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Pronóstico de los índices accionarios DAX y S&P 500 con redes neuronales diferenciales

Author

Listed:
  • Ortíz Arango Francisco

    (Universidad Panamericana)

  • Cabrera Llanos Agustín Ignacio

    (Instituto Politécnico Nacional)

  • López Herrera Francisco

    (Universidad Nacional Autonoma de México)

Abstract

En este trabajo se utiliza una red neuronal diferencial (RND) para describir las series de valores de cierre diarios de los índices accionarios DAX de Alemania y S&P 500 de Estados Unidos entre el periodo del 3 de julio de 2000 y el 13 de enero de 2012. Con la RND se lleva a cabo el pronóstico de los valores de cierre diarios de esos índices durante un periodo de cuatro semanas (del 16 de enero al 10 de febrero de 2012). Los resultados obtenidos confirman el hecho de que las redes neuronales diferenciales pueden constituirse en una de las herramientas más poderosas y precisas para poder pronosticar valores futuros de activos financieros.

Suggested Citation

  • Ortíz Arango Francisco & Cabrera Llanos Agustín Ignacio & López Herrera Francisco, 2013. "Pronóstico de los índices accionarios DAX y S&P 500 con redes neuronales diferenciales," Contaduría y Administración, Accounting and Management, vol. 58(3), pages 203-225, julio-sep.
    • Handle: RePEc:nax:conyad:v:58:y:2013:i:3:p:203-225
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    References listed on IDEAS

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    1. Hutchinson, James M & Lo, Andrew W & Poggio, Tomaso, 1994. "A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks," Journal of Finance, American Finance Association, vol. 49(3), pages 851-889, July.
    2. Lean Yu & Shouyang Wang & Kin Keung Lai, 2007. "Foreign-Exchange-Rate Forecasting With Artificial Neural Networks," International Series in Operations Research and Management Science, Springer, number 978-0-387-71720-3, September.
    3. McNelis, Paul D., 2004. "Neural Networks in Finance," Elsevier Monographs, Elsevier, edition 1, number 9780124859678.
    4. Jagielska, Ilona & Jaworski, Janusz, 1996. "Neural Network for Predicting the Performance of Credit Card Accounts," Computational Economics, Springer;Society for Computational Economics, vol. 9(1), pages 77-82, February.
    5. Kar Yan Tam & Melody Y. Kiang, 1992. "Managerial Applications of Neural Networks: The Case of Bank Failure Predictions," Management Science, INFORMS, vol. 38(7), pages 926-947, July.
    6. G P Zhang & V L Berardi, 2001. "Time series forecasting with neural network ensembles: an application for exchange rate prediction," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(6), pages 652-664, June.
    7. Nikola Gradojevic & Ramazan Gencay & Dragan Kukolj, 2009. "Option Pricing with Modular Neural Networks," Working Paper series 32_09, Rimini Centre for Economic Analysis.
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