Generalist<scp>CEOs</scp>and Credit Ratings*
Bibliographic record
Abstract
ABSTRACT A recent trend is that firms prefer to hire generalist CEOs with transferable skills (across firms or industries) over hiring specialist CEOs, but the consequences of this trend are unclear. In this study, we examine whether credit rating agencies consider a CEO's general skills as a credit risk factor when assessing an entity's overall creditworthiness. We predict and find that generalist CEOs are associated with lower credit ratings, suggesting that the presence of generalist CEOs is a significant credit rating factor. We also find that generalist CEOs are likely to take on more risks, which leads to more volatile performance ex post, and our path analyses confirm default risk is a significant mediator between credit ratings and CEOs' general skills. Our results hold in the presence of additional controls (e.g., CEO characteristics and corporate governance), when applying different fixed‐effect models and different matching methods, and for a subsample with forced CEO turnover. We also find that the negative relationship is attenuated for R&D‐intensive firms and firms in competitive industries. Last, we provide evidence that firms with generalist CEOs face higher borrowing costs, such as bond yields and syndicated loan spreads. Overall, our results contribute to a growing literature on the costs and benefits of hiring generalist CEOs, by providing a full picture of why hiring a generalist CEO may benefit shareholders but also cause misalignments with bondholders' interests.
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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 | 0.001 | 0.011 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".