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On corporate financial distress prediction: what can we learn from private firms in a small open economy?

Author

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  • Evangelos C. Charalambakis

    (Bank of Greece)

Abstract

We use a large panel dataset that includes nearly 31,000 Greek private firms to investigate which variables impact on the prediction of corporate financial distress. Based on a multi-period logit model that accounts for industry effects, we identify six firm-specific variables that best describe the probability of financial distress for Greek private firms. In particular, the results show that profitability, leverage, the ratio of retained earnings to total assets, the ability of a firm to export, liquidity and the ability of a firm to pay out dividends are strong predictors of financial distress. We also find that GDP growth and a dummy variable that considers the effect of the Greek debt crisis affect the probability of financial distress. In-sample and out-of-sample forecast tests show that the model that includes the six firm-specific variables , GDP growth and industry dummies exhibits the highest predictive ability. Finally, the predictive ability of the model remains high when we increase the forecast horizon.

Suggested Citation

  • Evangelos C. Charalambakis, 2014. "On corporate financial distress prediction: what can we learn from private firms in a small open economy?," Working Papers 188, Bank of Greece.
  • Handle: RePEc:bog:wpaper:188
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    as
    1. John Y. Campbell & Jens Hilscher & Jan Szilagyi, 2008. "In Search of Distress Risk," Journal of Finance, American Finance Association, vol. 63(6), pages 2899-2939, December.
    2. Merton, Robert C, 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 29(2), pages 449-470, May.
    3. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    4. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    5. 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.
    6. repec:bla:jfinan:v:59:y:2004:i:2:p:831-868 is not listed on IDEAS
    7. Gao, Huasheng & Harford, Jarrad & Li, Kai, 2012. "CEO pay cuts and forced turnover: Their causes and consequences," Journal of Corporate Finance, Elsevier, vol. 18(2), pages 291-310.
    8. Altman, Edward I. & Haldeman, Robert G. & Narayanan, P., 1977. "ZETATM analysis A new model to identify bankruptcy risk of corporations," Journal of Banking & Finance, Elsevier, vol. 1(1), pages 29-54, June.
    9. 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.
    10. John Asker & Joan Farre-Mensa & Alexander Ljungqvist, 2011. "Comparing the Investment Behavior of Public and Private Firms," NBER Working Papers 17394, National Bureau of Economic Research, Inc.
    11. 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..
    12. Omer Brav, 2009. "Access to Capital, Capital Structure, and the Funding of the Firm," Journal of Finance, American Finance Association, vol. 64(1), pages 263-308, February.
    13. 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.
    14. 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.
    15. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    16. 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.
    17. Sreedhar T. Bharath & Tyler Shumway, 2008. "Forecasting Default with the Merton Distance to Default Model," The Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1339-1369, May.
    18. Petersen, Mitchell A & Rajan, Raghuram G, 1994. "The Benefits of Lending Relationships: Evidence from Small Business Data," Journal of Finance, American Finance Association, vol. 49(1), pages 3-37, March.
    19. Roni Michaely & Michael R. Roberts, 2012. "Corporate Dividend Policies: Lessons from Private Firms," The Review of Financial Studies, Society for Financial Studies, vol. 25(3), pages 711-746.
    20. Dierkes, Maik & Erner, Carsten & Langer, Thomas & Norden, Lars, 2013. "Business credit information sharing and default risk of private firms," Journal of Banking & Finance, Elsevier, vol. 37(8), pages 2867-2878.
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    Cited by:

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    2. Josef Baumgartner & Jürgen Bierbaumer-Polly & Marian Fink & Klaus S. Friesenbichler & Serguei Kaniovski & Michael Klien & Simon Loretz & Hans Pitlik & Silvia Rocha-Akis & Franz Sinabell & Alexander Sc, 2020. "Ökonomische Bewertung der in der Regierungsklausur am 16. Juni 2020 vorgestellten Maßnahmen," WIFO Studies, WIFO, number 66415.

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    More about this item

    Keywords

    corporate financial distress; bankruptcy prediction; hazardmodel; financial statements;
    All these keywords.

    JEL classification:

    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies

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