CEO power and corporate risk: The impact of market competition and corporate governance
Bibliographic record
Abstract
Abstract Research Question/Issue Although there is no unified theory that can explain the relationship between CEO power and corporate risk, the empirical evidence generally finds a positive association. This study argues that market competition and corporate governance play critical roles in influencing this relationship. Research Findings/Insights Using a large panel of nonfinancial U.S. corporations for the period 1992–2015, I find that CEO power is positively associated with total and idiosyncratic measures of risk. However, this positive association remains significant only when market competition is high or corporate governance is strong. Theoretical/Academic Implications The research design of this study combines the predictions of agency theory, the behavioral agency model, and prospect theory to further our understanding of the relationship between CEO power and corporate risk, including consideration of how competition and corporate governance influence this relationship. Practitioner/Policy Implications The empirical evidence presented in this study can help boards to more accurately gauge when CEO power is most beneficial in terms of optimal levels of corporate risk and to better understand the relationship between power and risk. The results suggest that boards should grant more power to their CEOs when their firms operate in high‐competition markets or have strong corporate governance in place.
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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 | 0.004 | 0.019 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".