Neural Processing Letters

, Volume 31, Issue 3, pp 195–217 | Cite as

A Hybrid Intelligent Morphological Approach for Stock Market Forecasting



In this paper, a hybrid intelligent morphological approach is presented for stock market forecasting. It consists of a hybrid intelligent model composed of a Modular Morphological Neural Network (MMNN) and a Modified Genetic Algorithm (MGA), which searches for the minimum number of time lags for a correct time series representation, as well as by the initial weights, architecture and number of modules of the MMNN. Each element of the MGA population is trained via Back Propagation (BP) algorithm to further improve the parameters supplied by the MGA. Initially, the proposed method chooses the most tuned prediction model for time series representation, then it performs a behavioral statistical test in the attempt to adjust time phase distortions that appear in financial time series. An experimental analysis is conducted with the proposed method using four real world time series and five well-known performance measurements, demonstrating consistent better performance of this kind of morphological system.


Stock market forecasting Morphological neural networks Genetic algorithms Hybrid intelligent models 


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Copyright information

© Springer Science+Business Media, LLC. 2010

Authors and Affiliations

  1. 1.Information Technology Department[gm]2 Intelligent SystemsCampinasBrazil

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