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

Author

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  • Adriano Beluco

    (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)

  • Denise L. Bandeira

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

  • Alexandre Beluco

    (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)

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%.

Suggested Citation

  • Adriano Beluco & Denise L. Bandeira & Alexandre Beluco, 2017. "Modeling NYSE Composite US 100 Index with a Hybrid SOM and MLP-BP Neural Model," JRFM, MDPI, vol. 10(1), pages 1-13, February.
    • Handle: RePEc:gam:jjrfmx:v:10:y:2017:i:1:p:6-:d:89445
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    References listed on IDEAS

    as
    1. Leonidas Sandoval Junior & Asher Mullokandov & Dror Y. Kenett, 2015. "Dependency Relations among International Stock Market Indices," JRFM, MDPI, vol. 8(2), pages 1-39, May.
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