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An Early Warning System of Financial Distress Using Multinomial Logit Models and a Bootstrapping Approach

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  • Bi-Huei Tsai

Abstract

This study adopts multinomial logit models to separately measure the extent to which financial ratios and corporate governance signal the likelihood of "slight distress events" and "reorganization and bankruptcy." The results show that corporate governance variables are closely related to the occurrence of "slight distress events." The estimated misclassification costs of the 1,000 resamples generated through bootstrapping procedures are statistically lower for a model that makes use of corporate governance (CG model) than one without corporate governance (non-CG model) at all cutoff points in 2009, and cutoff points from 0.11 to 0.27 in 2008. Since corporate governance is incrementally useful in predicting financial distress, the CG model's predictive ability improves as two corporate governance factors are considered: ownership ratio of insiders and pledge-ownership ratio of insiders.

Suggested Citation

  • Bi-Huei Tsai, 2013. "An Early Warning System of Financial Distress Using Multinomial Logit Models and a Bootstrapping Approach," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 49(S2), pages 43-69, March.
    • Handle: RePEc:mes:emfitr:v:49:y:2013:i:s2:p:43-69
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    Cited by:

    1. Situm Mario, 2014. "Inability of Gearing-Ratio as Predictor for Early Warning Systems," Business Systems Research, Sciendo, vol. 5(2), pages 23-45, September.
    2. Rani Wijayanti & Sagita Rachmanira, 2020. "Early Warning System for Government Debt Crisis in Developing Countries," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 9(special i), pages 103-124.
    3. Chih‐Chun Chen & Chun‐Da Chen & Donald Lien, 2020. "Financial distress prediction model: The effects of corporate governance indicators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1238-1252, December.
    4. Almaskati, Nawaf & Bird, Ron & Yeung, Danny & Lu, Yue, 2021. "A horse race of models and estimation methods for predicting bankruptcy," Advances in accounting, Elsevier, vol. 52(C).
    5. Giuseppe Orlando & Roberta Pelosi, 2020. "Non-Performing Loans for Italian Companies: When Time Matters. An Empirical Research on Estimating Probability to Default and Loss Given Default," IJFS, MDPI, vol. 8(4), pages 1-22, November.
    6. Nawaf Almaskati & Ron Bird & Yue Lu & Danny Leung, 2019. "The Role of Corporate Governance and Estimation Methods in Predicting Bankruptcy," Working Papers in Economics 19/16, University of Waikato.

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