Regularidades no lineales en índices accionarios. Una aproximación con redes neuronales

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Publicado: sep 8, 2017
DOI: https://doi.org/10.20430/ete.v72i288.561
Palabras clave:
redes neuronales artificiales, metodología y aplicaciones, mercados accionarios

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Christian A. Johnson
Universidad Adolfo Ibáñez, Santiago, Chile
Miguel A. Padilla
Escuela Superior Politécnica del Litoral (Espol), Guayaquil, Ecuador

Resumen

Las redes neuronales artificiales (RNA) se han convertido en un importante instrumento para modelar y predecir los rendimientos accionarios. Debido a que son modelos que incorporan variables no lineales (característica de la mayoría de las series económicas y financieras) funcionan mejor que los modelos estadísticos tradicionales, como las regresiones lineales o modelos Box-Jenkins. Este estudio intenta encontrar regularidades en los índices accionarios de 27 países mediante un acercamiento de redes neuronales artificiales y su contraste con modelos lineales rezagados, y aporta evidencia a la discusión actual respecto a la teoría de los mercados eficientes. Asimismo se realizan predicciones extramuestrales dinámicas sustentadas también con una prueba no paramétrica, que confirma excelentes resultados de las redes neuronales en contraste con los modelos autorregresivos tradicionales.

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Johnson, C. A., & Padilla, M. A. (2017). Regularidades no lineales en índices accionarios. Una aproximación con redes neuronales. El Trimestre Económico, 72(288), 765–821. https://doi.org/10.20430/ete.v72i288.561
Número
Vol. 72 Núm. 288 (2005): octubre-diciembre
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Artículos

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