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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".