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Modelling non‐linearity in economic classification with neural networks

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  • Hennie Daniels
  • Bart Kamp
  • William Verkooijen

Abstract

In this paper results are presented of a study on economic classification with neural networks. Comparison is made between neural networks and linear modelling techniques and, in particular, comments are made on the problem of overfitting and the estimation of prediction errors in cases where the available data sets are relatively small. It is shown that selecting network parameters by k‐fold cross‐validation and weight decay training are effective remedies for these phenomena. The conclusions are illustrated in two cases: predicting the volume of the mortgage market in the Netherlands and the classification of bond ratings. © 1997 John Wiley & Sons, Ltd.

Suggested Citation

  • Hennie Daniels & Bart Kamp & William Verkooijen, 1997. "Modelling non‐linearity in economic classification with neural networks," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 6(4), pages 287-301, December.
    • Handle: RePEc:wly:isacfm:v:6:y:1997:i:4:p:287-301
    DOI: 10.1002/(SICI)1099-1174(199712)6:43.0.CO;2-P
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    Cited by:

    1. James R. Coakley & Carol E. Brown, 2000. "Artificial neural networks in accounting and finance: modeling issues," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 9(2), pages 119-144, June.

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