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Modelización y predicción de series de tiempo financieras utilizando redes neuronales

Author

Listed:
  • Hugo Roberto Balacco

    (Facultad de Ciencias Económicas, Universidad Nacional de Cuyo)

  • Gustavo Germán Maradona

    (Facultad de Ciencias Económicas, Universidad Nacional de Cuyo)

Abstract

The purpose of this work is to model and predict Financials Time Series by using neural networks. In order to achieve this aim, a recurrent total neural network with two hidden layers has been chosen; one layer for the linear threshold function and the other for the arctangent function. The series used in this research paper are the MERVAL index (Argentina) and the DOW JONES (USA). These results are based on information obtained over a period that goes from 1995 to 2006. The presentation will deal with the comparison of alternative techniques and the results obtained by other research workers.

Suggested Citation

  • Hugo Roberto Balacco & Gustavo Germán Maradona, 2011. "Modelización y predicción de series de tiempo financieras utilizando redes neuronales," Económica, Departamento de Economía, Facultad de Ciencias Económicas, Universidad Nacional de La Plata, vol. 0, pages 3-23, January-D.
    • Handle: RePEc:lap:journl:574
    as

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    File URL: http://economica.econo.unlp.edu.ar/documentos/20111229092452AM_Economica_574.pdf
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    References listed on IDEAS

    as
    1. Leung, Mark T. & Daouk, Hazem & Chen, An-Sing, 2000. "Forecasting stock indices: a comparison of classification and level estimation models," International Journal of Forecasting, Elsevier, vol. 16(2), pages 173-190.
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    More about this item

    Keywords

    Neural Network; Forecast; Architecture Types; Transfer Functions; Mean Absolute Error.;
    All these keywords.

    JEL classification:

    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General

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