The Extent and Determinants of Voluntary Disclosures in Annual Reports: Evidence from Banking and Finance Companies in Sri Lanka
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
Corporate disclosures are essential for every stakeholders. Hence, in excess of mandatory disclosures companies are voluntary disclosed information. The voluntary disclosure level is different from company to company and there may be some factors which are affect to this variation. Therefore, the objectives of this study are to identify the extent of voluntary disclosure level and its determinants. In order to achieve these objectives the study develop a voluntary disclosure index including 83 items and the nine sub categories which include in this index analyzed by employing content analysis in the annual reports of quoted public banking and finance companies for the time period of 2012 to 2015. Furthermore, this study analyze the selected variable to identify the determinants of voluntary disclosure level by employing panel data analysis. The study find that disclosures about general information, corporate environment, financial performance and risk management has more than 61% level and Corporate strategy, forward looking information, human and intellectual capital, competitive environment and outlook and corporate social responsibility information have less than 45% average in 2015 and it indicates that there is a much room for improvement in the context of voluntary disclosures. Furthermore, the study find that firm size, profitability, firm’s age, leverage and board independence as determinants of voluntary disclosure level and among them firm size, profitability and firm’s age have positive relationship and leverage and board independence has negative relationship.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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