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Forecasting gold price changes: Rolling and recursive neural network models

  • Parisi, Antonino
  • Parisi, Franco
  • Díaz, David

This paper analyzes recursive and rolling neural network models to forecast one-step-ahead sign variations in gold price. Different combinations of techniques and sample sizes are studied for feed forward and ward neural networks. The results shows the rolling ward networks exceed the recursive ward networks and feed forward networks in forecasting gold price sign variation. The results support the use of neural networks with a dynamic framework to forecast the gold price sign variations, recalculating the weights of the network on a period-by-period basis, through a rolling process. Our results are validated using the block bootstrap methodology with an average sign prediction of 60.68% with a standard deviation of 2.82% for the rolling ward net.

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Article provided by Elsevier in its journal Journal of Multinational Financial Management.

Volume (Year): 18 (2008)
Issue (Month): 5 (December)
Pages: 477-487

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Handle: RePEc:eee:mulfin:v:18:y:2008:i:5:p:477-487
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  1. Mark T. Leung & An-Sing Chen, 2005. "Performance evaluation of neural network architectures: the case of predicting foreign exchange correlations," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(6), pages 403-420.
  2. Sam Mirmirani & H.C. Li, 2004. "Gold Price, Neural Networks and Genetic Algorithm," Computational Economics, Society for Computational Economics, vol. 23(2), pages 193-200, 03.
  3. Pesaran, M.H. & Timmermann, A., 1990. "A Simple, Non-Parametric Test Of Predictive Performance," Cambridge Working Papers in Economics 9021, Faculty of Economics, University of Cambridge.
  4. Qi, Min, 2001. "Predicting US recessions with leading indicators via neural network models," International Journal of Forecasting, Elsevier, vol. 17(3), pages 383-401.
  5. McMillan, David G., 2005. "Non-linear dynamics in international stock market returns," Review of Financial Economics, Elsevier, vol. 14(1), pages 81-91.
  6. Arturo Estrella & Frederic S. Mishkin, 1998. "Predicting U.S. Recessions: Financial Variables As Leading Indicators," The Review of Economics and Statistics, MIT Press, vol. 80(1), pages 45-61, February.
  7. 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.
  8. Parisi F, Antonino & Parisi F, Franco & Guerrero C., José Luis, 2003. "Modelos predictivos de redes neuronales en índices bursátiles," El Trimestre Económico, Fondo de Cultura Económica, vol. 0(280), pages 721-744, octubre-d.
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