Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting
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 neur...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Springer
2014-04-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://www.atlantis-press.com/article/25868477.pdf |
| _version_ | 1818572332061425664 |
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| author | Yi Xiao Jin Xiao Fengbin Lu Shouyang Wang |
| author_facet | Yi Xiao Jin Xiao Fengbin Lu Shouyang Wang |
| author_sort | Yi Xiao |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T18:55:42Z |
| format | Article |
| id | doaj.art-6e6b20dddfcf4b7382f72426c7ab84be |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-12-14T18:55:42Z |
| publishDate | 2014-04-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-6e6b20dddfcf4b7382f72426c7ab84be2022-12-21T22:51:06ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832014-04-017210.1080/18756891.2013.864472Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices ForecastingYi XiaoJin XiaoFengbin LuShouyang WangStock 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.https://www.atlantis-press.com/article/25868477.pdfartificial neural networksensemble forecastingparticle swarm optimizationgenetic operatorstock e-exchange prices |
| spellingShingle | Yi Xiao Jin Xiao Fengbin Lu Shouyang Wang Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting International Journal of Computational Intelligence Systems artificial neural networks ensemble forecasting particle swarm optimization genetic operator stock e-exchange prices |
| title | Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting |
| title_full | Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting |
| title_fullStr | Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting |
| title_full_unstemmed | Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting |
| title_short | Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting |
| title_sort | ensemble anns pso ga approach for day ahead stock e exchange prices forecasting |
| topic | artificial neural networks ensemble forecasting particle swarm optimization genetic operator stock e-exchange prices |
| url | https://www.atlantis-press.com/article/25868477.pdf |
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