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Bankruptcy prediction in Norway: a comparison study


  • Rada Dakovic
  • Claudia Czado
  • Daniel Berg


In this article we develop statistical models for bankruptcy prediction of Norwegian firms in the limited liability sector using annual balance sheet information. We fit generalized linear, generalized linear mixed and Generalized Additive Models (GAM) in a discrete hazard setting. It is demonstrated that careful examination of the functional relationship between the explanatory variables and the probability of bankruptcy enhances the models' forecasting performance. Using information on the industry sector we model the unobserved heterogeneity between different sectors through an industry-specific random factor in the generalized linear mixed model. The models developed are shown to outperform the model with Altman's variables.

Suggested Citation

  • Rada Dakovic & Claudia Czado & Daniel Berg, 2010. "Bankruptcy prediction in Norway: a comparison study," Applied Economics Letters, Taylor & Francis Journals, vol. 17(17), pages 1739-1746.
  • Handle: RePEc:taf:apeclt:v:17:y:2010:i:17:p:1739-1746
    DOI: 10.1080/13504850903299594

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    Cited by:

    1. Alessandra Amendola & Francesco Giordano & Maria Lucia Parrella & Marialuisa Restaino, 2017. "Variable selection in high‐dimensional regression: a nonparametric procedure for business failure prediction," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 33(4), pages 355-368, August.
    2. Giordani, Paolo & Jacobson, Tor & Schedvin, Erik von & Villani, Mattias, 2014. "Taking the Twists into Account: Predicting Firm Bankruptcy Risk with Splines of Financial Ratios," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 49(4), pages 1071-1099, August.
    3. Nicoleta Bărbuță-Mișu & Mara Madaleno, 2020. "Assessment of Bankruptcy Risk of Large Companies: European Countries Evolution Analysis," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 13(3), pages 1-28, March.
    4. Martin Kukuk & Michael Rönnberg, 2013. "Corporate credit default models: a mixed logit approach," Review of Quantitative Finance and Accounting, Springer, vol. 40(3), pages 467-483, April.
    5. Christian Lohmann & Thorsten Ohliger, 2017. "Nonlinear Relationships and Their Effect on the Bankruptcy Prediction," Schmalenbach Business Review, Springer;Schmalenbach-Gesellschaft, vol. 18(3), pages 261-287, August.
    6. Milovan Stanisic & Danka Stefanovic & Nada Arezina & Vule Mizdrakovic, 2013. "Analysis of auditor`s reports and bankruptcy risk in banking sector in the Republic of Serbia," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 15(34), pages 431-441, June.
    7. du Jardin, Philippe, 2015. "Bankruptcy prediction using terminal failure processes," European Journal of Operational Research, Elsevier, vol. 242(1), pages 286-303.
    8. Hyeongjun Kim & Hoon Cho & Doojin Ryu, 2020. "Corporate Default Predictions Using Machine Learning: Literature Review," Sustainability, MDPI, Open Access Journal, vol. 12(16), pages 1-11, August.
    9. Christian Lohmann & Thorsten Ohliger, 2020. "Bankruptcy prediction and the discriminatory power of annual reports: empirical evidence from financially distressed German companies," Journal of Business Economics, Springer, vol. 90(1), pages 137-172, February.
    10. Djeundje, Viani Biatat & Crook, Jonathan, 2019. "Identifying hidden patterns in credit risk survival data using Generalised Additive Models," European Journal of Operational Research, Elsevier, vol. 277(1), pages 366-376.

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