The predictive value of data from virtual investment communities
- 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.
Author: | Benjamin M. Abdel-KarimORCiDGND, Alexander BenlianORCiDGND, Oliver HinzORCiDGND |
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URN: | urn:nbn:de:hebis:30:3-575632 |
DOI: | https://doi.org/10.3390/make3010001 |
ISSN: | 2504-4990 |
Parent Title (English): | Machine learning and knowledge extraction |
Publisher: | MDPI |
Place of publication: | Basel |
Document Type: | Article |
Language: | English |
Date of Publication (online): | 2020/12/23 |
Date of first Publication: | 2020/12/23 |
Publishing Institution: | Universitätsbibliothek Johann Christian Senckenberg |
Release Date: | 2020/12/27 |
Tag: | deep learning; financial decision support; prediction |
Volume: | 3.2021 |
Page Number: | 13 |
First Page: | 1 |
Last Page: | 13 |
HeBIS-PPN: | 476237297 |
Institutes: | Wirtschaftswissenschaften / Wirtschaftswissenschaften |
Dewey Decimal Classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft | |
Sammlungen: | Universitätspublikationen |
Open-Access-Publikationsfonds: | Wirtschaftswissenschaften |
Licence (German): | Creative Commons - Namensnennung 4.0 |