Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm

Authors:
Wei Shen;Xiaopen Guo;Chao Wu;Desheng Wu
Affiliations:
School of Business and Administration, North China Electric Power University, Beijing 102206, China;School of Business and Administration, North China Electric Power University, Beijing 102206, China;University of Waterloo, Computing and Financial Management, Ontario, Canada N2L 3G1;RiskLab, University of Toronto, Toronto, ON M5S 3G3 Canada and Reykjavik University Menntavegur 1, 101 Reykjavik, Iceland
Venue:
Knowledge-Based Systems
Year:
2011

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Abstract

Stock index forecasting is a hot issue in the financial arena. As the movements of stock indices are non-linear and subject to many internal and external factors, they pose a great challenge to researchers who try to predict them. In this paper, we select a radial basis function neural network (RBFNN) to train data and forecast the stock indices of the Shanghai Stock Exchange. We introduce the artificial fish swarm algorithm (AFSA) to optimize RBF. To increase forecasting efficiency, a K-means clustering algorithm is optimized by AFSA in the learning process of RBF. To verify the usefulness of our algorithm, we compared the forecasting results of RBF optimized by AFSA, genetic algorithms (GA) and particle swarm optimization (PSO), as well as forecasting results of ARIMA, BP and support vector machine (SVM). Our experiment indicates that RBF optimized by AFSA is an easy-to-use algorithm with considerable accuracy. Of all the combinations we tried in this paper, BIAS6+MA5+ASY4 was the optimum group with the least errors.