Abdel-Karim, Benjamin M. ; Benlian, Alexander ; Hinz, Oliver (2023)
The Predictive Value of Data from Virtual Investment Communities.
In: Machine Learning and Knowledge Extraction, 2020, 3 (1)
doi: 10.26083/tuprints-00017453
Article, Secondary publication, Publisher's Version
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Item Type: | Article |
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Type of entry: | Secondary publication |
Title: | The Predictive Value of Data from Virtual Investment Communities |
Language: | English |
Date: | 20 November 2023 |
Place of Publication: | Darmstadt |
Year of primary publication: | 2020 |
Place of primary publication: | Basel |
Publisher: | MDPI |
Journal or Publication Title: | Machine Learning and Knowledge Extraction |
Volume of the journal: | 3 |
Issue Number: | 1 |
Collation: | 13 Seiten |
DOI: | 10.26083/tuprints-00017453 |
Corresponding Links: | |
Origin: | Secondary publication DeepGreen |
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. |
Uncontrolled Keywords: | financial decision support, prediction, deep learning |
Status: | Publisher's Version |
URN: | urn:nbn:de:tuda-tuprints-174539 |
Classification DDC: | 000 Generalities, computers, information > 004 Computer science 300 Social sciences > 330 Economics |
Divisions: | 01 Department of Law and Economics > Betriebswirtschaftliche Fachgebiete > Fachgebiet Information Systems & E-Services |
Date Deposited: | 20 Nov 2023 09:56 |
Last Modified: | 27 Nov 2023 07:27 |
SWORD Depositor: | Deep Green |
URI: | https://tuprints.ulb.tu-darmstadt.de/id/eprint/17453 |
PPN: | 513476059 |
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