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A note on performance measures for business failure prediction models

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  • Hubert Ooghe
  • Christophe Spaenjers

Abstract

This note briefly describes three performance measures that can be used in business failure prediction models: the unweighted error rate (UER), D-max and the Gini-coefficient. The use of these measures (and the mathematical relationship between them) is illustrated with numerical examples. We hope that this note may help the reader to better understand (and possibly use) these classification criteria.

Suggested Citation

  • Hubert Ooghe & Christophe Spaenjers, 2010. "A note on performance measures for business failure prediction models," Applied Economics Letters, Taylor & Francis Journals, vol. 17(1), pages 67-70, January.
    • Handle: RePEc:taf:apeclt:v:17:y:2010:i:1:p:67-70
    DOI: 10.1080/13504850701719769
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    References listed on IDEAS

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    1. Balcaen, Sofie & Ooghe, Hubert, 2006. "35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems," The British Accounting Review, Elsevier, vol. 38(1), pages 63-93.
    2. H. Ooghe & C. Spaenjers & P. Vandermoere, 2005. "Business failure prediction: simple-intuitive models versus statistical models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/338, Ghent University, Faculty of Economics and Business Administration.
    3. Altman, Edward I., 1980. "Commercial Bank Lending: Process, Credit Scoring, and Costs of Errors in Lending," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 15(4), pages 813-832, November.
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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. Jorgen Vitting Andersen & Roy Cerqueti & Jessica Riccioni, 2021. "Rational expectations as a tool for predicting failure of weighted k-out-of-n reliability systems," Papers 2112.10672, arXiv.org.
    3. Li, Hui & Hong, Lu-Yao & He, Jia-Xun & Xu, Xuan-Guo & Sun, Jie, 2013. "Small sample-oriented case-based kernel predictive modeling and its economic forecasting applications under n-splits-k-times hold-out assessment," Economic Modelling, Elsevier, vol. 33(C), pages 747-761.
    4. Jørgen Vitting Andersen & Roy Cerqueti & Jessica Riccioni, 2023. "Rational expectations as a tool for predicting failure of weighted k-out-of-n reliability systems," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-03634370, HAL.
    5. Li, Hui & Sun, Jie, 2012. "Forecasting business failure: The use of nearest-neighbour support vectors and correcting imbalanced samples – Evidence from the Chinese hotel industry," Tourism Management, Elsevier, vol. 33(3), pages 622-634.
    6. Jørgen Vitting Andersen & Roy Cerqueti & Jessica Riccioni, 2023. "Rational expectations as a tool for predicting failure of weighted k-out-of-n reliability systems," Post-Print hal-03634370, HAL.
    7. Jorgen-Vitting Andersen & Roy Cerqueti & Jessica Riccioni, 2023. "Rational expectations as a tool for predicting failure of weighted k-out-of-n reliability systems," Annals of Operations Research, Springer, vol. 326(1), pages 295-316, July.

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