Title
Progress report: Improving the stock price forecasting performance of the bull flag heuristic with genetic algorithms and neural networks
Keywords
Computer Science, Artificial Intelligence
Abstract
We back-test a patten-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.
Journal Title
Intelligent Problem Solving: Methodologies and Approaches, Prodeedings
Volume
1821
Publication Date
1-1-2000
Document Type
Article
Language
English
First Page
617
Last Page
622
WOS Identifier
ISSN
0302-9743; 3-540-67689-9
Recommended Citation
"Progress report: Improving the stock price forecasting performance of the bull flag heuristic with genetic algorithms and neural networks" (2000). Faculty Bibliography 2000s. 2669.
https://stars.library.ucf.edu/facultybib2000/2669
Comments
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