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The Predictive Value of Data from Virtual Investment Communities

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
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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