Chief Financial Officers as Inside Directors
Bibliographic record
Abstract
Considerable prior research investigates whether the extent of insider presence on corporate boards is detrimental. However, the majority of past research treats all inside directors as a homogenous group. This study considers that issue in the context of chief financial officers (CFO) serving on their own company's board. Our research is important because individuals in different executive roles bring different skills and knowledge to board interactions, highlighting the potential for differential contributions. As prior research does not specifically distinguish CFOs from other board insiders, the potential benefits of knowledge sharing due to increased communication with other board members may have been masked. Specifically, the CFO is directly responsible for the quality of the financial reporting process and can therefore be associated with specific outcome measures. Our results show that the percentage of CFOs serving on their own boards is not large, likely due to the perspective (consistent with agency theory and reflected in independence guidelines) that company insiders on boards could promote their own best interest at the expense of shareholders. Contrary to this perception, we find that companies whose CFO has a seat on the board are associated with higher financial reporting quality (i.e., a lower likelihood of reporting a material weaknesses in internal controls or having a financial restatement, and better accruals quality). Yet, we also find potential drawbacks in that CFOs with a board seat tend to have higher excess compensation and lower likelihood of termination following poor performance, signaling greater entrenchment. While our results provide information to companies considering appointing the CFO to the board, both costs and benefits are demonstrated, and thus we conclude that each board should consider this decision based on its own circumstances and composition.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.004 |
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".