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. © Springer-Verlag Berlin Heidelberg 2007.
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CITATION STYLE
Chen, Y., Wu, Q., & Chen, F. (2007). An IP and GEP based dynamic decision model for stock market forecasting. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4491 LNCS, pp. 473–479). Springer Verlag. https://doi.org/10.1007/978-3-540-72383-7_56
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