Assess the Rating of SMEs by using Classification And Regression Trees (CART) with Qualitative Variables
Bibliographic record
Abstract
Italian banks have never used the credit rating system to grant funds to SMEs until the introduction of Basel II accord. Credit Rating Systems use financial ratios that are often not adapted to SMEs' assessment. In fact, small and medium-size enterprises are characterized by a high level of intangible assets. Some researchers focus their attention on the evaluation of qualitative variables of SMEs (management; corporate governance; SMEs-territory relationship), but no research is able to integrate these SMEs¡¯ qualitative variables into a single scoring model, or to sufficiently consider the characteristics of SMEs-financial markets relationship. This paper proposes a specific credit scoring model to SMEs' assessment which includes all these variables combining two methods: Altman¡¯s ¡®EM-Score¡¯ and CART (Classification and Regression Tree). This model is performed on a sample of 6, 534 Italian manufacturing firms getting a high level of reliability.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".