A neural network that explains as well as predicts financial market behavior

When a neural network makes a financial prediction, the user may benefit from knowing which previous time periods are illustrative of the current time period. The authors describe a high-performance neural network that in addition to predicting stock market direction, allows the user to visualize the relationship between current conditions and previous conditions that led to similar predictions. Visualization is accomplished by forming a gated multi-expert network using funnel-shaped multilayer dimensionality reduction networks. The neck of the funnel is a two-neuron layer that displays the training data and the decision boundaries in a two-dimensional space. This architecture facilitates a) interactive design of the decision functions and b) explanation of the relevance of past decisions from the training set to the current decision. They describe a stock market prediction system whose design incorporates a visual neural network for prediction, wavelet transforms and tapped delay lines for feature extraction, and a genetic algorithm for feature selection. This system shows that the visual neural network provides the low error rates (i.e., accurate predictions) of multi-expert networks along with the visual explanatory power of nonlinear dimensionality reduction