An Empirical Study on the Default Prediction Model in Small and Medium-Sized Enterprises Using Financial Ratios
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".