a comparative study on the classical and artifitial intelligence methods in stock price index forecasting and hybrid modeling

today, stock investment has become an important mean of national finance. apparently, it is significant for investors to estimate the stock price and select the trading chance accurately in advance, which will bring high return to stockholders. in the past, long-term trading processes and many technical analysis methods for stock market were put forward. however, stock market is a nonlinear system, due to the political, economical and psychological impact factors. thus, it is difficult for us to use traditional analysis tools to make stock transaction decision. with the developmenting of nonlinear methods such as neural networks and fuzzy neural networks, we can now use these methods for stock price forecasting. in this research, we presented three scenarios: 1) stock price forecasting with classical methods approach, 2) stock price forecasting with artificial intelligence methods approach, and 3) stock price forecasting with hybrid model. therefore, first, we designed classical models such as exponential smoothing, trend analysis, and arima, then artificial intelligent models such as neural networks and fuzzy neural networks were designed, next the third scenario i.e., hybrid model, was presented. finally, the scenarios were measured. the experimental result showed that the hybrid model had more accuracy than the classical or artificial intelligent models. the hybrid model enjoyed also such properties as fast convergence, high precision and strong function approximation ability, there¬fore, it is suitable for real stock price forecasting.