An analysis of a hybrid neural network and pattern recognition technique for predicting short-term increases in the New York Stock Exchange composite index

We introduce a method for combining template matching, form pattern recognition, and the feed-forward neural network, from artificial intelligence, to forecast stock market activity. We evaluate the effectiveness of the method for forecasting increases in the New York Stock Exchange Composite Index at a 5 trading day horizon. Results indicate that the technique is capable of returning results that are superior to those attained by random choice.