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Bankruptcy prediction models: How to choose the most relevant variables?

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  • du Jardin, Philippe

Abstract

This paper is a critical review of the variable selection methods used to build empirical bankruptcy prediction models. Recent decades have seen many papers on modeling techniques, but very few about the variable selection methods that should be used jointly or about their fit. This issue is of concern because it determines the parsimony and economy of the models and thus the accuracy of the predictions. We first analyze those variables that are considered the best bankruptcy predictors, then present variable selection and review the main variable selection techniques used to design financial failure models. Finally, we discuss the way these techniques are commonly used, and we highlight the problems that may occur with some non-linear methods.

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  • du Jardin, Philippe, 2009. "Bankruptcy prediction models: How to choose the most relevant variables?," MPRA Paper 44380, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:44380
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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. Simone Poli & Marco Gatti, 2024. "The relevance of cash flow information in predicting corporate bankruptcy in Italian private companies," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2024(1), pages 179-202.
    3. Ptak-Chmielewska Aneta, 2021. "Bankruptcy prediction of small- and medium-sized enterprises in Poland based on the LDA and SVM methods," Statistics in Transition New Series, Polish Statistical Association, vol. 22(1), pages 179-195, March.
    4. Duarte Trigueiros, 2019. "Improving the effectiveness of predictors in accounting-based models," Journal of Applied Accounting Research, Emerald Group Publishing Limited, vol. 20(2), pages 207-226, June.
    5. Youssef Zizi & Mohamed Oudgou & Abdeslam El Moudden, 2020. "Determinants and Predictors of SMEs’ Financial Failure: A Logistic Regression Approach," Risks, MDPI, vol. 8(4), pages 1-21, October.
    6. Veganzones, David & Séverin, Eric & Chlibi, Souhir, 2023. "Influence of earnings management on forecasting corporate failure," International Journal of Forecasting, Elsevier, vol. 39(1), pages 123-143.
    7. Aneta Ptak-Chmielewska, 2021. "Bankruptcy prediction of small- and medium-sized enterprises in Poland based on the LDA and SVM methods," Statistics in Transition New Series, Polish Statistical Association, vol. 22(1), pages 179-195, March.
    8. Šlefendorfas Gediminas, 2016. "Bankruptcy Prediction Model for Private Limited Companies of Lithuania," Ekonomika (Economics), Sciendo, vol. 95(1), pages 134-152, January.

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    More about this item

    Keywords

    Bankruptcy; Prediction models; Variable selection;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

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