Gold Price, Neural Networks and Genetic Algorithm

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.

[1]  W. Arthur,et al.  Increasing Returns and Path Dependence in the Economy , 1996 .

[2]  Ying-Wong Cheung,et al.  Do Gold Market Returns Have Long Memory , 1993 .

[3]  Chin Kuo,et al.  Neural Networks vs. Conventional Methods of Forecasting , 1996 .

[4]  W. Torous,et al.  Gold and the “weekend effect” , 1982 .

[5]  Brian D. Ripley,et al.  Statistical aspects of neural networks , 1993 .

[6]  J. Holland,et al.  Artificial Adaptive Agents in Economic Theory , 1991 .

[7]  Fred R. Kaen,et al.  PERSISTENT DEPENDENCE IN GOLD PRICES , 1982 .

[8]  Andrew B. Whinston,et al.  Advances in artificial intelligence in economics, finance, and management , 1994 .

[9]  Bo K. Wong,et al.  Neural network applications in finance: A review and analysis of literature (1990-1996) , 1998, Inf. Manag..

[10]  G. Grudnitski,et al.  Forecasting S&P and gold futures prices: An application of neural networks , 1993 .

[11]  Alice E. Smith,et al.  COST ESTIMATION PREDICTIVE MODELING: REGRESSION VERSUS NEURAL NETWORK , 1997 .

[12]  K. Finchem.  NEURAL NETWORK TECHNOLOGY MOVES TOWARD MAINSTREAM CONTROL USE , 1998 .

[13]  Lakhmi C. Jain,et al.  Fusion of Neural Networks, Fuzzy Sets, and Genetic Algorithms: Industrial Applications , 1998 .

[14]  Daniel Polani,et al.  Training Kohonen Feature Maps in Different Topologies: An Analysis Using Genetic Algorithms , 1993, ICGA.

[15]  James W. Denton,et al.  How Good Are Neural Networks for Causal Forecasting , 1995 .

[16]  Frédéric Gruau,et al.  Genetic Synthesis of Modular Neural Networks , 1993, ICGA.

[17]  James V. Hansen,et al.  Learning experiments with genetic optimization of a generalized regression neural network , 1996, Decis. Support Syst..

[18]  How Useful Are Leading Indicators of Inflation , 1995 .

[19]  Michael Y. Hu,et al.  Neural network forecasting of the British pound/US dol-lar exchange rate , 1998 .

[20]  Clifford Lau,et al.  Neural Networks: Theoretical Foundations and Analysis , 1991 .

[21]  Herbert Dawid,et al.  Adaptive Learning by Genetic Algorithms, Analytical Results and Applications to Economic Models, 2nd extended and revised edition , 1999 .

[22]  G. Geoffrey Booth,et al.  Conditional Dependence in Precious Metal Prices , 1991 .

[23]  Herbert Dawid,et al.  Adaptive Learning by Genetic Algorithms , 1996 .

[24]  D. M. Titterington,et al.  Neural Networks: A Review from a Statistical Perspective , 1994 .