Corporate Governance and Obfuscation in Chairmen’s Letters: The Case of MENA Banks
Bibliographic record
Abstract
The readability (RDB) of annual reports (ARs) plays a crucial role in determining the effectiveness of disclosure of information to interested parties, particularly investors. Given that investors rely on the financial information provided in ARs, the chairman’s letter serves as a key communication tool and is the most extensively read section of the report. Consequently, companies are under pressure to provide understandable ARs that can be easily interpreted by investors. Nevertheless, managers sometimes obscure such disclosures in an attempt to bury negative information and hide their own behavior. Drawing from the “managerial obfuscation hypothesis”, this study investigated how the corporate governance (CG) structures affect the RDB of ARs for a sample of 95 banks across seven countries in the MENA region from 2018 to 2022. The findings revealed that board size, frequency of board meetings, and ownership concentration significantly affected the RDB of ARs. Additionally, board independence and gender diversity had a significant negative effect on ARs’ RDB. Conversely, the study found that the presence of role duality within the board had an insignificant effect on ARs’ RDB. As a result, this study recommends enhancing CG structures to enhance the clarity of banks’ reports and boost investor trust.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".