An improved OIF elman neural network and its applications to stock market

Authors:
Limin Wang;Yanchun Liang;Xiaohu Shi;Ming Li;Xuming Han
Affiliations:
College of Computer Science and Technology, Jilin University, Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Changchun, China;College of Computer Science and Technology, Jilin University, Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Changchun, China;College of Computer Science and Technology, Jilin University, Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Changchun, China;Department of Computer Science and Technology, Changchun Taxation College, Changchun, China;College of Computer Science and Technology, Jilin University, Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Changchun, China
Venue:
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Year:
2006

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Abstract

An improved model is proposed based on the OIF Elman neural network by introducing direction and time profit factors and applied to the prediction of the composite index of stock. Simulation results show that the proposed model is feasible and effective. Comparisons are also made when the stock exchange is performed using prediction results from different models. It shows that the proposed model could improve the prediction precision evidently and realize the main purpose for investors to obtain more profits.