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

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)...

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Bibliographic Details
Published in2010 Second International Conference on Communication Systems, Networks and Applications Vol. 1; pp. 139 - 142
Main Authors Dongxiao Niu, Wei Shen, Yueshi Sun
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2010
Subjects
Adaptation model
Equations
Fish Swarm
Mathematical model
Predictive models
Radial Basis Function Neural Network
Stock Index Forecast
Swarm Intelligence
Variable speed drives
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ISBN1424474752
9781424474752
DOI10.1109/ICCSNA.2010.5588669

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Summary: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.
ISBN:1424474752
9781424474752
DOI:10.1109/ICCSNA.2010.5588669