Title
Progress Report: Improving The Stock Price Forecasting Performance Of The Bull Flag Heuristic With Genetic Algorithms And Neural Networks
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.
Publication Date
1-1-2000
Publication Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume
1821
Number of Pages
617-622
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
DOI Link
https://doi.org/10.1007/3-540-45049-1_74
Copyright Status
Unknown
Socpus ID
84957881950 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/84957881950
STARS Citation
Leigh, William; Odisho, Edwin; and Paz, Noemi, "Progress Report: Improving The Stock Price Forecasting Performance Of The Bull Flag Heuristic With Genetic Algorithms And Neural Networks" (2000). Scopus Export 2000s. 941.
https://stars.library.ucf.edu/scopus2000/941