Corporate governance disclosure: Empirical evidence in the Portuguese capital market
Bibliographic record
Abstract
The corporate governance theme has been a subject of great debate due to the financial scandals of recent years. However, it is currently seen as a key factor for the success of organizations. This is because of the strong evolution that it has undergone over the years and the increase in financial market demands. Corporate governance is also seen as a crucial component in strengthening investor confidence. According to the literature, good corporate governance allows for the achievement of a degree of trust necessary for the proper functioning of a market. Currently, good corporate governance practices contribute to attracting investors, increasing stakeholder confidence, raising a company's reputation, and increasing business transparency among other benefits. The present study aims to analyse corporate governance disclosure in companies listed on Euronext Lisbon in 2020. To achieve this aim, we perform a content analysis of the corporate governance and annual reports as well as the consolidated annual accounts of a sample comprising 32 companies listed on Euronext Lisbon as at 31st December 2020. To analyse the extent of disclosure, a disclosure index is created based on the recommendations of the Portuguese Securities Market Commission; this makes it possible to measure the degree of compliance with recommended disclosures. Only disclosures related to the board of directors, the audit committee, the external auditor, and the statutory auditor are considered. The average value of the disclosure index is 0.977, with the most disclosed information related to the statutory auditor and the least disclosed related to the audit committee. This study contributes to a good understanding of corporate governance in the Portuguese context.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".