Learning experiments with genetic optimization of a generalized regression neural network
Decision Support Systems - Special double issue: unified programming
Neural network applications in finance: a review and analysis of literature (1990-1996)
Information and Management
Adaptive Learning by Genetic Algorithms: Analytical Results and Applications to Economic Models
Adaptive Learning by Genetic Algorithms: Analytical Results and Applications to Economic Models
Neural Networks: Theoretical Foundations and Analysis
Neural Networks: Theoretical Foundations and Analysis
Fusion of Neural Networks, Fuzzy Sets, and Genetic Algorithms: Industrial Applications
Fusion of Neural Networks, Fuzzy Sets, and Genetic Algorithms: Industrial Applications
Genetic Synthesis of Modular Neural Networks
Proceedings of the 5th International Conference on Genetic Algorithms
Training Kohonen Feature Maps in Different Topologies: An Analysis Using Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Adaptive signal processing of asset price dynamics with predictability analysis
Information Sciences: an International Journal
Comparison of Stochastic Global Optimization Methods to Estimate Neural Network Weights
Neural Processing Letters
Using neural networks as a support tool in the decision making for insurance industry
Expert Systems with Applications: An International Journal
A neuro-computational intelligence analysis of the ecological footprint of nations
Computational Statistics & Data Analysis
Recognition of Western style musical genres using machine learning techniques
Expert Systems with Applications: An International Journal
Clustering the ecological footprint of nations using Kohonen's self-organizing maps
Expert Systems with Applications: An International Journal
A neuro-computational intelligence analysis of the global consumer software piracy rates
Expert Systems with Applications: An International Journal
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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.