Progress report: improving the stock price forecasting performance of the bull flag heuristic with genetic algorithms and neural networks

Authors:
William Leigh;Edwin Odisho;Noemi Paz;Mario Paz
Affiliations:
-;-;-;-
Venue:
IEA/AIE '00 Proceedings of the 13th international conference on Industrial and engineering applications of artificial intelligence and expert systems: Intelligent problem solving: methodologies and approaches
Year:
2000

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Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks

IEEE Transactions on Neural Networks

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Abstract

We back-test a pattern-based heuristic from stock market technical analysis on price and volume time series data for Alcoa Aluminum Company's common stock. Promising results are obtained using a pattern matching approach implemented with spreadsheet technology. Improvement in these results are attained through the application of neural networks and genetic algorithms. Results are confirmed statistically.