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Financial performance in manufacturing firms: a comparison between parametric and non parametric approaches

Listed author(s):
  • Eleonora Bartoloni

    ()

    (DISCE, Università Cattolica)

  • Maurizio Baussola

    ()

    (DISCE, Università Cattolica)

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 (DA) for bankruptcy prediction based on a logistic regression model, whereas the second, Data Envelopment Analysis (DEA), is a non-parametric approach for measuring firms’ efficiency which do es not require ex-ante information on bankrupted firms. By using a manufacturing sample of both healthy and bankrupted firms during the period 2003-2009 we provide an in-depth comparison of DA and DEA 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.

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File URL: http://www.unicatt.it/dipartimenti/DISES/allegati/dises1282.pdf
File Function: First version, 2012
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Paper provided by Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE) in its series DISCE - Quaderni del Dipartimento di Scienze Economiche e Sociali with number dises1282.

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Length: 33 pages
Date of creation: May 2012
Handle: RePEc:ctc:serie2:dises1282
Contact details of provider: Web page: http://www.unicatt.it/Dipartimenti/DISES
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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, 04.
  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, 08.
  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. 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.
  8. 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, 09.
  9. 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.
  10. repec:bla:joares:v:18:y:1980:i:1:p:109-131 is not listed on IDEAS
  11. 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.
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