Stock index forecasting based on a hybrid model
Forecasting the stock market price index is a challenging task. The exponential smoothing model (ESM), autoregressive integrated moving average model (ARIMA), and the back propagation neural network (BPNN) can be used to make forecasts based on time series. In this paper, a hybrid approach combining ESM, ARIMA, and BPNN is proposed to be the most advantageous of all three models. The weight of the proposed hybrid model (PHM) is determined by genetic algorithm (GA). The closing of the Shenzhen Integrated Index (SZII) and opening of the Dow Jones Industrial Average Index (DJIAI) are used as illustrative examples to evaluate the performances of the PHM. Numerical results show that the proposed model outperforms all traditional models, including ESM, ARIMA, BPNN, the equal weight hybrid model (EWH), and the random walk model (RWM).
Volume (Year): 40 (2012)
Issue (Month): 6 ()
|Contact details of provider:|| Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/375/description#description|
|Order Information:|| Postal: http://www.elsevier.com/wps/find/supportfaq.cws_home/regional|
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Armstrong, J. Scott, 1989. "Combining forecasts: The end of the beginning or the beginning of the end?," International Journal of Forecasting, Elsevier, vol. 5(4), pages 585-588.
- Wu, Berlin & Chang, Chih-Li, 2002. "Using genetic algorithms to parameters (d,r) estimation for threshold autoregressive models," Computational Statistics & Data Analysis, Elsevier, vol. 38(3), pages 315-330, January.
- Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
- Ribeiro, Celma O. & Oliveira, Sydnei M., 2011. "A hybrid commodity price-forecasting model applied to the sugar–alcohol sector," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 55(2), June.
- 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.
- Pao, H.T., 2009. "Forecasting energy consumption in Taiwan using hybrid nonlinear models," Energy, Elsevier, vol. 34(10), pages 1438-1446.
- Uri, Noel D, 1977. "Forecasting: A hybrid approach," Omega, Elsevier, vol. 5(4), pages 463-472.
- Huang, Min & He, Yong & Cen, Haiyan, 2007. "Predictive analysis on electric-power supply and demand in China," Renewable Energy, Elsevier, vol. 32(7), pages 1165-1174.
- Leigh, W. & Paz, M. & Purvis, R., 2002. "An analysis of a hybrid neural network and pattern recognition technique for predicting short-term increases in the NYSE composite index," Omega, Elsevier, vol. 30(2), pages 69-76, April.
- Sunil Gupta & Peter C. Wilton, 1987. "Combination of Forecasts: An Extension," Management Science, INFORMS, vol. 33(3), pages 356-372, March.
- Franses, Philip Hans & Ghijsels, Hendrik, 1999. "Additive outliers, GARCH and forecasting volatility," International Journal of Forecasting, Elsevier, vol. 15(1), pages 1-9, February.
When requesting a correction, please mention this item's handle: RePEc:eee:jomega:v:40:y:2012:i:6:p:758-766. See general information about how to correct material in RePEc.
If references are entirely missing, you can add them using this form.