When do Firms with New CEOs Engage in M&A? Understanding the Timing of New CEOs' First M&A Announcements
Bibliographic record
Abstract
Abstract New CEO appointments can create strategic uncertainty for stakeholders, potentially undermining the CEO's position. While the stakeholder uncertainty perspective suggests CEOs may act boldly to clarify their strategic intentions during early tenure, the CEO life cycle perspective proposes that CEOs avoid such moves during early tenure, as they still need to learn. This study integrates these views to examine whether and when new CEOs under high strategic uncertainty make bold strategic choices during early tenure. Focusing on first acquisitions – especially large and cross‐border deals – we argue that new CEOs have a higher hazard of announcing an acquisition under high strategic uncertainty, namely, outsider CEOs and those whose appointments were more negatively received. Leveraging the time CEOs spend in their role as a conceptual bridge between the two perspectives, we argue that the acquisition hazard under high strategic uncertainty increases over early tenure, as CEOs gather information and learn. Analysing 873 new US CEOs (2004–2020) with an extended Cox hazard model, we find a generally higher hazard of first acquisition announcements for outsider CEOs and those with more negative appointment reactions, especially for bolder deals. Evidence on time dependence is mixed, but more pronounced for outsider CEOs and large acquisitions.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".