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

    Authors

    W. Leigh; E. Odisho; N. Paz;M. Paz

    Comments

    Authors: contact us about adding a copy of your work at STARS@ucf.edu

    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

    WOS:000170571100074

    ISSN

    0302-9743; 3-540-67689-9

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