IDEAS home Printed from https://ideas.repec.org/h/wsi/wschap/9789814417501_0009.html
   My bibliography  Save this book chapter

MODELING CREDIT RISK FOR SMEs: EVIDENCE FROM THE US MARKET

In: Managing and Measuring Risk Emerging Global Standards and Regulations After the Financial Crisis

Author

Listed:
  • Edward I. Altman

    (New York University, USA)

  • Gabriele Sabato

    (Ulster Bank, Ireland)

Abstract

Considering the fundamental role played by small and medium sized enterprises (SMEs) in the economy of many countries and the considerable attention placed on SMEs in the new Basel Capital Accord, we develop a distress prediction model specifically for the SME sector and to analyze its effectiveness compared to a generic corporate model. The behavior of financial measures for SMEs is analyzed and the most significant variables in predicting the entities' credit worthiness are selected in order to construct a default prediction model. Using a logit regression technique on a panel of over 2,000 US firms (with sales less than $65 million) over the period 1994–2002, we develop a one-year default prediction model. This model has an out of sample prediction power which is almost 30 percent higher than a generic corporate model. An associated objective is to observe our model's ability to lower bank capital requirements considering the new Basel Capital Accord's rules for SMEs.

Suggested Citation

  • 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..
    • Handle: RePEc:wsi:wschap:9789814417501_0009
    as

    Download full text from publisher

    File URL: https://www.worldscientific.com/doi/pdf/10.1142/9789814417501_0009
    Download Restriction: Ebook Access is available upon purchase.
    File URL: https://www.worldscientific.com/doi/abs/10.1142/9789814417501_0009
    Download Restriction: Ebook Access is available upon purchase.
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Eisenbeis, Robert A, 1977. "Pitfalls in the Application of Discriminant Analysis in Business, Finance, and Economics," Journal of Finance, American Finance Association, vol. 32(3), pages 875-900, June.
    2. Engelmann, Bernd & Hayden, Evelyn & Tasche, Dirk, 2003. "Measuring the Discriminative Power of Rating Systems," Discussion Paper Series 2: Banking and Financial Studies 2003,01, Deutsche Bundesbank.
    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. En-Der Su & Shih-Ming Huang, 2010. "Comparing Firm Failure Predictions Between Logit, KMV, and ZPP Models: Evidence from Taiwan’s Electronics Industry," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 17(3), pages 209-239, September.
    2. Dean Fantazzini & Silvia Figini, 2009. "Random Survival Forests Models for SME Credit Risk Measurement," Methodology and Computing in Applied Probability, Springer, vol. 11(1), pages 29-45, March.
    3. Kristóf, Tamás, 2008. "A csődelőrejelzés és a nem fizetési valószínűség számításának módszertani kérdéseiről [Some methodological questions of bankruptcy prediction and probability of default estimation]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(5), pages 441-461.
    4. Montgomery, D. & Swinnen, G. & Vanhoof, K., 1997. "Comparison of some AI and statistical classification methods for a marketing case," European Journal of Operational Research, Elsevier, vol. 103(2), pages 312-325, December.
    5. 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.
    6. Martin Vojtek & Evžen Koèenda, 2006. "Credit-Scoring Methods (in English)," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 56(3-4), pages 152-167, March.
    7. Chrysovalantis Gaganis & Fotios Pasiouras & Charalambos Spathis & Constantin Zopounidis, 2007. "A comparison of nearest neighbours, discriminant and logit models for auditing decisions," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 15(1‐2), pages 23-40, January.
    8. Ha-Thu Nguyen, 2016. "Reject inference in application scorecards: evidence from France," EconomiX Working Papers 2016-10, University of Paris Nanterre, EconomiX.
    9. Harper, Susan D. & Wharton, B. Robert & Traylor, Harlon D., 1985. "A Prediction Of Grain Elevator Bankruptcies Using Linear Discriminant Analysis," 1985 Annual Meeting, August 4-7, Ames, Iowa 278559, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    10. Ibtissem Baklouti, 2014. "A Psychological Approach To Microfinance Credit Scoring Via A Classification And Regression Tree," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 21(4), pages 193-208, October.
    11. M. V. Pomazanov, 2022. "Second-order accuracy metrics for scoring models and their practical use," Papers 2204.07989, arXiv.org, revised Nov 2022.
    12. Anthony L. Loviscek & Frederick D. Crowley, 1988. "Analyzing Changes in Municipal Bond Ratings: A Different Perspective," Urban Studies, Urban Studies Journal Limited, vol. 25(2), pages 124-132, April.
    13. Ha Thu Nguyen, 2016. "Reject inference in application scorecards: evidence from France," Working Papers hal-04141601, HAL.
    14. Lukasz Prorokowski, 2016. "Rank-order statistics for validating discriminative power of credit risk models," Bank i Kredyt, Narodowy Bank Polski, vol. 47(3), pages 227-250.
    15. Hussein A. Abdou & John Pointon, 2011. "Credit Scoring, Statistical Techniques And Evaluation Criteria: A Review Of The Literature," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 18(2-3), pages 59-88, April.
    16. Rafael Repullo & Jesús Saurina & Carlos Trucharte, 2010. "Mitigating the pro-cyclicality of Basel II [Bank loan loss provisions: a re-examination of capital management, earnings management and signalling effects]," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 25(64), pages 659-702.
    17. Robert T. Daigler & Bruce D. Fielitz, 1981. "A Multiple Discriminant Analysis Of Technical Indicators On The New York Stock Exchange," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 4(3), pages 169-182, September.
    18. Paolo Angelis & Fulvio Gismondi & Riccardo Ottaviani, 1994. "A non-parametric statistical model for the control of Italian insurance companies," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 17(1), pages 69-84, March.
    19. Gestel, Tony Van & Baesens, Bart & Suykens, Johan A.K. & Van den Poel, Dirk & Baestaens, Dirk-Emma & Willekens, Marleen, 2006. "Bayesian kernel based classification for financial distress detection," European Journal of Operational Research, Elsevier, vol. 172(3), pages 979-1003, August.
    20. Carlos Serrano-Cinca, 1997. "Feedforward neural networks in the classification of financial information," The European Journal of Finance, Taylor & Francis Journals, vol. 3(3), pages 183-202.

    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:wsi:wschap:9789814417501_0009. 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: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscientific.com/page/worldscibooks .

    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.