Post‐CEO retirement appointments and financial accounting—Evidence from CEO turnovers
Bibliographic record
Abstract
Abstract Prior research has shown that when boards seek to appoint CEOs as outside directors, the director labor market rewards CEOs' accounting performance. This study examines whether the external labor market's assessment of the accounting performance is moderated by CEOs' past exercise of financial reporting discretion in the form of accruals and real earnings management and financial statement readability. Our results show a positive association between post‐CEO board opportunities and within‐GAAP accruals management as well as to more readable financial statements. Earnings restatements are associated with fewer board positions and director pay. However, the director labor market appears to punish R&D expenditure above the industry median, suggesting that boards view overinvestment as a risky avenue for growth. Finally, the results suggest that for CEOs with planned retirement, the director labor market provides some mitigating effect on the horizon problem.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".