Developing a Scoring Credit Model Based on the Methodology of International Credit Rating Agencies
Bibliographic record
Abstract
The purpose of this work is to examine the relationship of various financial and non-financial (qualitative) factors of performance of non-financial companies and their credit ratings. We developed the scoring model which was based on the methodologies of international and Russian rating agencies. The modelled ratings of non-financial companies for 2018–2020 were compared with actual ratings assigned by the rating agencies and discrepancies were explained. The sample includes companies from retail, protein and agriculture, steel, oil and gas sectors from Russia, USA, Luxembourg, England, Canada, India, Ukraine and Brazil. The paper proved that addition of business and environmental, social and governance factors improved the quality ofscoring models in comparison to those including only financial metrics. There are strong patterns in the resulting ratings of companies for some industries. Retail industry companies are associated with high sales indicators, while steel industry companies have high interest expenses coverage ratios. Oil and gas industry companies mostly show high results in reserves coefficients. The study developed a credit rating forecasting tool that emulates the work of analysts of rating agencies and therefore has a high predictive power. The developed model can be used by financial market practitioners to predict the credit ratings of Russian companies in the face of the refusal of international rating agencies to rate Russian issuers.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".