Bibliographic record
Abstract
Credit rating agencies rate companies and states by assigning them scores depending on their level of solvency. These scores are inversely proportional to default risk and then proportional to quotes which are proportional to bonds value. Consequently, scores are calculated depending on companies and states bankruptcy risk. In our paper, we assess company solvency using numerical symbols and an accelerating risk model. Although the Big3 1 rating agencies use uniformly distributed risk to rate corporate bonds, we think that the distribution should vary uniformly. Our theoretical model is based on a homogeneously risk varying path with a fluctuating speed but a constant acceleration of risk. We measure this acceleration and calculate risk intervals by using a linear regression where asset volatility represents the dependent variable, and a set of 20 company categories representing the independent variable. Comparative statics are used to illustrate our analysis. We obtain a very significant coefficient for the exogenous variable, representing homogeneous risk intervals. We use 20 classes of risk to be consistent with the "US equivalent rating", as the Big3 rating agencies do, which allows us to determine risk classes and rate companies according to the numerical scale obtained. We compare our numerical scale to the equivalent rating tables used by Moody's, Fitch Ratings and S&P. According to our findings, companies with a risk level under 16 are considered to be solvent, while those with a 17-to-20-risk level are considered to be in trouble. Indeed, the length of 1 Moody's, Standard & Poor's and Fitch Ratings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".