Stock index forecasting based on a hybrid model

Ju-Jie Wang, Jian-Zhou Wang, Zhe-George Zhang and Shu-Po Guo

Omega, 2012, vol. 40, issue 6, 758-766

Abstract: 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).

Keywords: Stock price; Forecasting; Exponential smoothing; ARIMA; BPNN; Genetic algorithm; Hybrid model (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (56)

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DOI: 10.1016/j.omega.2011.07.008

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