Structural learning of neural networks for forecasting stock prices

Junzo Watada

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Generally, a neural network spends much computation time and cost in forecasting the value and movement of a stock. The reason is because a neural network requires exponential time in computation according to the number of units in a hidden layer. The objective of the paper is to optimally build a neural network through structurally learning. The results enable us to reduce the computational time and cost as well as to understand the structure more easily. In the paper the method is employed in forecasting the price movement of a stock. The optimization of the network by the structured learning is evaluated based on its real use.

    Original languageEnglish
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Pages972-979
    Number of pages8
    Volume4253 LNAI - III
    Publication statusPublished - 2006
    Event10th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2006 - Bournemouth
    Duration: 2006 Oct 92006 Oct 11

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume4253 LNAI - III
    ISSN (Print)03029743
    ISSN (Electronic)16113349

    Other

    Other10th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2006
    CityBournemouth
    Period06/10/906/10/11

    Keywords

    • Forecasting the stock
    • Structural learning

    ASJC Scopus subject areas

    • Computer Science(all)
    • Biochemistry, Genetics and Molecular Biology(all)
    • Theoretical Computer Science

    Fingerprint Dive into the research topics of 'Structural learning of neural networks for forecasting stock prices'. Together they form a unique fingerprint.

  • Cite this

    Watada, J. (2006). Structural learning of neural networks for forecasting stock prices. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4253 LNAI - III, pp. 972-979). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4253 LNAI - III).