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Neural Network Model Selection for Financial Time Series Prediction

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  • Francesco Virili

    (University of Siegen)

  • Bernd Freisleben

    (University of Siegen)

Abstract

Summary Can neural network model selection be guided by statistical procedures such as hypothesis tests, information criteria and cross-validation? Recently, Anders and Korn (1999) proposed five neural network model specification strategies based on different statistical procedures. In this paper, we use and adapt the Anders-Korn framework to find appropriate neural network models for financial time series prediction. The most important new issue in this context is the specification of the dynamic structure of the models, i.e. the selection of the lagged values of the input time series. A linear model is built with full dynamic structure, then its possible nonlinear extensions are tested using a statistical procedure inspired by the Anders-Korn approach. Promising results are obtained with an application to predict the monthly time series of mortgage loans purchased in The Netherlands.

Suggested Citation

  • Francesco Virili & Bernd Freisleben, 2001. "Neural Network Model Selection for Financial Time Series Prediction," Computational Statistics, Springer, vol. 16(3), pages 451-463, September.
    • Handle: RePEc:spr:compst:v:16:y:2001:i:3:d:10.1007_s001800100078
    DOI: 10.1007/s001800100078
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    References listed on IDEAS

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    1. Timo Teräsvirta & Chien‐Fu Lin & Clive W. J. Granger, 1993. "Power Of The Neural Network Linearity Test," Journal of Time Series Analysis, Wiley Blackwell, vol. 14(2), pages 209-220, March.
    2. Norman R. Swanson & Halbert White, 1997. "A Model Selection Approach To Real-Time Macroeconomic Forecasting Using Linear Models And Artificial Neural Networks," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 540-550, November.
    3. Granger, C. W. J. & Newbold, Paul, 1986. "Forecasting Economic Time Series," Elsevier Monographs, Elsevier, edition 2, number 9780122951831 edited by Shell, Karl.
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