Estimation of misreporting probability in corporate credit rating: A nonparametric approach
Bibliographic record
Abstract
Abstract There has been heated debate regarding credit‐rating agencies' (CRAs') reporting accuracy of corporate credit ratings, which is essential for investors because they rely on those crediting ratings to make investment decisions. We estimate the reporting accuracy using the data on corporate ratings from Standard & Poor from January 1986 to December 2011. First, there is a U‐shape in the overall misreporting pattern: the left‐hand side (the high‐rating groups) has a lower misreporting probability (3%), the middle has no misreporting, and the right‐hand side has a high misreporting probability (6%). Second, we find that there is a significant difference across the industries. The financial sector has the highest misreporting probability (35% in the lowest rating group) and misreporting magnitude (rating rank jump between true rating and reported rating), and the energy industry has the lowest misreporting probability. Last, when the economic condition is good, CRAs are likelier to inflate the rating.
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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 | 0.037 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".