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An EPC Forecasting Method for Stock Index Based on Integrating Empirical Mode Decomposition, SVM and Cuckoo Search Algorithm

Author

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  • Li Xiangfei

    (School of Management, Tianjin Polytechnic University, Tianjin300387, China)

  • Zhang Zaisheng

    (College of Management and Economics, Tianjin University, Tianjin300072, China)

  • Huang Chao

    (School of Accountancy, Shanghai University of Finance and Economics, Shanghai200433, China)

Abstract

In order to improve the forecasting accuracy, a hybrid error-correction approach by integrating support vector machine (SVM), empirical mode decomposition (EMD) and the improved cuckoo search algorithm (ICS) was introduced in this study. By using two indexes as examples, the empirical study shows our proposed approach by means of synchronously predict the prediction error which used to correct the preliminary predicted values has better prediction precision than other five competing approaches, furthermore, the improved strategies for cuckoo search algorithm has better performance than other three evolutionary algorithms in parameters selection.

Suggested Citation

  • Li Xiangfei & Zhang Zaisheng & Huang Chao, 2014. "An EPC Forecasting Method for Stock Index Based on Integrating Empirical Mode Decomposition, SVM and Cuckoo Search Algorithm," Journal of Systems Science and Information, De Gruyter, vol. 2(6), pages 481-504, December.
  • Handle: RePEc:bpj:jossai:v:2:y:2014:i:6:p:481-504:n:1
    DOI: 10.1515/JSSI-2014-0481
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    References listed on IDEAS

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    1. Anderson, Richard G. & Hoffman, Dennis L. & Rasche, Robert H., 2002. "Reply to the comments on 'A vector error-correction forecasting model of the U.S. economy'," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 613-614, December.
    2. Pai, Ping-Feng & Lin, Chih-Sheng, 2005. "A hybrid ARIMA and support vector machines model in stock price forecasting," Omega, Elsevier, vol. 33(6), pages 497-505, December.
    3. Yu, Lean & Wang, Shouyang & Lai, Kin Keung, 2008. "Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm," Energy Economics, Elsevier, vol. 30(5), pages 2623-2635, September.
    4. Walton, S. & Hassan, O. & Morgan, K. & Brown, M.R., 2011. "Modified cuckoo search: A new gradient free optimisation algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 44(9), pages 710-718.
    5. Anderson, Richard G. & Hoffman, Dennis L. & Rasche, Robert H., 2002. "A vector error-correction forecasting model of the US economy," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 569-598, December.
    6. Lin, Chiun-Sin & Chiu, Sheng-Hsiung & Lin, Tzu-Yu, 2012. "Empirical mode decomposition–based least squares support vector regression for foreign exchange rate forecasting," Economic Modelling, Elsevier, vol. 29(6), pages 2583-2590.
    7. Wang, Ju-Jie & Wang, Jian-Zhou & Zhang, Zhe-George & Guo, Shu-Po, 2012. "Stock index forecasting based on a hybrid model," Omega, Elsevier, vol. 40(6), pages 758-766.
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    Cited by:

    1. Wang, Jue & Wang, Zhen & Li, Xiang & Zhou, Hao, 2022. "Artificial bee colony-based combination approach to forecasting agricultural commodity prices," International Journal of Forecasting, Elsevier, vol. 38(1), pages 21-34.

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