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Financial Performance in Manufacturing Firms: A Comparison Between Parametric and Non-Parametric Approaches

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  • Eleonora Bartoloni
  • Maurizio Baussola

Abstract

This paper provides a methodological analysis of credit risk in manufacturing firms by using two different credit scoring approaches. The first is the traditional discriminant approach for bankruptcy prediction based on a logistic regression model, whereas the second, data envelopment analysis, is a nonparametric approach for measuring firms’ efficiency that does not require ex-ante information on bankrupted firms. By using a manufacturing sample of both healthy and bankrupted firms during the period 2003–09 we provide an in-depth comparison of discriminant analysis and data envelopment analysis and conclude that a correct evaluation of firms’ credit worthiness is the result of successive fine-tuning procedures requiring the use of multiple methodological tools.

Suggested Citation

  • Eleonora Bartoloni & Maurizio Baussola, 2014. "Financial Performance in Manufacturing Firms: A Comparison Between Parametric and Non-Parametric Approaches," Business Economics, Palgrave Macmillan;National Association for Business Economics, vol. 49(1), pages 32-45, January.
  • Handle: RePEc:pal:buseco:v:49:y:2014:i:1:p:32-45
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    References listed on IDEAS

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    1. Sean Cleary, 1999. "The Relationship between Firm Investment and Financial Status," Journal of Finance, American Finance Association, vol. 54(2), pages 673-692, April.
    2. repec:bla:joares:v:10:y:1972:i:1:p:167-179 is not listed on IDEAS
    3. Heitor Almeida & Murillo Campello & Michael S. Weisbach, 2004. "The Cash Flow Sensitivity of Cash," Journal of Finance, American Finance Association, vol. 59(4), pages 1777-1804, August.
    4. Altman, Edward I. & Marco, Giancarlo & Varetto, Franco, 1994. "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, Elsevier, vol. 18(3), pages 505-529, May.
    5. Raffaella Calabrese & Silvia Angela Osmetti, 2011. "Generalized Extreme Value Regression for Binary Rare Events Data: an Application to Credit Defaults," Working Papers 201120, Geary Institute, University College Dublin.
    6. Lamont, Owen & Polk, Christopher & Saa-Requejo, Jesus, 2001. "Financial Constraints and Stock Returns," Review of Financial Studies, Society for Financial Studies, vol. 14(2), pages 529-554.
    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. Giudici, Giancarlo & Paleari, Stefano, 2000. "The Provision of Finance to Innovation: A Survey Conducted among Italian Technology-Based Small Firms," Small Business Economics, Springer, vol. 14(1), pages 37-53, February.
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    10. Charles J. Hadlock & Joshua R. Pierce, 2010. "New Evidence on Measuring Financial Constraints: Moving Beyond the KZ Index," Review of Financial Studies, Society for Financial Studies, vol. 23(5), pages 1909-1940.
    11. Alessandra Canepa & Paul Stoneman, 2008. "Financial constraints to innovation in the UK: evidence from CIS2 and CIS3," Oxford Economic Papers, Oxford University Press, vol. 60(4), pages 711-730, October.
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    More about this item

    JEL classification:

    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • L25 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Firm Performance

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