An IP and GEP Based Dynamic Decision Model for Stock Market Forecasting

Authors:
Yuehui Chen;Qiang Wu;Feng Chen
Affiliations:
School of Information Science and Engineering, University of Jinan, Jinan 250022, P.R. China;School of Information Science and Engineering, University of Jinan, Jinan 250022, P.R. China;School of Information Science and Engineering, University of Jinan, Jinan 250022, P.R. China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Year:
2007

Citing 3
Cited 0

Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms

Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence (Studies in Computational Intelligence)

Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence (Studies in Computational Intelligence)
Immune programming

Information Sciences: an International Journal

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Abstract

The forecasting models for stock market index using computational intelligence such as Artificial Neural networks(ANNs) and Genetic programming(GP), especially hybrid Immune Programming (IP) Algorithm and Gene Expression Programming(GEP) have achieved favorable results. However, these studies, have assumed a static environment. This study investigates the development of a new dynamic decision forecasting model. Application results prove the higher precision and generalization capacity of the predicting model obtained by the new method than static models.