Forecasting stock market volatility using social media sentiment analysis

In the era where social media significantly influences public sentiment, platforms such as Twitter have become vital in predicting stock market trends. This paper presents a cutting-edge predictive model that integrates historical stock market data, Twitter sentiment analysis, and an extensive array of tweet-related features. Utilizing advanced regression techniques and deep neural networks, our model forecasts the daily closing prices of the U.S. stock market indices with notable accuracy and demonstrates a strong link between market values, sentiment scores, and social media activities. Our analysis particularly emphasizes the importance of tweet diffusion and the influence of prominent Twitter users in refining prediction accuracy. Contrary to conventional wisdom, we discovered that incorporating a wide range of tweet-derived features significantly improves the model’s performance without leading to sparsity challenges. This study not only questions established paradigms but also underscores the potential of social media analytics in financial market forecasting, with substantial implications for investors, market analysts, and policy makers.