Bankruptcy Prediction for Micro and Small Enterprises Using Financial, Non-Financial, Business Sector and Macroeconomic Variables: The Case of the Lithuanian Construction Sector
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Risks |
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| Online Access: | https://www.mdpi.com/2227-9091/11/5/97 |
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| author | Rasa Kanapickienė Tomas Kanapickas Audrius Nečiūnas |
| author_facet | Rasa Kanapickienė Tomas Kanapickas Audrius Nečiūnas |
| author_sort | Rasa Kanapickienė |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T03:21:03Z |
| format | Article |
| id | doaj.art-0b3e7e2d70884a5fae68caebb39f06fd |
| institution | Directory Open Access Journal |
| issn | 2227-9091 |
| language | English |
| last_indexed | 2024-03-11T03:21:03Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Risks |
| spelling | doaj.art-0b3e7e2d70884a5fae68caebb39f06fd2023-11-18T03:09:20ZengMDPI AGRisks2227-90912023-05-011159710.3390/risks11050097Bankruptcy Prediction for Micro and Small Enterprises Using Financial, Non-Financial, Business Sector and Macroeconomic Variables: The Case of the Lithuanian Construction SectorRasa Kanapickienė0Tomas Kanapickas1Audrius Nečiūnas2Department of Finance, Faculty of Economics and Business Administration, Vilnius University, 10222 Vilnius, LithuaniaDepartment of Software Engineering, Faculty of Informatics, Kaunas University of Technology, 44249 Kaunas, LithuaniaDepartment of Applied Informatics, Faculty of Informatics, Kaunas University of Technology, 51368 Kaunas, LithuaniaCredit-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.https://www.mdpi.com/2227-9091/11/5/97bankruptcy predictionsmall and micro enterprisesfinancial ratiosmacroeconomic variablesconstruction-sector variablesnon-financial variables |
| spellingShingle | Rasa Kanapickienė Tomas Kanapickas Audrius Nečiūnas Bankruptcy Prediction for Micro and Small Enterprises Using Financial, Non-Financial, Business Sector and Macroeconomic Variables: The Case of the Lithuanian Construction Sector Risks bankruptcy prediction small and micro enterprises financial ratios macroeconomic variables construction-sector variables non-financial variables |
| title | Bankruptcy Prediction for Micro and Small Enterprises Using Financial, Non-Financial, Business Sector and Macroeconomic Variables: The Case of the Lithuanian Construction Sector |
| title_full | Bankruptcy Prediction for Micro and Small Enterprises Using Financial, Non-Financial, Business Sector and Macroeconomic Variables: The Case of the Lithuanian Construction Sector |
| title_fullStr | Bankruptcy Prediction for Micro and Small Enterprises Using Financial, Non-Financial, Business Sector and Macroeconomic Variables: The Case of the Lithuanian Construction Sector |
| title_full_unstemmed | Bankruptcy Prediction for Micro and Small Enterprises Using Financial, Non-Financial, Business Sector and Macroeconomic Variables: The Case of the Lithuanian Construction Sector |
| title_short | Bankruptcy Prediction for Micro and Small Enterprises Using Financial, Non-Financial, Business Sector and Macroeconomic Variables: The Case of the Lithuanian Construction Sector |
| title_sort | bankruptcy prediction for micro and small enterprises using financial non financial business sector and macroeconomic variables the case of the lithuanian construction sector |
| topic | bankruptcy prediction small and micro enterprises financial ratios macroeconomic variables construction-sector variables non-financial variables |
| url | https://www.mdpi.com/2227-9091/11/5/97 |
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