CEO–CFO Compatibility and Audit Risk
Bibliographic record
Abstract
Abstract This study examines the influence of CEO–CFO compatibility (proxied by the similarity of their personalities) on audit risk (proxied by audit fees). Relying on similarity‐attraction theory, we posit that alignment between the CEO's and CFO's personalities − specifically their ‘Big Five’ traits − enhances internal communication, information sharing and decision‐making processes within the organization. This alignment, in turn, reduces audit risk associated with the firm's financial reporting. We test our theory using firm fixed effects and find that greater CEO–CFO personality similarity is associated with reduced audit fees. Further, we find that the tenure of the CEO–CFO relationship partially explains the relation between their personality similarity and audit fees. Finally, we find that the effect of CEO–CFO personality similarity on audit fees is stronger when corporate governance allows greater managerial autonomy, that is, CEO–CFO compatibility is more important for reducing audit risk when corporate governance is weak. Our results are robust after controlling for many other characteristics of the CEO and CFO and potential endogeneity related to CEO turnover.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".