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Combining Kohonen maps and prior payment behavior for small enterprise default prediction

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  • Francesco Ciampi

    (University of Florence)

  • Valentina Cillo

    (Link Campus University)

  • Fabio Fiano

    (Link Campus University)

Abstract

This study aims to verify the potential of combining corporate prior payment behavior and Kohonen maps for small enterprise default prediction. Logistic regression, discrete-time hazard models, and Kohonen maps were applied to a sample of 1200 Italian small enterprises, and two categories of prediction models were calculated: one exclusively based on financial ratios and the other based also on payment behavior-related variables. The main findings are as follows: (1) Kohonen map-based trajectories give significantly higher prediction accuracy rates compared to both logistic and hazard models; (2) the longer the forecast horizon and/or the smaller the firm’s size, the greater are the improvements in prediction accuracy obtainable through Kohonen maps; (3) accuracy rates are higher when company payment behavior-related variables are added to financial ratios as default predictors; and (4) the smaller a firm, the greater is the increase in accuracy obtainable by adding payment behavior-related variables.

Suggested Citation

  • Francesco Ciampi & Valentina Cillo & Fabio Fiano, 2020. "Combining Kohonen maps and prior payment behavior for small enterprise default prediction," Small Business Economics, Springer, vol. 54(4), pages 1007-1039, April.
  • Handle: RePEc:kap:sbusec:v:54:y:2020:i:4:d:10.1007_s11187-018-0117-2
    DOI: 10.1007/s11187-018-0117-2
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