Modeling NYSE Composite US 100 Index with a Hybrid SOM and MLP-BP Neural ModelReportar como inadecuado


Modeling NYSE Composite US 100 Index with a Hybrid SOM and MLP-BP Neural Model


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1

Instituto Federal de Educação, Ciência e Tecnologia do Rio Grande do Sul IFRS, Campus Viamão, Av Sen Salgado Filho, 7000, Bairro Sáo Lucas, 94440-000, Viamão, RS, Brazil

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Universidade Federal do Rio Grande do Sul UFRGS, Escola de Administração, Rua Washington Luiz, 855, Centro Histórico, 90010-460, Porto Alegre, RS, Brazil

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Universidade Federal do Rio Grande do Sul UFRGS, Instituto de Pesquisas Hidráulicas IPH, Av Bento Gonçalves, 9500, Bairro Agronomia, 91501-970, Porto Alegre, RS, Brazil





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Author to whom correspondence should be addressed.



Academic Editor: Michael McAleer

Abstract Neural networks are well suited to predict future results of time series for various data types. This paper proposes a hybrid neural network model to describe the results of the database of the New York Stock Exchange NYSE. This hybrid model brings together a self organizing map SOM with a multilayer perceptron with back propagation algorithm MLP-BP. The SOM aims to segment the database into different clusters, where the differences between them are highlighted. The MLP-BP is used to construct a descriptive mathematical model that describes the relationship between the indicators and the closing value of each cluster. The model was developed from a database consisting of the NYSE Composite US 100 Index over the period of 2 April 2004 to 31 December 2015. As input variables for neural networks, ten technical financial indicators were used. The model results were fairly accurate, with a mean absolute percentage error varying between 0.16% and 0.38%. View Full-Text

Keywords: modeling financial indicators; NYSE indexes; self organizing maps; multilayer perceptron; back propagation algorithm; software Matlab modeling financial indicators; NYSE indexes; self organizing maps; multilayer perceptron; back propagation algorithm; software Matlab





Autor: Adriano Beluco 1, Denise L. Bandeira 2 and Alexandre Beluco 3,*

Fuente: http://mdpi.com/



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