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

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 (RBFN...

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Vydáno v:	Knowledge-based systems Ročník 24; číslo 3; s. 378 - 385
Hlavní autoři:	Shen, Wei, Guo, Xiaopen, Wu, Chao, Wu, Desheng
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 01.04.2011
Témata:	Artificial fish swarm algorithm Data mining K-means clustering algorithm Radial basis function neural network Shanghai Stock Exchange Index K-means clustering algorithm Shanghai Stock Exchange Index Artificial fish swarm algorithm Data mining Radial basis function neural network
ISSN:	0950-7051, 1872-7409
On-line přístup:	Získat plný text
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Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23
ISSN:	0950-7051 1872-7409
DOI:	10.1016/j.knosys.2010.11.001