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Pronóstico del rendimiento del IPC (Índice de Precios y Cotizaciones)mediante el uso de redes neuronales diferenciales

Author

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  • Cabrera Llanos Agustín Ignacio

    (Instituto Politécnico Nacional)

  • Ortíz Arango Francisco

    (Universidad Panamericana)

Abstract

Over the years the use of artificial neural networks as a tool for simulation, modeling and description of nonlinear dynamical systems has been consolidated as an effective and relatively fast technique thanks to the great development experienced in computer systems. This technique commonly used in some areas of Applied Engineering was first used in financial applications since the mid-nineties. This paper uses one of the most recent and powerful techniques in the field of neural networks: Differential Neural Networks Analysis (DNNA), a technique frequently used in analysis of biotechnology processes. This technique carries out the analysis and estimation of the evolution of behavior in the IPC (Stock Market Index) of the BMV (Mexican Stock Exchange) during the period from November 8, 1999 to January 27, 2011. The analysis also includes an intra-day forecast (6 values into a trading session) of the IPC return, the forecast extends during one day after the last data time series of the IPC. The predicted results showed a great similarity with actual data.

Suggested Citation

  • Cabrera Llanos Agustín Ignacio & Ortíz Arango Francisco, 2012. "Pronóstico del rendimiento del IPC (Índice de Precios y Cotizaciones)mediante el uso de redes neuronales diferenciales," Contaduría y Administración, Accounting and Management, vol. 57(2), pages 63-81, abril-jun.
    • Handle: RePEc:nax:conyad:v:57:y:2012:i:2:p:63-81
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    References listed on IDEAS

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    4. McNelis, Paul D., 2004. "Neural Networks in Finance," Elsevier Monographs, Elsevier, edition 1, number 9780124859678.
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