Do CEOs’ characteristics affect compliance with IFRS 7 risk disclosure requirements?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it