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A Review of Artificial Neural Networks: How Well Do They Perform in Forecasting Time Series?

  • Elsy Gómez-Ramos


    (Escuela Superior de Economía, Instituto Politécnico Nacional, D. F. México, México)

  • Francisco Venegas-Martínez


    (Escuela Superior de Economía, Instituto Politécnico Nacional, D. F. México, México)

At the beginning of the 90’s, Artificial Neural Networks (ANNs) started their applications in finance. The ANNs are data-drive, self-adaptive and non-linear methods that do not require specific assumptions about the underlying model. In general, there are five groups of networks used as forecasting tools: 1) Feedforward Networks, like the Multilayer Perceptron (MLP), 2) Recurrent Networks, 3) Polynomial Networks, 4) Modular Networks, and 5) Support Vector Machine. This paper carries out a review of the specialized literature on ANNs and makes a comparative analysis according to their performance in forecasting stock indices and exchange rates. The objective is to assess the performance when applying different types of networks in relation to MLP. It is shown that the MLP is the best network in forecasting time series. However, it is shown that the MLP has important delimitations in several respects: network architecture, basic functions and initialization weights.

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Article provided by Analítika - Revista de Análisis Estadístico/Journal of Statistical Analysis in its journal Analítika.

Volume (Year): 6 (2013)
Issue (Month): 2 (Diciembre)
Pages: 7-15

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Handle: RePEc:inp:inpana:v:6:y:2013:i:2:p:7-15
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  1. Khurshid Kiani & Terry Kastens, 2008. "Testing Forecast Accuracy of Foreign Exchange Rates: Predictions from Feed Forward and Various Recurrent Neural Network Architectures," Computational Economics, Society for Computational Economics, vol. 32(4), pages 383-406, November.
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  4. Perez-Rodriguez, Jorge V. & Torra, Salvador & Andrada-Felix, Julian, 2005. "STAR and ANN models: forecasting performance on the Spanish "Ibex-35" stock index," Journal of Empirical Finance, Elsevier, vol. 12(3), pages 490-509, June.
  5. 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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