期刊
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE
卷 40, 期 6, 页码 758-766出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.omega.2011.07.008
关键词
Stock price; Forecasting; Exponential smoothing; ARIMA; BPNN; Genetic algorithm; Hybrid model
资金
- Ministry of Education overseas cooperation [Z2007-1-62012]
- Lanzhou University [LZULL200910]
- NSERC of Canada [RGPIN197319]
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). (C) 2011 Elsevier Ltd. All rights reserved.
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