J. Risk Financial Manag. 2017, 10(1), 6; https://doi.org/10.3390/jrfm10010006
Modeling NYSE Composite US 100 Index with a Hybrid SOM and MLP-BP Neural Model
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
2
Universidade Federal do Rio Grande do Sul (UFRGS), Escola de Administração, Rua Washington Luiz, 855, Centro Histórico, 90010-460, Porto Alegre, RS, Brazil
3
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
*
Author to whom correspondence should be addressed.
Academic Editor: Michael McAleer
Received: 22 August 2016 / Revised: 18 January 2017 / Accepted: 19 January 2017 / Published: 5 February 2017
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-TextKeywords:
modeling financial indicators; NYSE indexes; self organizing maps; multilayer perceptron; back propagation algorithm; software Matlab
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Beluco, A.; Bandeira, D.L.; Beluco, A. Modeling NYSE Composite US 100 Index with a Hybrid SOM and MLP-BP Neural Model. J. Risk Financial Manag. 2017, 10, 6.
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J. Risk Financial Manag.
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