The Predictive Value of Data from Virtual Investment Communities

Benjamin M. Abdel-Karim, Alexander Benlian and Oliver Hinz

Publications of Darmstadt Technical University, Institute for Business Studies (BWL) from Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL)

Abstract: 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.

Date: 2021
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Published in Machine Learning and Knowledge Extraction 1 (2021) : pp. 1-13

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Working Paper: The Predictive Value of Data from Virtual Investment Communities (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:dar:wpaper:124589

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