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

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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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    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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