.0147 and −0.1479), indicating that sentiment polarity alone cannot adequately capture market volatility. Feature importance analysis revealed that Trading Volume (0.48) and Market Event Type (0.31) are the most influential predictors, while Sentiment Score (0.14) contributes marginally. These findings suggest that market volatility is primarily volume-driven and event-reactive, with sentiment serving as a secondary amplifier rather than a direct causal factor. The study concludes that combining sentiment analysis with quantitative and temporal indicators may improve the modeling of complex market dynamics in future research.