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Bankruptcy Risk and Productive Efficiency in Manufacturing Firms

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  • Leonardo Becchetti

    ()
    (University of Rome II - Faculty of Economics)

  • Jaime Humberto Sierra Gonzalez 2

    ()
    (Pontifical University Javeriana Inicio - Department of Economics)

Abstract

The paper investigates the determinants of bankruptcy in three representative unbalanced samples of Italian firms for the periods 1989-1991, 1992-94 and 1995-97. Two important results are that: i) the degree of relative firm inefficiency measured as the distance from the efficient frontier has significant explanatory power in predicting bankruptcy ii) qualitative regressors such as customers' concentration and strength and proximity of competitors have significant predictive power and suggest that banks should not restrict their monitoring activity to balance sheet variables. These findings remain significant after controlling for balance sheet liquidity and profitability variables usually considered in these estimates

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Bibliographic Info

Paper provided by Tor Vergata University, CEIS in its series CEIS Research Paper with number 30.

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Length: 32
Date of creation: 11 Aug 2003
Date of revision:
Handle: RePEc:rtv:ceisrp:30

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Postal: CEIS - Centre for Economic and International Studies - Faculty of Economics - University of Rome "Tor Vergata" - Via Columbia, 2 00133 Roma
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Web page: http://www.ceistorvergata.it
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Postal: CEIS - Centre for Economic and International Studies - Faculty of Economics - University of Rome "Tor Vergata" - Via Columbia, 2 00133 Roma
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Web: http://www.ceistorvergata.it

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Keywords: bankruptcy prediction; stochastic frontiers; qualitative indicators;

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Citations

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Cited by:
  1. So Sohn & Yoon Kim, 2013. "Behavioral credit scoring model for technology-based firms that considers uncertain financial ratios obtained from relationship banking," Small Business Economics, Springer, vol. 41(4), pages 931-943, December.
  2. Laura Marta Nuã‘Ez, 2004. "The problem of variable selection for financial distress: applying GRASP methaeuristics," Working Papers Economia wp04-30, Instituto de Empresa, Area of Economic Environment.
  3. Koetter, M. & Bos, J.W.B. & Heid, F. & Kolari, J.W. & Kool, C.J.M. & Porath, D., 2007. "Accounting for distress in bank mergers," Journal of Banking & Finance, Elsevier, vol. 31(10), pages 3200-3217, October.
  4. Balcaen, S. & Manigart, S. & Ooghe, H., 2009. "From Distress to exit: determinants of the time to exit," Vlerick Leuven Gent Management School Working Paper Series 2009-12, Vlerick Leuven Gent Management School.
  5. Piruna Polsiri & Kingkarn Sookhanaphibarn, 2009. "Corporate Distress Prediction Models Using Governance and Financial Variables: Evidence from Thai Listed Firms during the East Asian Economic Crisis," Journal of Economics and Management, College of Business, Feng Chia University, Taiwan, vol. 5(2), pages 273-304, July.
  6. Ferreira Filipe, Sara & Grammatikos, Theoharry & Michala, Dimitra, 2014. "Forecasting Distress in European SME Portfolios," MPRA Paper 53572, University Library of Munich, Germany.
  7. Ha-Thu Nguyen, 2014. "Default Predictors in Credit Scoring - Evidence from France’s Retail Banking Institution," EconomiX Working Papers 2014-26, University of Paris West - Nanterre la Défense, EconomiX.
  8. Ipatova, Irina & Peresetsky, Аnatoly, 2013. "Technical efficiency of Russian plastic and rubber production firms," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 32(4), pages 71-92.
  9. Ruey-Ching Hwang & Jhao-Siang Siao & Huimin Chung & C. Chu, 2011. "Assessing bankruptcy prediction models via information content of technical inefficiency," Journal of Productivity Analysis, Springer, vol. 36(3), pages 263-273, December.
  10. Leonardo Becchetti & Melody Garcia & Giovanni Trovato, 2009. "Credit rationing and credit view: empirical evidence from loan data," CEIS Research Paper 144, Tor Vergata University, CEIS, revised 30 Sep 2009.

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