More Results and the Australian Model Rating Methodology Authors
Bibliographic record
Abstract
In a continuing effort to provide benchmarks for middle market companies, Moody's has created a model for estimating firm default probabilities using only financial statements. 1 Specifically estimated and tested against middle market firms, Moody's RiskCalc for Canada and the US was released in June 2000. The model allows one to quickly and efficiently attach default probabilities and rank firms from least to greatest probability of default. An unprecedently large dataset of private companies gives us the ability to estimate and validate such a model. As a purely objective, quantitative model, it can serve as a transparent benchmark of credit quality, one that summarizes financial statements into a single number. This report documents the following: • Description of Moody's unique private firm database in Australia, with comparisons to the data on US and Canada, • An updated description of the Moody's methodology for predicting default, • A comparison of the relationship of various financial ratios to default, and • Empirical tests of Moody's model. The following is meant to be a self-contained description of the derivation and testing of Moody's default model, however, some nuances may be omitted. A more complete documentation of the approach is contained in RiskCalcTM for Private Companies: Moody's Default Model. 2 1 "Middle market " means "unlisted " or "private " firms without traded equity information.
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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.048 | 0.011 |
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".