Year: 2025
Type: article
Abstract: This study investigates the predictive power of social media sentiment on stock market returns. As social media platforms have become increasingly influential in shaping public opinion, their impact on financial markets has grown significantly. Through a comprehensive analysis of existing literature and empirical evidence, we examine how sentiment expressed on platforms such as Twitter, Reddit, and StockTwits correlates with and potentially predicts stock market movements. The research employs a multi-methodological approach, combining sentiment analysis techniques with machine learning models to evaluate the relationship between social media discourse and market performance. Our findings indicate that social media sentiment demonstrates significant predictive power for stock market returns, particularly during periods of high market volatility and for specific sectors. The GameStop short squeeze of 2021 serves as a pivotal case study, illustrating how coordinated retail investor sentiment on social media can dramatically impact stock prices and challenge traditional market dynamics. This research contributes to the growing field of behavioral finance by highlighting the importance of incorporating social media sentiment analysis into investment strategies and market forecasting models. The implications extend to investors, financial institutions, and regulators seeking to understand and navigate the evolving landscape of information flow in financial markets.
Author: Leandro Yacoubian
Language: English
Subfield: Finance
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4411006091