Measuring the effect of disclosure quality of integrated business reporting on the predictive power of accounting information and firm value
Bibliographic record
Abstract
This paper measures the effect of disclosure quality of integrated business reports on the predictive power of accounting information and firms' value in the Egyptian Stock Market. In order to achieve the research objectives, the research relies on content analysis approach in examining the annual reports of the companies listed in the Egyptian Stock Exchange from 2015 to 2018. The study depends on measuring the independent variable i.e. disclosure quality of the integrated business reports on building up a disclosure index consisting of 45 items in 8 groups equally weighted, whereas; dependent variables which represents the predictive power of accounting information measured by adopting three different methodologies; namely Accounting Conservatism, Share Prices, and Discretionary Accruals. Concerning to firm value, the study uses Tobin's Q model to measure the relationship between the quality disclosure of the integrated business reports and the firm value. The results indicate that the quality disclosure of integrated business report leads to increase accounting conservatism and share prices, whereas the statistics analysis reports a negative effect towards discretionary accruals indicating that the quality disclosure of integrated business report leads to decrease in discretionary accruals.
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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 | 0.008 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".