ABSTRACT This research applies technical indexes, which usually used by the fund manager, to be the input variables for a neural predictive model. The neural predictive model based on the Multi-Layers Backpropagation Artificial Neural Networks is used to predict the variations insector stock index of Taiwan. The output variables of the proposed neural predictive model are the variations of 2、5、10、15 and 22 days stock indexes. The data pairs of 230 and 1095 days are used to train the neural models by conventional training, and the data pairs of 230 days are also used to train the neural predictive model by moving window training. The simulation results reveal the prediction of 2 days variations of stock indexes is best accurate with least convergent error, and 5, 10, 15 and 22days prediction is second, third, forth, fifth respectively. The 2 days prediction with 1,095 days training data pairs has the best accuracy. It means more information for training will obtain better predictive accuracy.