The Predictive Power of Social Media Sentiment for Short-Term Stock Movements

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearch

Authors

Ever since modern-day financial markets existed, people have been trying to forecast movements in stock prices, as accurate predictions would entail economic benefits and the reduction of risks. This paper examines whether social media sentiment can be used to predict short-term stock movements. Using more than two years of data from Twitter, we assess the effect the extracted sentiment holds for 10 companies listed in the S&P500. Applying different sentiment analysis approaches and forecasting models, we find that for three out of the ten companies, sentiment does significantly improve the forecasting performance. A custom-built sentiment model outperforms an off-the-shelf VADER model, and tree-based models deliver better performance than linear ones. On the theoretical front, this provides evidence against the Efficient Market Hypothesis and warrants future research regarding the circumstances under which stock returns might be predictable.
Original languageEnglish
Title of host publicationWirtschaftsinformatik 2022 Proceedings
EditorsSven Laumer, Martin Matzner
Number of pages13
PublisherThe Association for Information Systems (AIS)
Publication date2022
Publication statusPublished - 2022
Externally publishedYes

    Research areas

  • Informatics - Social sentiment, Twitter, stock market, predictive power, forecasting