Bibliographic record
Abstract
We propose a theory for rating financial securities in the presence of structural maximization by the issuer in a market with investors who rely on credit rating. Two types of investors, simple investors who price tranches solely based on the ratings and model-based investors who use the rating information to calibrate models, are considered. Concepts of self-consistency and information gap are proposed to study different rating criteria. In particular, the expected loss criterion used by Moody’s satisfies self-consistency, but the probability of default criterion used by Standard & Poor’s does not. Moreover, the probability of default criterion typically has a higher information gap than the expected loss criterion. Empirical evidence in the post–Dodd–Frank period is consistent with our theoretical implications. We show that a set of axioms based on self-consistency leads to a tractable representation for all self-consistent rating criteria, which can also be extended to incorporate economic scenarios. New examples of self-consistent and scenario-based rating criteria are suggested. This paper was accepted by Agostino Capponi, finance. Funding: This work was supported by the National Key Research and Development Program of China [Grant 2020YFA0712700], the Natural Sciences and Engineering Research Council of Canada [Grants RGPIN-2018-03823, RGPAS-2018-522590], and the National Natural Science Foundation of China [Grant 12371476]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.01075 .
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".