NDLI: RBF and Artificial Fish Swarm Algorithm for short-term forecast of stock indices

Content Provider	IEEE Xplore Digital Library
Author	Dongxiao Niu Wei Shen Yueshi Sun
Copyright Year	2010
Description	Author affiliation: School of Business and Administration, North China Electric Power University, Beijing, China (Dongxiao Niu; Wei Shen; Yueshi Sun)
Abstract	The movement of stock index is difficult to predict for it is non-linear and subject to many inside and outside factors. Researchers in this field have tried many methods, SVM and ANN, for example, and have achieved good results. In this paper, we select Radial Basis Functions Neural Network (RBFNN) to train data and forecast the stock index in Shanghai Stock Exchanges. In order to solve the problem of slow convergence and low accuracy, and to ensure better forecasting result, we introduce Artificial Fish Swarm Algorithm (AFSA) to optimize RBF, mainly in parameter selection. Empirical tests indicate that RBF neural network optimized by AFSA can have ideal result in short-term forecast of stock indices.
Starting Page	139
Ending Page	142
File Size	449015
Page Count	4
File Format	PDF
ISBN	9781424474752
e-ISBN	9781424474783
DOI	10.1109/ICCSNA.2010.5588669
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-29
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptation model Stock Index Forecast Variable speed drives Predictive models Fish Swarm Mathematical model Sun Radial Basis Function Neural Network Equations Swarm Intelligence
Content Type	Text
Resource Type	Article

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