Do CEOs With a Financial Background Matter for the Success of Newly Public Firms?
Bibliographic record
Abstract
ABSTRACT We uncover strong evidence that newly public firms run by financial expert chief executive officers (CEOs) have a lower probability of involuntary delisting and a longer survival time in the aftermarket. This result is robust to alternative definitions of long‐term viability and endogeneity concerns. Our cross‐sectional analysis reveals that the positive effect of financial expert CEOs on initial public offering (IPO) survival is more pronounced in large and complex firms but weaker in dynamic settings. Additional tests show that CEOs with a career background in finance gain better access to the primary equity market than other domain experts, as evidenced by a more efficient price discovery process and greater financial visibility in the aftermarket. Furthermore, these CEOs are associated with more efficient post‐IPO outcomes which lie at the core of their skills set, such as capital expenditures and acquisitions, rather than research and development (R&D) projects, which are typically outside their domain of expertise.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.000 |
| 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".