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Stock price pattern recognition-a recurrent neural network approach

Recurrent neural networks were applied to the recognition of stock patterns, and a method for evaluating the networks was developed. In stock trading, triangle patterns indicate an important clue to the trend of future change in stock prices, but the patterns are not clearly defined by rule-based approaches. From stock-price data for all names of corporations listed in the first section of the Tokyo Stock Exchange, an expert called chart reader extracted 16 triangles. These patterns were divided into two groups, 15 training patterns and one test pattern. Using stock data from the past three years for 16 names, 16 recognition experiments in which the groups were cyclically used were carried out. The experiments revealed that the given test triangle was accurately recognized in 15 out of 16 experiments and that the number of the mismatching patterns was 1.06 per name on the average. A method was developed for evaluating recurrent networks with context transition performances, particularly temporal transition performances. The method for the triangle sequences is applicable to reducing mismatching patterns