Business Education of CEO-CFO and Annual Report Readability
Bibliographic record
Abstract
Financial report readability captures the transparency and effectiveness of information communicated by firms¡¯ executives. It¡¯s interesting to investigate whether business knowledge, cognitive preferences, and professional ethics taught by a business education will shape the CEO/ CFO¡¯s thinking in determining words, languages, paragraphs, and contents presented in financial reports when the self-interested CEO/CFO tends to influence the interpretation of financial information users. Using a sample of S&P 1500 CEOs and CFOs, we find that the CEO (CFO) with a business degree is associated with better (worse) readability of annual reports and the positive (negative) relation is strengthened (moderated) by internal corporate governance and external analyst following. Furthermore, we explore the interaction between CEO¡¯s and CFO¡¯s education background and provide additional evidence for the conflict of interest between CEOs and CFOs on financial reporting strategies. Our findings suggest business education, although has no direct impact on financial reporting quality, is beneficial to CEOs for strengthening their monitoring role in firm¡¯s financial reporting activities. Also, we consider that the ethical topics in business education can enhance financial executives¡¯ awareness to conduct practice under the ethical codes. Our study has implications for academic literature, business education, industry practitioners, and standards setting.
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 | 0.005 | 0.054 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".