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Bankruptcy Prediction for Micro and Small Enterprises Using Financial, Non-Financial, Business Sector and Macroeconomic Variables: The Case of the Lithuanian Construction Sector

Author

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  • Rasa Kanapickienė

    (Department of Finance, Faculty of Economics and Business Administration, Vilnius University, 10222 Vilnius, Lithuania)

  • Tomas Kanapickas

    (Department of Software Engineering, Faculty of Informatics, Kaunas University of Technology, 44249 Kaunas, Lithuania)

  • Audrius Nečiūnas

    (Department of Applied Informatics, Faculty of Informatics, Kaunas University of Technology, 51368 Kaunas, Lithuania)

Abstract

Credit-risk models that are designed for general application across sectors may not be suitable for the construction industry, which has unique characteristics and financial risks that require specialised modelling approaches. Moreover, advanced bankruptcy-prediction models are often used to achieve the highest accuracy in large modern datasets. Therefore, the aim of this research is the creation of enterprise-bankruptcy prediction (EBP) models for Lithuanian micro and small enterprises (MiSEs) in the construction sector. This issue is analysed based on classification models and the specific types of variable used. Firstly, four types of variable are proposed. In EBP models, financial variables substantially explain an enterprise’s financial statements and performance from different perspectives. Including enterprises’ non-financial, construction-sector and macroeconomic variables improves the characteristics of EBP models. The inclusion of macroeconomic variables in the model has a particularly significant impact. These findings can be of great significance to investors, creditors, policymakers and practitioners in assessing financial risks and making informed decisions. The second question is related to the classification models used. To develop the EBP models, logistic regression (LR), artificial neural networks (ANNs) and multivariate adaptive regression splines (MARS) were used. In addition, this study developed two-stage hybrid models, i.e., the LR is combined with ANNs. The findings show that two-stage hybrid models do not improve bankruptcy prediction. It cannot be argued that ANN models are more accurate in predicting bankruptcy. The MARS model demonstrates the best bankruptcy prediction, i.e., this model could be a valuable tool for stakeholders to evaluate enterprises’ financial risk.

Suggested Citation

  • Rasa Kanapickienė & Tomas Kanapickas & Audrius Nečiūnas, 2023. "Bankruptcy Prediction for Micro and Small Enterprises Using Financial, Non-Financial, Business Sector and Macroeconomic Variables: The Case of the Lithuanian Construction Sector," Risks, MDPI, vol. 11(5), pages 1-33, May.
    • Handle: RePEc:gam:jrisks:v:11:y:2023:i:5:p:97-:d:1150556
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    References listed on IDEAS

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    1. Crook, Jonathan N. & Edelman, David B. & Thomas, Lyn C., 2007. "Recent developments in consumer credit risk assessment," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1447-1465, December.
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    Cited by:

    1. Emilia Herman & Kinga-Emese Zsido, 2023. "The Financial Sustainability of Retail Food SMEs Based on Financial Equilibrium and Financial Performance," Mathematics, MDPI, vol. 11(15), pages 1-26, August.

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