Applicability of information criteria for neural network architecture selection

Christian Haefke, Christian Helmenstein

    Research output: Contribution to conferencePaper

    Abstract

    In most of the empirical research on capital markets, stock market indexes are used as proxies for the aggregate market development. In previous work we found that a particular market segment of the Vienna stock exchange might be less efficient than the whole market and hence easier to forecast. Extending the focus of investigation in this paper, we use feedforward networks and linear models to predict the all share index WBI as well as various subindexes covering the highly liquid, semi-liquid, and initial public offering (IPO) market segment. In order to shed some light on network construction principles, we compare different models as selected by hold-out crossvalidation (HCV), Akaike's information criterion (AIC), and Schwartz' information criterion (SIC). The forecasts are subsequently evaluated on the basis of hypothetical trading in the out-of-sample period.

    Original languageEnglish (US)
    Pages293-301
    Number of pages9
    StatePublished - Dec 1 1996
    EventProceedings of the IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering, CIFEr - New York, NY, USA
    Duration: Mar 24 1996Mar 26 1996

    Other

    OtherProceedings of the IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering, CIFEr
    CityNew York, NY, USA
    Period3/24/963/26/96

    Fingerprint

    Network architecture
    Neural networks
    Liquids
    Financial markets
    Market segments
    Information criterion
    Stock exchange
    Capital markets
    Initial public offerings
    Stock market index
    Cross-validation
    Market development
    Akaike information criterion
    Empirical research
    Feedforward networks
    Network model

    ASJC Scopus subject areas

    • Computer Science(all)
    • Economics, Econometrics and Finance(all)
    • Engineering(all)

    Cite this

    Haefke, C., & Helmenstein, C. (1996). Applicability of information criteria for neural network architecture selection. 293-301. Paper presented at Proceedings of the IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering, CIFEr, New York, NY, USA, .

    Applicability of information criteria for neural network architecture selection. / Haefke, Christian; Helmenstein, Christian.

    1996. 293-301 Paper presented at Proceedings of the IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering, CIFEr, New York, NY, USA, .

    Research output: Contribution to conferencePaper

    Haefke, C & Helmenstein, C 1996, 'Applicability of information criteria for neural network architecture selection' Paper presented at Proceedings of the IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering, CIFEr, New York, NY, USA, 3/24/96 - 3/26/96, pp. 293-301.
    Haefke C, Helmenstein C. Applicability of information criteria for neural network architecture selection. 1996. Paper presented at Proceedings of the IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering, CIFEr, New York, NY, USA, .
    Haefke, Christian ; Helmenstein, Christian. / Applicability of information criteria for neural network architecture selection. Paper presented at Proceedings of the IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering, CIFEr, New York, NY, USA, .9 p.
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