Computational Economics

, Volume 23, Issue 2, pp 193–200 | Cite as

Gold Price, Neural Networks and Genetic Algorithm

  • Sam Mirmirani
  • H.C. Li
Article

Abstract

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 

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References

  1. Akgiray, Vedat G., Booth, Geoffrey, Hatem, John J. and Chowdhury, Mustafa (1991). Conditional dependence in precious metal prices. The Financial Review, 26 (3), 367–386.Google Scholar
  2. Brian, Arthur, W. (1994). Increasing Returns and Path Dependence in the Economy, The University of Michigan Press, Ann Arbor.Google Scholar
  3. Ball, C., Torous, W. and Tschogel, A. (1982). Gold and the 'weekend effect'. Journal of Futures Markets, 2 (2), 175–182.Google Scholar
  4. Bode, Jurgen (1998). Neural networks for cost estimation. Cost Engineering, 40 (1), 25–30, January.Google Scholar
  5. Booth, G., Kaen, F. and Koveos, P. (1982). Persistent dependence in gold prices. Journal of Financial Research, 5 (1), 85–93.Google Scholar
  6. Cheng, Bing and Titterington, D.M. (1994). Neural networks: A review from a statistical perspective. Statistical Science, 9 (1), 2–30.Google Scholar
  7. Cheung, Yin-Wong and Lai, Kon S. (1993). Do gold market returns have long memory? Financial Review, 28 (2), 181–202.Google Scholar
  8. Dawid, Herbert (1996). Adaptive Learning by Genetic Algorithms, Springer, Berlin.Google Scholar
  9. Denton, James, W. (1995). How good are neural networks for causal forecasting? The Journal of Business Forecasting Methods & Systems, 14 (2), 17–23, Summer.Google Scholar
  10. Garner, C. Alan (1995). How useful are leading indicators of inflation? Economic Review (Federal Reserve Bank of Kansas City), 80 (2), 2–7, Second Quarter.Google Scholar
  11. Gruau, Frederic (1993). Genetic synthesis of modular neural networks. In S. Forrest, (ed.), Proceedings of the Fifth International Conference on Genetic Algorithms, 318–325. Morgan Kaufmann, San Mateo, CA.Google Scholar
  12. Grudnitski, Gary and Osburn, Larry (1993). Forecasting S&P and gold futures prices: An application of neural networks. The Journal of Futures Markets, 13 (6), 631–644.Google Scholar
  13. Hansen, James V. and Meservy, Rayman D. (1996). Learning with the genetic optimization of a generalized regression neural network. Decision Support Systems, 18, 317–325.Google Scholar
  14. Haubrich, Joseph G. (1998). Gold prices. Economic Commentary, Federal Reserve bank of Cleveland, 1–4, March l.Google Scholar
  15. Holland, John H. and Miller, John H. (1991). Artificial adaptive agents in economic theory. American Economic Review, 81 (2), 365–370, May.Google Scholar
  16. Jain, Lakhmi C. and Martin, N.M. (1999). Fusion of Neural Networks, Fuzzy Sets, and Genetic Algorithms, Industrial Applications, CRC Press, Boca Raton, FL.Google Scholar
  17. Kinchem, Kirk (1998). Neural network technology moves toward mainstream control use. Pulp & Paper, 72 (4), 63–68, April.Google Scholar
  18. Koeing, Evan F. (1996). Should high prices of gold be of concern? The Southwest Economy, Federal Reserve Bank of Dallas, 4, 6–9.Google Scholar
  19. Kuo, Chin and Reitsch, A. (1995/1996). Neural networks vs. conventional methods of forecasting. The Journal of Business Forecasting Methods & Systems, 14 (4), 17–25, Winter.Google Scholar
  20. Lau, Clifford (1992). Neural Networks: Theoretical Foundations and Analysis. The IEEE Press, New York.Google Scholar
  21. Polani, Daniel and Uthmann, Thomas (1993). Training kohonen feature maps in different topologies: An analysis using genetic algorithms. In S. Forrest (ed.), Proceedings of the Fifth International Conference on Genetic Algorithms, 326–333. Morgan Kaufmann, San Mateo, CA.Google Scholar
  22. Ripley, Brian D. (1993). Statistical aspects of neural networks. In J. Barndorff-Nielsen, L. Jensen and W.S. Kendall (eds.), Networks and Chaos-Statistical and Probabilistic Aspects, 40–123. Chapman & Hall, London. pp.Google Scholar
  23. Smith, A.E. and Mason, A. (1997). Cost estimation predictive modeling: Regression versus neural network. The Engineering Economist, 42 (2), 137–161, Winter.Google Scholar
  24. Whinston, A. and Johnson, J. (eds.) (1996). Advances in Artificial Intelligence in Economics, Finance and Management, JAI Press, Greenwich, CT.Google Scholar
  25. Wong, Bo K. and Yakup, Selvi (1998). Neural network applications in finance: A review and analysis of literature (1990-1996). Information and Management, 129–139, October.Google Scholar
  26. Zhang, Gioqinang and Hu, Michael (1998). Neural network forecasting of the British pound/U.S. dollar exchange rate. Omega, 495–506, August.Google Scholar

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.

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