Accurate stock price forecasting can be immensely helpful for investors, because more accurate predictions will bring more profit for investors. Stock market participants usually make their short-term investment decisions according to recent stock knowledge such as the analytical report before market opened, yesterday stock index volatility of other countries, and the price fluctuations in recent days. However, there are three major drawbacks in the conventional time series model: (1) the majority statistical methods rely upon assumptions for the variables; (2) most models do not consider the volatility of different country stock market; and (3) most forecasting models do not provide the predicting rule of stock price index. Based on these reasons above, this paper proposes a new model, using multi-stock volatility causality (Dow Jones (Dow Jones Industrial Average), NASDAQ (the largest US electronic stock market)) joining to fusion ANFIS models, to forecast Taiwan stock index. Furthermore, one-period and two-period adaptive expectation model are applied to the proposed model for enhancing the forecasting performance. To evaluate the forecasting performances, the proposed model is compared with two different models, Chen’s model and Huarng’s model. The experimental results indicate that the proposed model is superior to the listing methods in terms of RMSE (root mean squared error).
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