Do CEOs’ characteristics affect compliance with IFRS 7 risk disclosure requirements?
Bibliographic record
Abstract
Purpose This paper aims to examine the relationship between CEO’s attributes and the level of compliance with financial instruments risk disclosure (hereafter FIRD) as required by International Financial Reporting Standard (IFRS) 7. Design/methodology/approach A data set of financial institutions listed on the Toronto Stock Exchange over the period 2015–2020 has been analyzed. Panel regressions have been estimated to provide empirical support for the testable hypotheses. Findings The research findings reveal that chief executive officer (CEO) compensation and financial expertise are positively associated with the level of FIRD provided by Canadian financial institutions. However, the analysis does not document any significant statistical linkage between the compliance score and CEO tenure, gender and age. Practical implications This study has important implications for stakeholders evaluating the determinants of reporting quality, for boards of directors considering CEO compensation and expertise and for standard setters considering the compliance level with new standards requirements. Originality/value This paper provides novel evidence on the linkage between CEO attributes and corporate disclosure. To the best of the authors’ knowledge, this paper is among the first to explore the impact of CEO characteristics on compliance with International Accounting Standards Board disclosure requirements. The analysis is also among the first to investigate compliance with IFRS 7 before and after the amendments required by IFRS 9.
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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 | 0.006 | 0.032 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".