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Distinctiveness of Highly Risky Italian Firms That are Saved-A Logistic Approach

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  • Marco Muscettola

Abstract

In our paper, we use a default mode approach in order to accurately classify a sample of 3,835 Italian manufactu?ring companies, and to gauge their health status on the basis of variables taken from the financial statement. The present study is oriented to test the potentiality of salvation for firms included within the worst classes of rating. The research aims to support the resolution of an elaborate theme: the identification of both highly risky companies designed to survive despite their own class of statistical rating, and firms that will move closer to a default status. In this way, the consequences of our examination could help to recognize, among firms considered "highly risky", the latent durability on the time.

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  • Marco Muscettola, 2019. "Distinctiveness of Highly Risky Italian Firms That are Saved-A Logistic Approach," Applied Economics and Finance, Redfame publishing, vol. 6(1), pages 64-73, January.
    • Handle: RePEc:rfa:aefjnl:v:6:y:2019:i:1:p:64-73
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    References listed on IDEAS

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    1. Mossman, Charles E, et al, 1998. "An Empirical Comparison of Bankruptcy Models," The Financial Review, Eastern Finance Association, vol. 33(2), pages 35-53, May.
    2. Wiginton, John C., 1980. "A Note on the Comparison of Logit and Discriminant Models of Consumer Credit Behavior," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 15(3), pages 757-770, September.
    3. Crouhy, Michel & Galai, Dan & Mark, Robert, 2001. "Prototype risk rating system," Journal of Banking & Finance, Elsevier, vol. 25(1), pages 47-95, January.
    4. Foglia, A. & Laviola, S. & Marullo Reedtz, P., 1998. "Multiple banking relationships and the fragility of corporate borrowers," Journal of Banking & Finance, Elsevier, vol. 22(10-11), pages 1441-1456, October.
    5. Becchetti, Leonardo & Sierra, Jaime, 2003. "Bankruptcy risk and productive efficiency in manufacturing firms," Journal of Banking & Finance, Elsevier, vol. 27(11), pages 2099-2120, November.
    6. Blum, M, 1974. "Failing Company Discriminant-Analysis," Journal of Accounting Research, Wiley Blackwell, vol. 12(1), pages 1-25.
    7. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    8. Marco Muscettola, 2015. "Difficulties for small firms to invest in research prerogatives. An empirical analysis of a sample of Italian firms," Applied Economics, Taylor & Francis Journals, vol. 47(15), pages 1495-1510, March.
    9. 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.
    10. Fabien Couderc & Olivier Renault, 2005. "Times-To-Default:Life Cycle, Global and Industry Cycle Impact," FAME Research Paper Series rp142, International Center for Financial Asset Management and Engineering.
    11. Marco Muscettola, 2014. "Probability of Efficiency: Statistical Implications That Lead Firms to Achieve a Minimal and Sufficient ¡°Return-On-Investment¡±," Journal of Management and Strategy, Journal of Management and Strategy, Sciedu Press, vol. 5(4), pages 26-36, November.
    12. Carey, Mark & Hrycay, Mark, 2001. "Parameterizing credit risk models with rating data," Journal of Banking & Finance, Elsevier, vol. 25(1), pages 197-270, January.
    13. 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.
    14. 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..
    15. Laitinen, Erkki K. & Laitinen, Teija, 2000. "Bankruptcy prediction: Application of the Taylor's expansion in logistic regression," International Review of Financial Analysis, Elsevier, vol. 9(4), pages 327-349.
    16. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January.
    17. 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.
    18. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    19. Zmijewski, Me, 1984. "Methodological Issues Related To The Estimation Of Financial Distress Prediction Models," Journal of Accounting Research, Wiley Blackwell, vol. 22, pages 59-82.
    20. Lo, Andrew W., 1986. "Logit versus discriminant analysis : A specification test and application to corporate bankruptcies," Journal of Econometrics, Elsevier, vol. 31(2), pages 151-178, March.
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