Enhancing credit risk assessments of SMEs with non-financial information
We investigate non-financial variables for predicting bankruptcy in small and medium-sized enterprises (SMEs). The variables encompass management, board and ownership structures and are sourced from universally accessible information, rendering them available to all stakeholders and allowing for the...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Cogent Economics & Finance |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/23322039.2024.2418910 |
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| author | Ranik Raaen Wahlstrøm Linn-Kristin Becker Trude Nonstad Fornes |
| author_facet | Ranik Raaen Wahlstrøm Linn-Kristin Becker Trude Nonstad Fornes |
| author_sort | Ranik Raaen Wahlstrøm |
| collection | DOAJ |
| description | We investigate non-financial variables for predicting bankruptcy in small and medium-sized enterprises (SMEs). The variables encompass management, board and ownership structures and are sourced from universally accessible information, rendering them available to all stakeholders and allowing for the analysis of all SMEs within a market. Using a large and recent sample of SMEs, we empirically examine the variables that predict bankruptcy over time horizons of one, two and three years. Our analysis incorporates state-of-the-art discrete hazard models, the least absolute shrinkage and selection operator (LASSO), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), bagging and random forest. We also test robustness using balanced datasets generated using the synthetic minority oversampling technique (SMOTE). We find that including non-financial variables enhances bankruptcy predictions compared to using financial variables alone. Moreover, our results show that among our variables, the most significant non-financial predictors of bankruptcy are the age of chief executive officers (CEOs), chairpersons and board members, as well as ownership share and place of the board members’ residences. |
| format | Article |
| id | doaj-art-fa798857471f4392863d6d7f0826511a |
| institution | Kabale University |
| issn | 2332-2039 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Economics & Finance |
| spelling | doaj-art-fa798857471f4392863d6d7f0826511a2025-01-22T13:51:12ZengTaylor & Francis GroupCogent Economics & Finance2332-20392024-12-0112110.1080/23322039.2024.2418910Enhancing credit risk assessments of SMEs with non-financial informationRanik Raaen Wahlstrøm0Linn-Kristin Becker1Trude Nonstad Fornes2NTNU Business School, Norwegian University of Science and Technology, Trondheim, NorwayNTNU Business School, Norwegian University of Science and Technology, Trondheim, NorwayNTNU Business School, Norwegian University of Science and Technology, Trondheim, NorwayWe investigate non-financial variables for predicting bankruptcy in small and medium-sized enterprises (SMEs). The variables encompass management, board and ownership structures and are sourced from universally accessible information, rendering them available to all stakeholders and allowing for the analysis of all SMEs within a market. Using a large and recent sample of SMEs, we empirically examine the variables that predict bankruptcy over time horizons of one, two and three years. Our analysis incorporates state-of-the-art discrete hazard models, the least absolute shrinkage and selection operator (LASSO), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), bagging and random forest. We also test robustness using balanced datasets generated using the synthetic minority oversampling technique (SMOTE). We find that including non-financial variables enhances bankruptcy predictions compared to using financial variables alone. Moreover, our results show that among our variables, the most significant non-financial predictors of bankruptcy are the age of chief executive officers (CEOs), chairpersons and board members, as well as ownership share and place of the board members’ residences.https://www.tandfonline.com/doi/10.1080/23322039.2024.2418910Small and medium-sized enterprises (SMEs)bankruptcy predictioncorporate governancenon-financial predictorsLASSOC25 |
| spellingShingle | Ranik Raaen Wahlstrøm Linn-Kristin Becker Trude Nonstad Fornes Enhancing credit risk assessments of SMEs with non-financial information Cogent Economics & Finance Small and medium-sized enterprises (SMEs) bankruptcy prediction corporate governance non-financial predictors LASSO C25 |
| title | Enhancing credit risk assessments of SMEs with non-financial information |
| title_full | Enhancing credit risk assessments of SMEs with non-financial information |
| title_fullStr | Enhancing credit risk assessments of SMEs with non-financial information |
| title_full_unstemmed | Enhancing credit risk assessments of SMEs with non-financial information |
| title_short | Enhancing credit risk assessments of SMEs with non-financial information |
| title_sort | enhancing credit risk assessments of smes with non financial information |
| topic | Small and medium-sized enterprises (SMEs) bankruptcy prediction corporate governance non-financial predictors LASSO C25 |
| url | https://www.tandfonline.com/doi/10.1080/23322039.2024.2418910 |
| work_keys_str_mv | AT ranikraaenwahlstrøm enhancingcreditriskassessmentsofsmeswithnonfinancialinformation AT linnkristinbecker enhancingcreditriskassessmentsofsmeswithnonfinancialinformation AT trudenonstadfornes enhancingcreditriskassessmentsofsmeswithnonfinancialinformation |