Bibliographic record
Abstract
This study examines the sophistication of rating agencies in incorporating managerial risk‐taking incentives into their credit risk evaluation. We measure risk‐taking incentives using two proxies: the sensitivity of managerial wealth to stock return volatility (vega) and the sensitivity of managerial wealth to stock price (delta). We find that rating agencies impound managerial risk‐taking incentives in their credit risk assessments. Assuming other things equal, a one standard deviation increase in vega (delta) will lead to an approximately one‐notch (two‐notch) rating downgrade. In addition, we evaluate the significance of credit ratings in the design of CEO compensation. Our findings suggest that rating‐troubled firms will gear down managerial incentives of risk seeking. In particular, other things equal, a rating downgrade to the lower edge of the investment category (i.e., BBB−) in the immediate prior year will bring about an approximately 51 percent reduction of vega incentive from options newly granted to the CEO in the current year. However, we find no evidence that firms' rating concerns significantly affect delta. Given the significance of credit ratings in the marketplace and their close connection to accounting, the findings of the current study advance our understanding, not only of how sophisticated rating agencies are in incorporating forward‐looking information (i.e., vega and delta) into risk assessments, but of how influential the raters are in changing firms' compensation policies. The findings also have implications on the role of accounting in constraining excessive managerial risk taking with improved disclosures on managerial compensation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".