The Impact of Social Media Sentiment on Intraday Stock Price Volatility

This study highlights the increasing impact of real-time digital discourse on financial markets by examining the relationship between mood on social media and intraday stock price volatility. The study investigates whether online investor sentiment may account for short-term stock price variations by utilizing sentiment analysis techniques on social media sites like Twitter and Stock Twits and combining them with highfrequency trading data. The study measures sentiment signals and correlates them with intraday price changes across a selection of U.S. stocks using both lexicon-based and machine learning (including transformer-based) sentiment models. The results show a statistically significant correlation between intraday volatility and the overall sentiment on social media, especially when market activity is high. These findings highlight the significance of information-driven and behavioural aspects in financial markets and imply that real-time sentiment analysis can be a useful tool for predicting short-term volatility. In addition to highlighting issues with data noise, sentiment interpretation, and causality, the study adds to the body of knowledge on market microstructure, behavioural finance, and financial data analytics