Twitter sentiment and stock market movements: The predictive power of social media, by Talita Greyling and Stephanié Rossouw
For VoxEU
The influence that investor sentiment can have on market movements is often overlooked by traditional financial models. This column analyses nearly three million stock-related tweets to investigate whether sentiment derived from such tweets can predict intraday stock market fluctuations. The findings suggest that tweet-based sentiment strongly predicts market trends in both developed and emerging markets. Recognising traders’ emotions has implications for traders, analysts, and regulators seeking to anticipate and interpret market behaviour.Research on the accurate prediction of stock market movements is of interest to academics, economists, and financial analysts due to the profitability of accurately predicting the markets. The intersection of behavioural finance and Big Data has therefore become increasingly relevant in financial market analysis. Events such as the GameStop short squeeze in 2021 demonstrate the power of social media in influencing stock prices. This phenomenon aligns with behavioural finance theories that emphasise the role of investor sentiment in asset pricing (Baker and Wurgler 2006). While traditional asset-pricing models assume rational decision-making, behavioural biases and herd mentality often drive real-world trading decisions (De Long et al. 1990).
Previous research has explored investor sentiment using social media platforms such as Twitter (now X). Antweiler and Frank (2004) found that online messages contain valuable predictive information. Similarly, Bollen et al. (2011) demonstrated that Twitter mood correlates with stock market fluctuations. Van Wincoop and Gholampour (2017) considered the sentiment of opinionated tweets in predicting the euro-dollar exchange rate. Our study (Greyling and Rossouw 2022) builds on this research by analysing sentiment and emotions extracted from stock-related tweets to predict intraday market movements across multiple stock exchanges.
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ABOUT THE AUTHORS
Stephanié Rossouw is an Associate Professor and Deputy Head of the School of Social Science and Humanities at Auckland University of Technology (AUT), New Zealand. Her research focuses on well-being and happiness, particularly using Big Data. She co-created the Gross National Happiness.Today Project (GNH), which tracks global happiness in real-time via Google Trends, and has earned multiple awards, including the University of Johannesburg's Vice Chancellor's Award for Innovation.
In 2024, she received the ISQOLS Fellow Award for her contributions to well-being research through Big Data and AI. Her work on the COVID-19 lockdown's impact on happiness earned Wiley's Top Cited Article award, and her research papers have ranked in the top percentiles for citations.
Talita Greyling is a Professor in the School of Economics at the University of Johannesburg in South Africa. She specialises in well-being economics and quality of life studies and has a keen interest in fourth industrial revolution applications. She developed and established the "Gross National Happiness Index.today (GNH.today) project".
The project uses 4IR methods and Big Data to construct real-time happiness and emotion data. Consequently, the GNH.today project partnered with the Auckland University of Technology. The project received the Vice Chancellor's Distinguished Award for Innovation and was accepted as official statistical data by Stats New Zealand.
