International Journal of Computational Intelligence Systems

Volume 6, Issue 1, January 2013, Pages 96 - 114

Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting

Authors
Yi Xiao, Jin Xiao, Fengbin Lu, Shouyang Wang
Corresponding Author
Yi Xiao
Received 23 November 2011, Accepted 22 September 2012, Available Online 2 January 2013.
DOI
https://doi.org/10.1080/18756891.2013.756227How to use a DOI?
Keywords
artificial neural networks, ensemble forecasting, particle swarm optimization, genetic operator, stock e-exchange prices
Abstract

Stock e-exchange prices forecasting is an important financial problem that is receiving increasing attention. This study proposes a novel three-stage nonlinear ensemble model. In the proposed model, three different types of neural-network based models, i.e. Elman network, generalized regression neural network (GRNN) and wavelet neural network (WNN) are constructed by three non-overlapping training sets and are further optimized by improved particle swarm optimization (IPSO). Finally, a neural-network-based nonlinear meta-model is generated by learning three neural-network based models through support vector machines (SVM) neural network. The superiority of the proposed approach lies in its flexibility to account for potentially complex nonlinear relationships. Three daily stock indices time series are used for validating the forecasting model. Empirical results suggest the ensemble ANNs-PSO-GA approach can significantly improve the prediction performance over other individual models and linear combination models listed in this study.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
6 - 1
Pages
96 - 114
Publication Date
2013/01/02
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
https://doi.org/10.1080/18756891.2013.756227How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Yi Xiao
AU  - Jin Xiao
AU  - Fengbin Lu
AU  - Shouyang Wang
PY  - 2013
DA  - 2013/01/02
TI  - Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting
JO  - International Journal of Computational Intelligence Systems
SP  - 96
EP  - 114
VL  - 6
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2013.756227
DO  - https://doi.org/10.1080/18756891.2013.756227
ID  - Xiao2013
ER  -