Neural network training techniques for a gold trading model

The purpose of the study was to build and evaluate a decision support system for financial models, incorporating a neural network approach. Since neural network models may employ a variety of different training techniques, the authors have developed an approach to choosing and optimizing the structures and procedures used during training, to optimize the fit between the trained neural network and the financial decision-support goals. They evaluate alternative methods for training a network to forecast gold market prices. Essential to this evaluation is the identification of an appropriate trading model to evaluate system performance, without constraining the details of the financial decisions that can be made with the resulting trained neural network. The methods explored were neural network models using both standard and non-standard training techniques, and varying parameters used during weight adjustment and in the training schedule. They illustrate techniques for choosing network structure and inputs, data segmentation, error measures for training, error measures for validation, and optimizing validation set lengths. These techniques are applied with respect to the aim of forecasting the price of gold