Computational Economics

, Volume 23, Issue 2, pp 193–200

Gold Price, Neural Networks and Genetic Algorithm

  • Sam Mirmirani
  • H.C. Li

DOI: 10.1023/B:CSEM.0000021677.46295.60

Cite this article as:
Mirmirani, S. & Li, H. Computational Economics (2004) 23: 193. doi:10.1023/B:CSEM.0000021677.46295.60


Economic theory has failed to provide sufficient explanation of the dynamicpath of price movement over time. Therefore, the use of any linear ornon-linear functional form to model the gold price movement is bound to bearbitrary in nature. Neural Networks equipped with genetic algorithm have theadvantage of simulating the non-linear models when little a priori knowledgeof the structure of problem domains exist. Studies suggest that such a systemprovides better predictions when compared with traditional econometric models.The NeuroGenetic Optimizer software is applied to the NYMEX database of dailygold cash price covering 12/31/1974–12/31/1998 period. Among differentmethods, back-propagation neural networks with genetic algorithms is used topredict gold price movement. The results indicate that prices in the past, upto 36 days, strongly affect the gold prices of the future. This confirms thefact that there is short-term time dependence in gold price movements.

neural networks genetic algorithm gold price 

Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • Sam Mirmirani
    • 1
  • H.C. Li
    • 2
  1. 1.Department of EconomicsBryant CollegeSmithfieldU.S.A.
  2. 2.Department of FinanceBryant CollegeSmithfieldU.S.A.

Personalised recommendations