Do Credit Ratings Reflect Underlying Firm Characteristics? Evidence from the Utility Industry
Bibliographic record
Abstract
The recent financial crisis has raised questions regarding the role credit rating agencies play in monitoring the quality of corporate debt. Utility industry deregulation serves as a natural testing ground for evaluating the prudence of rating agencies and their monitoring process. Following deregulation and the Enron scandal, the general opinion among industry professionals is that utilities are being punished by credit rating agencies. Contrary to this popular belief, we find that the utility credit ratings are significantly higher compared to those of other firms, and this significance is more pronounced in the post-deregulation period. We also do not find any evidence that the credit ratings of utilities are more likely to be downgraded (upgraded) following deregulation. Although rating agencies often cite regulatory reasons for placing utilities on negative credit watches, these firms ‟ ratings are rarely downgraded after being placed on negative watches. We also find that while firms in other industries adjust their capital structures following rating changes, rating changes have insignificant impact on utilities. Thus, despite the statements often seen in popular press, credit ratings of utilities seem more of a product of interactions between utilities and rating agencies than of firm characteristics. In general, our evidence indicates that credit ratings might not always be reflective of the underlying firm characteristics.
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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 | 0.005 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".