Information content of credit rating affirmations
Bibliographic record
Abstract
Abstract We examine the economic determinants and informational effects of credit rating affirmations (i.e., the reiteration of past credit ratings) for a sample of US public firms from 1995 to 2020. We find that credit rating affirmations typically follow major corporate events and changes in firm fundamentals that increase information uncertainty about a firm's creditworthiness, suggesting that affirmations reduce uncertainty. We further document that rating affirmations provide value‐relevant information to equity and debt investors. Using a short‐window event study method, we show that equity investors react positively to rating affirmations and that information uncertainty around affirmations diminishes. These findings are more pronounced for firms with non‐investment‐grade ratings. We further show that our results strengthen for firms with greater pre‐affirmation information uncertainty. Finally, consistent with our information uncertainty reduction results from the stock market, we report that bond yield spreads decrease for affirmed firms. Again, the effect is more pronounced for firms with non‐investment‐grade ratings. In summary, we highlight the significant capital markets' effects of credit rating affirmations, an area that the literature has largely ignored.
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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 | 0.003 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".