Bibliographic record
Abstract
Purpose of the paper: This study aims to investigate changes in equity volatility around CEO turnovers. It proposes hypotheses regarding the impact of CEO performance and types of turnover on the changes in equity volatility. It extends the current understanding of the existing theories by providing new empirical evidence. Design/methodology/approach: This paper uses both event study and regression analysis to examine and test the hypotheses proposed empirically. Data are obtained from multiple databases. Findings: This study finds evidence that the relationship between changes in equity volatility and the likelihood of CEO turnovers does not monotonically increase, but is a function of the various types of turnovers and successions. Compared to the departure of outperforming CEOs, the change in equity volatility following the departure of underperforming CEOs is much greater. The positive relationship between the change in volatility and past underperformance is stronger for forced turnovers than for voluntary turnovers. Equity volatility is substantially lower when the CEO relinquishing the post remains as an executive chairman, especially for those successions involving outside appointments. Originality/value: This paper extends the strategy and ability hypothesis by investigating the behaviour of equity volatility around CEO departures in case of prior under or outperformance along with the forced or voluntary nature of the turnover and provide new empirical evidence on the theories.
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 | 0.001 | 0.014 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".