[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/ibn/ijbmjn/v13y2018i4p57.html
   My bibliography  Save this article

Using Prior Payment Behavior Variables for Small Enterprise Default Prediction Modelling

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.ccsenet.org/journal/index.php/ijbm/article/download/73976/40931
    Download Restriction: no
    File URL: http://www.ccsenet.org/journal/index.php/ijbm/article/view/73976
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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..
    9. 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.
    10. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Edward I. Altman & Marco Balzano & Alessandro Giannozzi & Stjepan Srhoj, 2023. "Revisiting SME default predictors: The Omega Score," Journal of Small Business Management, Taylor & Francis Journals, vol. 61(6), pages 2383-2417, November.
    3. Keijo Kohv & Oliver Lukason, 2021. "What Best Predicts Corporate Bank Loan Defaults? An Analysis of Three Different Variable Domains," Risks, MDPI, vol. 9(2), pages 1-19, January.
    4. fernández, María t. Tascón & gutiérrez, Francisco J. Castaño, 2012. "Variables y Modelos Para La Identificación y Predicción Del Fracaso Empresarial: Revisión de La Investigación Empírica Reciente," Revista de Contabilidad - Spanish Accounting Review, Elsevier, vol. 15(1), pages 7-58.
    5. Pranith K. Roy & Krishnendu Shaw, 2023. "A credit scoring model for SMEs using AHP and TOPSIS," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 372-391, January.
    6. Serrano-Cinca, Carlos & Gutiérrez-Nieto, Begoña & Bernate-Valbuena, Martha, 2019. "The use of accounting anomalies indicators to predict business failure," European Management Journal, Elsevier, vol. 37(3), pages 353-375.
    7. David Veganzones, 2022. "Corporate failure prediction using threshold‐based models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 956-979, August.
    8. Frieda Rikkers & Andre E. Thibeault, 2009. "A Structural form Default Prediction Model for SMEs, Evidence from the Dutch Market," Multinational Finance Journal, Multinational Finance Journal, vol. 13(3-4), pages 229-264, September.
    9. John Nkwoma Inekwe, 2016. "Financial Distress, Employees’ Welfare and Entrepreneurship Among SMEs," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 129(3), pages 1135-1153, December.
    10. Hui Hu & Milind Sathye, 2015. "Predicting Financial Distress in the Hong Kong Growth Enterprises Market from the Perspective of Financial Sustainability," Sustainability, MDPI, vol. 7(2), pages 1-15, January.
    11. Rassoul Yazdipour & Richard Constand, 2010. "Predicting Firm Failure: A Behavioral Finance Perspective," Journal of Entrepreneurial Finance, Pepperdine University, Graziadio School of Business and Management, vol. 14(3), pages 90-104, Fall.
    12. Kaya, Orcun, 2022. "Determinants and consequences of SME insolvency risk during the pandemic," Economic Modelling, Elsevier, vol. 115(C).
    13. Erkki Laitinen, 2011. "Assessing viability of Finnish reorganization and bankruptcy firms," European Journal of Law and Economics, Springer, vol. 31(2), pages 167-198, April.
    14. Oliver Lukason & Germo Valgenberg, 2021. "Failure Prediction in the Condition of Information Asymmetry: Tax Arrears as a Substitute When Financial Ratios Are Outdated," JRFM, MDPI, vol. 14(10), pages 1-13, October.
    15. Ana Paula Matias Gama & Helena Susana Amaral Geraldes, 2012. "Credit risk assessment and the impact of the New Basel Capital Accord on small and medium‐sized enterprises," Management Research Review, Emerald Group Publishing Limited, vol. 35(8), pages 727-749, July.
    16. Cathcart, Lara & Dufour, Alfonso & Rossi, Ludovico & Varotto, Simone, 2020. "The differential impact of leverage on the default risk of small and large firms," Journal of Corporate Finance, Elsevier, vol. 60(C).
    17. Joël Bessis, 2009. "Risk Management in Banking," Post-Print hal-00494876, HAL.
    18. Simon Cornée, 2014. "Soft Information and Default Prediction in Cooperative and Social Banks," Journal of Entrepreneurial and Organizational Diversity, European Research Institute on Cooperative and Social Enterprises, vol. 3(1), pages 89-103, June.
    19. Lin, Hsiou-Wei William & Lo, Huai-Chun & Wu, Ruei-Shian, 2016. "Modeling default prediction with earnings management," Pacific-Basin Finance Journal, Elsevier, vol. 40(PB), pages 306-322.
    20. Chiara Pederzoli & Grid Thoma & Costanza Torricelli, 2013. "Modelling Credit Risk for Innovative SMEs: the Role of Innovation Measures," Journal of Financial Services Research, Springer;Western Finance Association, vol. 44(1), pages 111-129, August.

    More about this item

    JEL classification:

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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ibn:ijbmjn:v:13:y:2018:i:4:p:57. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Canadian Center of Science and Education (email available below). General contact details of provider: https://edirc.repec.org/data/cepflch.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.