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The Predictive Value of Data from Virtual Investment Communities

1
Information Systems & Information Management, Goethe University, 60323 Frankfurt, Germany
2
Information Systems & E-Services, Darmstadt University of Technology, 64289 Darmstadt, Germany
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Author to whom correspondence should be addressed.
Mach. Learn. Knowl. Extr. 2021, 3(1), 1-13; https://0-doi-org.brum.beds.ac.uk/10.3390/make3010001
Received: 23 November 2020 / Revised: 15 December 2020 / Accepted: 19 December 2020 / Published: 23 December 2020
Optimal investment decisions by institutional investors require accurate predictions with respect to the development of stock markets. Motivated by previous research that revealed the unsatisfactory performance of existing stock market prediction models, this study proposes a novel prediction approach. Our proposed system combines Artificial Intelligence (AI) with data from Virtual Investment Communities (VICs) and leverages VICs’ ability to support the process of predicting stock markets. An empirical study with two different models using real data shows the potential of the AI-based system with VICs information as an instrument for stock market predictions. VICs can be a valuable addition but our results indicate that this type of data is only helpful in certain market phases. View Full-Text
Keywords: financial decision support; prediction; deep learning financial decision support; prediction; deep learning
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MDPI and ACS Style

Abdel-Karim, B.M.; Benlian, A.; Hinz, O. The Predictive Value of Data from Virtual Investment Communities. Mach. Learn. Knowl. Extr. 2021, 3, 1-13. https://0-doi-org.brum.beds.ac.uk/10.3390/make3010001

AMA Style

Abdel-Karim BM, Benlian A, Hinz O. The Predictive Value of Data from Virtual Investment Communities. Machine Learning and Knowledge Extraction. 2021; 3(1):1-13. https://0-doi-org.brum.beds.ac.uk/10.3390/make3010001

Chicago/Turabian Style

Abdel-Karim, Benjamin M., Alexander Benlian, and Oliver Hinz. 2021. "The Predictive Value of Data from Virtual Investment Communities" Machine Learning and Knowledge Extraction 3, no. 1: 1-13. https://0-doi-org.brum.beds.ac.uk/10.3390/make3010001

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