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Default Patterns in Seven EU Countries: A Random Forest Approach

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  • Andreas Behr
  • Jurij Weinblat

Abstract

This study uses the relatively new “random forest” (RF) approach, which is based on decision-tree analysis by combining the results of a large set of decision trees. RFs have so far been little used for default prediction but offer an interesting alternative to well-established default prediction techniques. Based on accounting data from 945,062 observed European firms from seven countries in 2010 and 1,019,312 firms in 2011, we provide evidence on the country-specific default patterns. Because of the strong imbalance of the data sets with regard to the solvency status, standard RF implementations have to be modified to allow the estimation of realistic default propensities. We find that by far most accurate out-of-sample default propensities can be obtained for Italy followed by Portugal and Spain and the least accurate for the UK and Finland. The debt ratio, rate of return on sales, dynamic gearing ratio, and the rate of return on assets are found to be the most important variables for default prediction. The variable importance rankings are rather country specific, pointing to heterogeneity in the default patterns across the countries studied.

Suggested Citation

  • Andreas Behr & Jurij Weinblat, 2017. "Default Patterns in Seven EU Countries: A Random Forest Approach," International Journal of the Economics of Business, Taylor & Francis Journals, vol. 24(2), pages 181-222, May.
    • Handle: RePEc:taf:ijecbs:v:24:y:2017:i:2:p:181-222
    DOI: 10.1080/13571516.2016.1252532
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    Cited by:

    1. Mercadier, Mathieu & Lardy, Jean-Pierre, 2019. "Credit spread approximation and improvement using random forest regression," European Journal of Operational Research, Elsevier, vol. 277(1), pages 351-365.
    2. Lucian Belascu & Alexandra Horobet & Georgiana Vrinceanu & Consuela Popescu, 2021. "Performance Dissimilarities in European Union Manufacturing: The Effect of Ownership and Technological Intensity," Sustainability, MDPI, vol. 13(18), pages 1-19, September.
    3. Falco J. Bargagli-Stoffi & Jan Niederreiter & Massimo Riccaboni, 2020. "Supervised learning for the prediction of firm dynamics," Papers 2009.06413, arXiv.org.
    4. Falco J. Bargagli-Stoffi & Fabio Incerti & Massimo Riccaboni & Armando Rungi, 2023. "Machine Learning for Zombie Hunting: Predicting Distress from Firms' Accounts and Missing Values," Papers 2306.08165, arXiv.org.
    5. Jan Niederreiter, 2023. "Broadening Economics in the Era of Artificial Intelligence and Experimental Evidence," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 9(1), pages 265-294, March.
    6. Jurij Weinblat, 2018. "Forecasting European high-growth Firms - A Random Forest Approach," Journal of Industry, Competition and Trade, Springer, vol. 18(3), pages 253-294, September.

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