期刊
INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS
卷 7, 期 2, 页码 272-290出版社
ATLANTIS PRESS
DOI: 10.1080/18756891.2013.864472
关键词
artificial neural networks; ensemble forecasting; particle swarm optimization; genetic operator; stock e-exchange prices
资金
- Humanities and Social Sciences Youth Foundation of the Ministry of Education in China [11YJC870028]
- Selfdetermined Research Funds of CCNU from the Colleges' Basic Research and Operation of MOE [CCNU13F030]
- Natural Science Foundation Teachers' Fund for Doctor Stations, Ministry of Educationof China [71101100, 71001096]
- New Teachers' Fund for Doctor Stations, Ministry of Education [20110181120047]
- China Postdoctoral Science Fuundation [2011M500418, 2012T50148, 2013M530753]
- Excellent Youth fund of Sichuan University [2013SCU04A08]
- Frontier Cross-innovation Fund of Sichuan University [skqy201352]
- Soft Science Foundation of Sichuan Province [2013ZR0016]
- Center for Forecasting Science of the Chinese Academy of Sciences
- National Center for Mathematics and Interdisciplinary Sciences in China
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.
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