The Predictive Power of Social Media Sentiment for Short-Term Stock Movements
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung
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Wirtschaftsinformatik 2022 Proceedings. Hrsg. / Sven Laumer; Martin Matzner. The Association for Information Systems (AIS), 2022.
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung
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TY - CHAP
T1 - The Predictive Power of Social Media Sentiment for Short-Term Stock Movements
AU - Wilksch, Moritz
AU - Abramova, Olga
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Informatics
KW - Social sentiment
KW - Twitter
KW - stock market
KW - predictive power
KW - forecasting
M3 - Article in conference proceedings
BT - Wirtschaftsinformatik 2022 Proceedings
A2 - Laumer, Sven
A2 - Matzner, Martin
PB - The Association for Information Systems (AIS)
ER -