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Progress Report: Improving the Stock Price Forecasting Performance of the Bull Flag Heuristic with Genetic Algorithms and Neural Networks

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Intelligent Problem Solving. Methodologies and Approaches (IEA/AIE 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1821))

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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.

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Author information

Authors and Affiliations

  1. Department of MIS, University of Central Florida, Orlando, FL

    William Leigh, Edwin Odisho & Noemi Paz

  2. Department of Civil Engineering, University of Louisville, Louisville, Kentucky

    Mario Paz

Authors
  1. William Leigh
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  2. Edwin Odisho
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  3. Noemi Paz
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  4. Mario Paz
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Editor information

Editors and Affiliations

  1. The Center for Advanced Computer Studies, University of Lousiana, 2 Rex Street, Lafayette, LA, 70504-4330, USA

    Rasiah Logananthara

  2. Department of Neural Information Processing, University of Ulm, Oberer Eselsberg, 89069, Ulm, Germany

    Günther Palm

  3. Department of Computer Science, Southwest Texas State University, 601 University Drive, San Marcos, TX, 78666-4616, USA

    Moonis Ali

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© 2000 Springer-Verlag Berlin Heidelberg

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Leigh, W., Odisho, E., Paz, N., Paz, M. (2000). Progress Report: Improving the Stock Price Forecasting Performance of the Bull Flag Heuristic with Genetic Algorithms and Neural Networks. In: Logananthara, R., Palm, G., Ali, M. (eds) Intelligent Problem Solving. Methodologies and Approaches. IEA/AIE 2000. Lecture Notes in Computer Science(), vol 1821. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45049-1_74

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  • DOI: https://doi.org/10.1007/3-540-45049-1_74

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67689-8

  • Online ISBN: 978-3-540-45049-8

  • eBook Packages: Springer Book Archive

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