Pronóstico del Indice de la Bolsa de Colombia usando Redes Neuronales (Forecasting the Colombian Exchange Market Index (IGBC) using Neural Networks)

Adriana Arango (, Juan D. Velásquez (

This paper appears in: Revista IEEE América Latina

Publication Date: June 2014
Volume: 12,   Issue: 4 
ISSN: 1548-0992

In this article, the daily Colombian exchange market index (IGBC) is forecasted using linear models, artificial neural networks and adaptive neuro-fuzzy inference systems with the aim of evaluate the accuracy of the forecasts when nonlinear models are used. In addition, we evaluate the explanatory power of other international market indexes, oil prices and exchange rates. Our findings are the following: first, an autoregressive neural network better captures the behavior of the IGBC in comparison with linear and adaptive neuro-fuzzy models; second, the preferred explanatory variables are able to explain complex properties as heteroskedasticity and non-normality of the residuals. And third, it is necessary consider as inputs not only the explanatory variables alone but also their interactions.

Index Terms:
ANFIS, HyFIS, linear regression, financial prediction, nonlinear models   

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