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Domain Knowledge Features versus LASSO Features in Predicting Risk of Corporate Bankruptcy—DEA Approach

Author

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  • Martina Mokrišová

    (Faculty of Management and Business, University of Prešov, Konštantínova 16, 080 01 Prešov, Slovakia)

  • Jarmila Horváthová

    (Faculty of Management and Business, University of Prešov, Konštantínova 16, 080 01 Prešov, Slovakia)

Abstract

Predicting the risk of corporate bankruptcy is one of the most important challenges for researchers dealing with the issue of financial health evaluation. The risk of corporate bankruptcy is most often assessed with the use of early warning models. The results of these models are significantly influenced by the financial features entering them. The aim of this paper was to select the most suitable financial features for bankruptcy prediction. The research sample consisted of enterprises conducting a business within the Slovak construction industry. The features were selected using the domain knowledge (DK) approach and Least Absolute Shrinkage and Selection Operator (LASSO). The performance of VRS DEA (Variable Returns to Scale Data Envelopment Analysis) models was assessed with the use of accuracy, ROC (Receiver Operating Characteristics) curve, AUC (Area Under the Curve) and Somers’ D. The results show that the DK+DEA model achieved slightly better AUC and Somers’ D compared to the LASSO+DEA model. On the other hand, the LASSO+DEA model shows a smaller deviation in the number of identified businesses on the financial distress frontier. The added value of this research is the finding that the application of DK features achieves significant results in predicting businesses’ bankruptcy. The added value for practice is the selection of predictors of bankruptcy for the analyzed sample of enterprises.

Suggested Citation

  • Martina Mokrišová & Jarmila Horváthová, 2023. "Domain Knowledge Features versus LASSO Features in Predicting Risk of Corporate Bankruptcy—DEA Approach," Risks, MDPI, vol. 11(11), pages 1-18, November.
    • Handle: RePEc:gam:jrisks:v:11:y:2023:i:11:p:199-:d:1280888
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    References listed on IDEAS

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    1. Zeineb Affes & Rania Hentati-Kaffel, 2019. "Predicting US Banks Bankruptcy: Logit Versus Canonical Discriminant Analysis," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-03045837, HAL.
    2. Zeineb Affes & Rania Hentati-Kaffel, 2019. "Predicting US Banks Bankruptcy: Logit Versus Canonical Discriminant Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 199-244, June.
    3. Khurram Ajaz Khan & Robert Dankiewicz & Yana Kliuchnikava & Judit Olah, 2020. "How Do Entrepreneurs Feel Bankruptcy?," International Journal of Entrepreneurial Knowledge, Center for International Scientific Research of VSO and VSPP, vol. 8(1), pages 89-101, June.
    4. Fejér-Király Gergely, 2015. "Bankruptcy Prediction: A Survey on Evolution, Critiques, and Solutions," Acta Universitatis Sapientiae, Economics and Business, Sciendo, vol. 3(1), pages 93-108, December.
    5. Koh, SzeKee & Durand, Robert B. & Dai, Lele & Chang, Millicent, 2015. "Financial distress: Lifecycle and corporate restructuring," Journal of Corporate Finance, Elsevier, vol. 33(C), pages 19-33.
    6. Psillaki, Maria & Tsolas, Ioannis E. & Margaritis, Dimitris, 2010. "Evaluation of credit risk based on firm performance," European Journal of Operational Research, Elsevier, vol. 201(3), pages 873-881, March.
    7. Lee, Chia-Yen & Cai, Jia-Ying, 2020. "LASSO variable selection in data envelopment analysis with small datasets," Omega, Elsevier, vol. 91(C).
    8. Yi Cao & Xiaoquan Liu & Jia Zhai & Shan Hua, 2022. "A two‐stage Bayesian network model for corporate bankruptcy prediction," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 455-472, January.
    9. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    10. du Jardin, Philippe, 2015. "Bankruptcy prediction using terminal failure processes," European Journal of Operational Research, Elsevier, vol. 242(1), pages 286-303.
    11. Seiford, Lawrence M. & Zhu, Joe, 2002. "Modeling undesirable factors in efficiency evaluation," European Journal of Operational Research, Elsevier, vol. 142(1), pages 16-20, October.
    12. Frydman, Halina & Altman, Edward I & Kao, Duen-Li, 1985. "Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress," Journal of Finance, American Finance Association, vol. 40(1), pages 269-291, March.
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