Abstract
A new approach for forecasting stock index based on Multi Expression Neural Network (MENN) is proposed in this paper. The approach employs the multi expression programming (MEP) to evolve the architecture of the MENN and the particle swarm optimization (PSO) to optimize the parameters encoded in the MENN. This framework allows input variables selection, over-layer connections for the various nodes involved. The performance and effectiveness of the proposed method are evaluated using stock market forecasting problems and compared with the related methods. © 2008 Springer-Verlag Berlin Heidelberg.
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CITATION STYLE
Jia, G., Chen, Y., & Wu, P. (2008). MENN method applications for stock market forecasting. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5263 LNCS, pp. 30–39). Springer Verlag. https://doi.org/10.1007/978-3-540-87732-5_4
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