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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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  • Parisi, Antonino & Parisi, Franco & Díaz, David, 2008. "Forecasting gold price changes: Rolling and recursive neural network models," Journal of Multinational Financial Management, Elsevier, vol. 18(5), pages 477-487, December.
  • Handle: RePEc:eee:mulfin:v:18:y:2008:i:5:p:477-487

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    References listed on IDEAS

    1. 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.
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    3. Qi, Min, 2001. "Predicting US recessions with leading indicators via neural network models," International Journal of Forecasting, Elsevier, vol. 17(3), pages 383-401.
    4. 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.
    5. Sam Mirmirani & H.C. Li, 2004. "Gold Price, Neural Networks and Genetic Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 23(2), pages 193-200, March.
    6. 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.
    7. 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.
    8. McMillan, David G., 2005. "Non-linear dynamics in international stock market returns," Review of Financial Economics, Elsevier, vol. 14(1), pages 81-91.
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    1. repec:wsi:ijitdm:v:16:y:2017:i:01:n:s0219622016500504 is not listed on IDEAS
    2. repec:wsi:apjorx:v:34:y:2017:i:05:n:s0217595917500208 is not listed on IDEAS
    3. Ruan, Qingsong & Huang, Ying & Jiang, Wei, 2016. "The exceedance and cross-correlations between the gold spot and futures markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 463(C), pages 139-151.
    4. Ntim, Collins G. & English, John & Nwachukwu, Jacinta & Wang, Yan, 2015. "On the efficiency of the global gold markets," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 218-236.
    5. Zhao, Ze & Wang, Jianzhou & Zhao, Jing & Su, Zhongyue, 2012. "Using a Grey model optimized by Differential Evolution algorithm to forecast the per capita annual net income of rural households in China," Omega, Elsevier, vol. 40(5), pages 525-532.
    6. Xian, Lu & He, Kaijian & Lai, Kin Keung, 2016. "Gold price analysis based on ensemble empirical model decomposition and independent component analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 454(C), pages 11-23.
    7. Gutiérrez, Martha & Franco, Giovanni & Campuzano, Carlos, 2013. "Gold prices: Analyzing its cyclical behavior," REVISTA LECTURAS DE ECONOMÍA, UNIVERSIDAD DE ANTIOQUIA - CIE, issue 79, pages 113-142, September.
    8. Shafiee, Shahriar & Topal, Erkan, 2010. "An overview of global gold market and gold price forecasting," Resources Policy, Elsevier, vol. 35(3), pages 178-189, September.

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