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Using Prior Payment Behavior Variables for Small Enterprise Default Prediction Modelling

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  • Francesco Ciampi

Abstract

This study aims to verify the potential of combining prior payment behavior variables and financial ratios for SE default prediction modelling. Logistic regression was applied to a sample of 980 Italian SEs in order to calculate and compare two categories of default prediction models, one exclusively based on financial ratios and the other based also on company payment behavior related variables. The main findings are- 1) using prior payment behavior variables significantly improves the effectiveness of SE default prediction modelling; ii) the longer the forecast horizon and/or the smaller the size of the firms which are the object of analysis, the higher the improvements in prediction accuracy that can be obtained by using also prior payment behavior variables as default predictors; iii) SE default prediction modelling should be separately implemented for different size groups of firms.

Suggested Citation

  • Francesco Ciampi, 2018. "Using Prior Payment Behavior Variables for Small Enterprise Default Prediction Modelling," International Journal of Business and Management, Canadian Center of Science and Education, vol. 13(4), pages 1-57, March.
    • Handle: RePEc:ibn:ijbmjn:v:13:y:2018:i:4:p:57
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    1. Edward Altman & Gabriele Sabato, 2005. "Effects of the New Basel Capital Accord on Bank Capital Requirements for SMEs," Journal of Financial Services Research, Springer;Western Finance Association, vol. 28(1), pages 15-42, October.
    2. Balcaen, Sofie & Ooghe, Hubert, 2006. "35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems," The British Accounting Review, Elsevier, vol. 38(1), pages 63-93.
    3. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    4. Edward I. Altman & Gabriele Sabato, 2013. "MODELING CREDIT RISK FOR SMEs: EVIDENCE FROM THE US MARKET," World Scientific Book Chapters, in: Oliviero Roggi & Edward I Altman (ed.), Managing and Measuring Risk Emerging Global Standards and Regulations After the Financial Crisis, chapter 9, pages 251-279, World Scientific Publishing Co. Pte. Ltd..
    5. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    6. Altman, Edward I. & Saunders, Anthony, 1997. "Credit risk measurement: Developments over the last 20 years," Journal of Banking & Finance, Elsevier, vol. 21(11-12), pages 1721-1742, December.
    7. Edward I. Altman & Brooks Brady & Andrea Resti & Andrea Sironi, 2005. "The Link between Default and Recovery Rates: Theory, Empirical Evidence, and Implications," The Journal of Business, University of Chicago Press, vol. 78(6), pages 2203-2228, November.
    8. Ciampi, Francesco, 2015. "Corporate governance characteristics and default prediction modeling for small enterprises. An empirical analysis of Italian firms," Journal of Business Research, Elsevier, vol. 68(5), pages 1012-1025.
    9. Altman, Edward I. & Haldeman, Robert G. & Narayanan, P., 1977. "ZETATM analysis A new model to identify bankruptcy risk of corporations," Journal of Banking & Finance, Elsevier, vol. 1(1), pages 29-54, June.
    10. Peter Back, 2005. "Explaining financial difficulties based on previous payment behavior, management background variables and financial ratios," European Accounting Review, Taylor & Francis Journals, vol. 14(4), pages 839-868.
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    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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