TY  - JOUR
A1  - Abdel-Karim, Benjamin M.
A1  - Benlian, Alexander
A1  - Hinz, Oliver
T1  - The predictive value of data from virtual investment communities
T2  - Machine learning and knowledge extraction
N2  - 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.
KW  - financial decision support
KW  - prediction
KW  - deep learning
Y1  - 2020
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/57563
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-575632
SN  - 2504-4990
VL  - 3.2021
SP  - 1
EP  - 13
PB  - MDPI
CY  - Basel
ER  -