An Empirical Study on the Default Prediction Model in Small and Medium-Sized Enterprises Using Financial Ratios
Notice bibliographique
Résumé
Background/Objectives: There were many studies and empirical analysis of the default prediction model using the financial ratios of large companies, such as listed companies, but despite the large impact on the national economy, there was not much research on small and medium-sized enterprises due to lack of data, and it was also limited. Therefore, we studied the default prediction model of small and medium-sized enterprises through empirical analysis.Methods/Statistical analysis: The nine financial ratios that were estimated to have a high level of default prediction power are used in the screening of guarantee support by the Korea Credit Guarantee Fund, a public institution that supports small and medium-sized enterprises comprehensively, were verified through discriminant analysis to determine whether there was a significant difference between the default companies and the normal companies. Between 2014 and 2016, 429 companies that took out loans with support from the Korea Credit Guarantee Fund were analyzed by using the statistical program SPSS 22.Findings: The nine financial ratios (capital adequacy ratio, debt to equity ratio, total borrowings to total assets, ratio of operating profit to total capitals, ratio operating profit to sales, financial cost burden ratio, total assets turnover ratio, total capitals investment efficiency, cash flow to current liabilities) were useful in combining to distinguish between default and normal companies. All nine financial ratios were significant in distinguishing between default and normal companies. The discriminant power was significant in order of financial cost burden ratio, ratio of operating profit to total capitals, ratio of operating profit to sales, capital adequacy ratio, debt to equity ratio, total borrowings to total assets, cash flow to current liabilities, total capitals investment efficiency, and total assets turnover ratio.Improvements/Applications: This study provided a default prediction model in small and medium-sized enterprises by conducting empirical analysis of small and medium-sized enterprises. It can be said that it is meaningful to be able to use this study model as an indicator to predict the default of small and medium-sized enterprises and to proactively manage the negative impact on the national economy.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».