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Credit Risk Assessment of Corporate Sector in Croatia

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  • Lana Ivicic

    (Croatian National Bank, Zagreb)

  • Sasa Cerovac

    (Croatian National Bank, Zagreb)

Abstract

The main goal of this paper is modeling credit risk of non-financial businesses entities by assessing the rating migration probabilities and predicting the probability of default over one year horizon on the basis of corporate financial accounts. Our research provides a number of new important insights. Ratings migration matrices are symmetrical in every observed period, which implies that default state is not final terminal state. We find a high degree of rating stability, with the exception of some volatility generated by firms in the middle of the ratings scale. In the period of lower economic growth probabilities of transition between different risks categories are lower than in the period of higher economic growth. Probabilities of default are relatively stable across enterprises operating in different economic activities. After considering a wide range of potential predictors of default, multivariate logistic regression results reveal that the most important are the ratio of shareholders’ equity to total assets and the ratio of EBIT to total liabilities, both negatively related to the probability of default. In addition, higher liquidity, profitability and sales as well as construction and real estate sector affiliation all decrease the companies’ probability of default in the following year. The model correctly classifies relatively reasonable percentage of companies in the sample (74% of all the companies, 71% of defaulted and 75% of non-defaulted companies) when the threshold is set in such a way to maximize the sum of correctly predicted proportions for both defaulted and nondefaulted companies.

Suggested Citation

  • Lana Ivicic & Sasa Cerovac, 2009. "Credit Risk Assessment of Corporate Sector in Croatia," Financial Theory and Practice, Institute of Public Finance, vol. 33(4), pages 373-399.
    • Handle: RePEc:ipf:finteo:v:33:y:2009:i:4:p:373-399
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    File URL: http://www.ijf.hr/eng/FTP/2009/4/ivicic-cerovac.pdf
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    References listed on IDEAS

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    1. Nickell, Pamela & Perraudin, William & Varotto, Simone, 2000. "Stability of rating transitions," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 203-227, January.
    2. Andreas Charitou & Evi Neophytou & Chris Charalambous, 2004. "Predicting corporate failure: empirical evidence for the UK," European Accounting Review, Taylor & Francis Journals, vol. 13(3), pages 465-497.
    3. 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.
    4. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    5. Tor Jacobson & Jesper Lindé & Kasper Roszbach, 2013. "Firm Default And Aggregate Fluctuations," Journal of the European Economic Association, European Economic Association, vol. 11(4), pages 945-972, August.
    6. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
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    Cited by:

    1. Hense, Florian, 2015. "Interest rate elasticity of bank loans: The case for sector-specific capital requirements," CFS Working Paper Series 504, Center for Financial Studies (CFS).

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