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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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    1. Michel Dietsch, 2004. "Should SME exposures be treated as retail or corporate exposures: a comparative analysis of probabilities of default and assets correlations in French and German SMEs," ULB Institutional Repository 2013/14164, ULB -- Universite Libre de Bruxelles.
    2. Tam, KY, 1991. "Neural network models and the prediction of bank bankruptcy," Omega, Elsevier, vol. 19(5), pages 429-445.
    3. Peel, M. J. & Peel, D. A., 1988. "A multilogit approach to predicting corporate failure--Some evidence for the UK corporate sector," Omega, Elsevier, vol. 16(4), pages 309-318.
    4. 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.
    5. Crouhy, Michel & Galai, Dan & Mark, Robert, 2001. "Prototype risk rating system," Journal of Banking & Finance, Elsevier, vol. 25(1), pages 47-95, January.
    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. Zhang, Guoqiang & Y. Hu, Michael & Eddy Patuwo, B. & C. Indro, Daniel, 1999. "Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis," European Journal of Operational Research, Elsevier, vol. 116(1), pages 16-32, July.
    8. du Jardin, Philippe, 2010. "Predicting bankruptcy using neural networks and other classification methods: the influence of variable selection techniques on model accuracy," MPRA Paper 44375, University Library of Munich, Germany.
    9. Grunert, Jens & Norden, Lars & Weber, Martin, 2005. "The role of non-financial factors in internal credit ratings," Journal of Banking & Finance, Elsevier, vol. 29(2), pages 509-531, February.
    10. Teija Laitinen & Maria Kankaanpaa, 1999. "Comparative analysis of failure prediction methods: the Finnish case," European Accounting Review, Taylor & Francis Journals, vol. 8(1), pages 67-92.
    11. Eric Rosenberg & Alan Gleit, 1994. "Quantitative Methods in Credit Management: A Survey," Operations Research, INFORMS, vol. 42(4), pages 589-613, August.
    12. Grice, John Stephen & Ingram, Robert W., 2001. "Tests of the generalizability of Altman's bankruptcy prediction model," Journal of Business Research, Elsevier, vol. 54(1), pages 53-61, October.
    13. Blum, M, 1974. "Failing Company Discriminant-Analysis," Journal of Accounting Research, Wiley Blackwell, vol. 12(1), pages 1-25.
    14. Berger, Allen N & Udell, Gregory F, 1995. "Relationship Lending and Lines of Credit in Small Firm Finance," The Journal of Business, University of Chicago Press, vol. 68(3), pages 351-381, July.
    15. Deakin, Eb, 1972. "Discriminant Analysis Of Predictors Of Business Failure," Journal of Accounting Research, Wiley Blackwell, vol. 10(1), pages 167-179.
    16. Kar Yan Tam & Melody Y. Kiang, 1992. "Managerial Applications of Neural Networks: The Case of Bank Failure Predictions," Management Science, INFORMS, vol. 38(7), pages 926-947, July.
    17. Stefan Buehler & Christian Kaiser & Franz Jaeger, 2012. "The geographic determinants of bankruptcy: evidence from Switzerland," Small Business Economics, Springer, vol. 39(1), pages 231-251, July.
    18. 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.
    19. P. Du Jardin & E. Séverin, 2011. "Predicting Corporate Bankruptcy Using Self-Organising map: An empirical study to Improve the Forecasting horizon of financial failure model," Post-Print hal-00801878, HAL.
    20. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    21. 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.
    22. Manlio Del Giudice, 2011. "Knowledge Management and Family Business," Innovation, Technology, and Knowledge Management, in: Knowledge and the Family Business, chapter 0, pages 11-46, Springer.
    23. Edmister, Robert O., 1972. "An Empirical Test of Financial Ratio Analysis for Small Business Failure Prediction," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 7(2), pages 1477-1493, March.
    24. Pompe, Paul P.M. & Bilderbeek, Jan, 2005. "The prediction of bankruptcy of small- and medium-sized industrial firms," Journal of Business Venturing, Elsevier, vol. 20(6), pages 847-868, November.
    25. 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.
    26. 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.
    27. 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.
    28. Yuta Ogane, 2016. "Banking relationship numbers and new business bankruptcies," Small Business Economics, Springer, vol. 46(2), pages 169-185, February.
    29. 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..
    30. 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.
    31. Lacher, R. C. & Coats, Pamela K. & Sharma, Shanker C. & Fant, L. Franklin, 1995. "A neural network for classifying the financial health of a firm," European Journal of Operational Research, Elsevier, vol. 85(1), pages 53-65, August.
    32. Dambolena, Ismael G & Khoury, Sarkis J, 1980. "Ratio Stability and Corporate Failure," Journal of Finance, American Finance Association, vol. 35(4), pages 1017-1026, September.
    33. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January.
    34. Nicholas Wilson & Barbara Summers & Robert Hope, 2000. "Using Payment Behaviour Data for Credit Risk Modelling," International Journal of the Economics of Business, Taylor & Francis Journals, vol. 7(3), pages 333-346.
    35. 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.
    36. Dietsch, Michel & Petey, Joel, 2004. "Should SME exposures be treated as retail or corporate exposures? A comparative analysis of default probabilities and asset correlations in French and German SMEs," Journal of Banking & Finance, Elsevier, vol. 28(4), pages 773-788, April.
    37. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    38. Glenn Milligan, 1981. "A monte carlo study of thirty internal criterion measures for cluster analysis," Psychometrika, Springer;The Psychometric Society, vol. 46(2), pages 187-199, June.
    39. Traczynski, Jeffrey, 2017. "Firm Default Prediction: A Bayesian Model-Averaging Approach," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(3), pages 1211-1245, June.
    40. Turetsky, Howard F & McEwen, Ruth Ann, 2001. "An Empirical Investigation of Firm Longevity: A Model of the Ex Ante Predictors of Financial Distress," Review of Quantitative Finance and Accounting, Springer, vol. 16(4), pages 323-343, June.
    41. Stewart Thornhill & Raphael Amit, 2003. "Learning About Failure: Bankruptcy, Firm Age, and the Resource-Based View," Organization Science, INFORMS, vol. 14(5), pages 497-509, October.
    42. Pindado, Julio & Rodrigues, Luis & de la Torre, Chabela, 2008. "Estimating financial distress likelihood," Journal of Business Research, Elsevier, vol. 61(9), pages 995-1003, September.
    43. Yuta Ogane, 2016. "Banking relationship numbers and new business bankruptcies," Small Business Economics, Springer, vol. 46(2), pages 169-185, February.
    44. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    45. du Jardin, Philippe, 2015. "Bankruptcy prediction using terminal failure processes," European Journal of Operational Research, Elsevier, vol. 242(1), pages 286-303.
    46. Julio Pindado & Luis F. Rodrigues, 2004. "Parsimonious Models of Financial Insolvency in Small Companies," Small Business Economics, Springer, vol. 22(1), pages 51-66, February.
    47. Donald C. Hambrick & Richard A. D'Aveni, 1992. "Top Team Deterioration as Part of the Downward Spiral of Large Corporate Bankruptcies," Management Science, INFORMS, vol. 38(10), pages 1445-1466, October.
    48. Manlio Del Giudice & Maria Rosaria Della Peruta & Elias G. Carayannis, 2011. "Knowledge and the Family Business," Innovation, Technology, and Knowledge Management, Springer, number 978-1-4419-7353-5, December.
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