Evaluating Financial Prediction Models for Business Failure in Moroccan Industrial Firms: Analysis and Strategic Implications
Guardado en:
| Udgivet i: | African Journal of Business and Economic Research vol. 20, no. 2 (Jun 2025), p. 171-197 |
|---|---|
| Hovedforfatter: | |
| Andre forfattere: | , |
| Udgivet: |
Adonis & Abbey Publishers Ltd
|
| Fag: | |
| Online adgang: | Citation/Abstract Full Text - PDF |
| Tags: |
Ingen Tags, Vær først til at tagge denne postø!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3236094427 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1750-4554 | ||
| 022 | |a 1750-4562 | ||
| 024 | 7 | |a 10.31920/1750-4562/2025/v20n2a8 |2 doi | |
| 035 | |a 3236094427 | ||
| 045 | 2 | |b d20250601 |b d20250630 | |
| 084 | |a 292036 |2 nlm | ||
| 100 | 1 | |a Elmazouny, Chayma | |
| 245 | 1 | |a Evaluating Financial Prediction Models for Business Failure in Moroccan Industrial Firms: Analysis and Strategic Implications | |
| 260 | |b Adonis & Abbey Publishers Ltd |c Jun 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a This paper aims to identify some financial characteristics that differentiate healthy companies from failing ones and evaluate the performance of five classification methods for predicting the business failure of Moroccan firms: LDA, LR, SVM, ANN, and K-NN. Using 51 healthy and 40 failing companies in 2019, it was found that failing firms, compared with healthy companies, exhibited lower profitability, limited internal generation of resources, high dependence on supplier credit, and lengthy recovery periods. Results from K-fold cross-validation show that LDA can detect failures but returns many false positives; ANNs balance accuracy with sensitivity well. SVM reduces false positives but could miss some failures, while K-NN and LR are unpredictable with complex data. Bootstrapping necessitates model generalisation, where ANNs are the most acceptable model for classification-based prediction. The study offers valuable insights for stakeholders to enhance financial risk assessments despite limitations such as sample size and static analysis. | |
| 651 | 4 | |a Morocco | |
| 653 | |a Profitability | ||
| 653 | |a Prediction models | ||
| 653 | |a Companies | ||
| 653 | |a Classification | ||
| 653 | |a Bootstrap method | ||
| 653 | |a Risk assessment | ||
| 653 | |a Discriminant analysis | ||
| 653 | |a Neural networks | ||
| 653 | |a Support vector machines | ||
| 653 | |a Predictions | ||
| 653 | |a Stakeholders | ||
| 653 | |a Industrial enterprises | ||
| 653 | |a Bootstrapping | ||
| 653 | |a Business | ||
| 700 | 1 | |a Bourhaba, Zineb | |
| 700 | 1 | |a Kandrouch Abdelkrim | |
| 773 | 0 | |t African Journal of Business and Economic Research |g vol. 20, no. 2 (Jun 2025), p. 171-197 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3236094427/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3236094427/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |