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Vorhersage von Aktienkursbewegungen der Energiebranche mithilfe maschinellen Lernens und Stimmungserkennung von Beiträgen aus sozialen Medien

  • The goal of this empirical study is to answer whether predictions about stock price movements can be made with the use of machine learning in the energy sector and what influence contributions from social media have on its development. To answer the research question, the social media platforms Twitter and Reddit, in terms of the suitability of the information, were studied and evaluated. Then, the sentiments of the posts from social media were collected and used in machine learning models. The models include the Gradient Boosted Regression Tree, Multilayer Perceptron, and Long Short-Term i Memory, which predict a subsequent day's closing stock price. The study showed that deviations from predictions of stock price movements of 1.05 % are possible and further sentiment values do not show significant positive effect on reducing the error value. The result shows that the collected sentiments from the social media platform Twitter have no positive effect on the stock price movements within the energy industry. Keywords: stock market, stock prediction, artificial neural networks, machine learning, energy market, sentiment analysis

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Metadaten
Author:Jan Vogt
URN:urn:nbn:de:hbz:1393-opus4-7410
Document Type:Master's Thesis
Language:German
Year of Completion:2021
Date of final exam:2022/01/26
Release Date:2022/05/04
Page Number:108
Institutes:Fachbereich 1 - Institut Informatik
DDC class:300 Sozialwissenschaften / 330 Wirtschaft
Licence (German):License LogoNo Creative Commons