The Disciplining Effect of Credit Default Swap Trading on the Quality of Credit Rating Agencies†
Bibliographic record
Abstract
ABSTRACT This study examines whether credit default swap (CDS) trading initiation can serve as a disciplining mechanism for leading credit rating agencies. Specifically, we investigate whether rating agencies improve their rating quality when an alternative source of credit risk information from CDS threatens to expose inaccuracies in their ratings. Understanding potential drivers of credit rating quality is important given the prominence of credit rating agencies as debt market gatekeepers and perceptions that the agencies have underperformed in providing high‐quality credit risk assessments in recent decades. We hypothesize and find that the initiation of CDS trading improves the accuracy of issuer‐paid credit ratings. This evidence is robust to a number of sensitivity tests including alternative ways of measuring rating accuracy and correction for selection bias. We also find that the timeliness of credit ratings, watch list, and outlook placements improves post‐initiation—particularly for negative shocks to credit risk. This study contributes to the credit rating literature by documenting that CDS trading can help discipline rating agencies. It also contributes to the literature studying the implications of the CDS market.
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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 | 0.009 | 0.112 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".