An efficient CMAC neural network for stock index forecasting

Stock index forecasting is one of the major activities of financial firms and private investors in making investment decisions. Although many techniques have been developed for predicting stock index, building an efficient stock index forecasting model is still an attractive issue since even the smallest improvement in prediction accuracy can have a positive impact on investments. In this paper, an efficient cerebellar model articulation controller neural network (CAMC NN) is proposed for stock index forecasting. The traditional CAMC NN scheme has been successfully used in robot control due to its advantages of fast learning, reasonable generalization capability and robust noise resistance. But, few studies have been reported in using a CMAC NN scheme for forecasting problems. To improve the forecasting performance, this paper presents an efficient CMAC NN scheme. The proposed CMAC NN scheme employs a high quantization resolution and a large generalization size to reduce generalization error, and uses an efficient and fast hash coding to accelerate many-to-few mappings. The forecasting results and robustness evaluation of the proposed CMAC NN scheme were compared with those of a support vector regression (SVR) and a back-propagation neural network (BPNN). Experimental results from Nikkei 225 and Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) closing indexes show that the performance of the proposed CMAC NN scheme was superior to the SVR and BPNN models.