Determinants of Default Probability for Audited and Unaudited SMEs under Stressed Conditions in Zimbabwe
Using stepwise logistic regression models, the study aims to separately detect and explain the determinants of default probability for unaudited and audited small-to-medium enterprises (SMEs) under stressed conditions in Zimbabwe. For effectiveness purposes, we use two separate datasets for unaudite...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
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| Series: | Economies |
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| Online Access: | https://www.mdpi.com/2227-7099/10/11/274 |
| _version_ | 1797468501515960320 |
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| author | Frank Ranganai Matenda Mabutho Sibanda |
| author_facet | Frank Ranganai Matenda Mabutho Sibanda |
| author_sort | Frank Ranganai Matenda |
| collection | DOAJ |
| description | Using stepwise logistic regression models, the study aims to separately detect and explain the determinants of default probability for unaudited and audited small-to-medium enterprises (SMEs) under stressed conditions in Zimbabwe. For effectiveness purposes, we use two separate datasets for unaudited and audited SMEs from an anonymous Zimbabwean commercial bank. The results of the paper indicate that the determinants of default probability for unaudited and audited SMEs are not identical. These determinants include financial ratios, firm and loan characteristics, and macroeconomic variables. Furthermore, we discover that the classification rates of SME default prediction models are enhanced by fusing financial ratios and firm and loan features with macroeconomic factors. The study highlights the vital contribution of macroeconomic factors in the prediction of SME default probability. We recommend that financial institutions model separately the default probability for audited and unaudited SMEs. Further, it is recommended that financial institutions should combine financial ratios and firm and loan characteristics with macroeconomic variables when designing default probability models for SMEs in order to augment their classification rates. |
| first_indexed | 2024-03-09T19:08:22Z |
| format | Article |
| id | doaj.art-6c0aa67b97a44df295041c191e648569 |
| institution | Directory Open Access Journal |
| issn | 2227-7099 |
| language | English |
| last_indexed | 2024-03-09T19:08:22Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Economies |
| spelling | doaj.art-6c0aa67b97a44df295041c191e6485692023-11-24T04:22:28ZengMDPI AGEconomies2227-70992022-11-01101127410.3390/economies10110274Determinants of Default Probability for Audited and Unaudited SMEs under Stressed Conditions in ZimbabweFrank Ranganai Matenda0Mabutho Sibanda1School of Accounting, Economics and Finance, University of KwaZulu-Natal, Durban 4000, South AfricaSchool of Accounting, Economics and Finance, University of KwaZulu-Natal, Durban 4000, South AfricaUsing stepwise logistic regression models, the study aims to separately detect and explain the determinants of default probability for unaudited and audited small-to-medium enterprises (SMEs) under stressed conditions in Zimbabwe. For effectiveness purposes, we use two separate datasets for unaudited and audited SMEs from an anonymous Zimbabwean commercial bank. The results of the paper indicate that the determinants of default probability for unaudited and audited SMEs are not identical. These determinants include financial ratios, firm and loan characteristics, and macroeconomic variables. Furthermore, we discover that the classification rates of SME default prediction models are enhanced by fusing financial ratios and firm and loan features with macroeconomic factors. The study highlights the vital contribution of macroeconomic factors in the prediction of SME default probability. We recommend that financial institutions model separately the default probability for audited and unaudited SMEs. Further, it is recommended that financial institutions should combine financial ratios and firm and loan characteristics with macroeconomic variables when designing default probability models for SMEs in order to augment their classification rates.https://www.mdpi.com/2227-7099/10/11/274default probabilitydistressed financial and economic conditionsunaudited and audited SMEsdeterminantsstepwise logistic regression |
| spellingShingle | Frank Ranganai Matenda Mabutho Sibanda Determinants of Default Probability for Audited and Unaudited SMEs under Stressed Conditions in Zimbabwe Economies default probability distressed financial and economic conditions unaudited and audited SMEs determinants stepwise logistic regression |
| title | Determinants of Default Probability for Audited and Unaudited SMEs under Stressed Conditions in Zimbabwe |
| title_full | Determinants of Default Probability for Audited and Unaudited SMEs under Stressed Conditions in Zimbabwe |
| title_fullStr | Determinants of Default Probability for Audited and Unaudited SMEs under Stressed Conditions in Zimbabwe |
| title_full_unstemmed | Determinants of Default Probability for Audited and Unaudited SMEs under Stressed Conditions in Zimbabwe |
| title_short | Determinants of Default Probability for Audited and Unaudited SMEs under Stressed Conditions in Zimbabwe |
| title_sort | determinants of default probability for audited and unaudited smes under stressed conditions in zimbabwe |
| topic | default probability distressed financial and economic conditions unaudited and audited SMEs determinants stepwise logistic regression |
| url | https://www.mdpi.com/2227-7099/10/11/274 |
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