The Impact of Applying the Accounting Disclosure in Accordance with the IFRS on Increasing Profitability in Listed Banks: An Analytical Study
Bibliographic record
Abstract
The paper provided a content analysis on the impact of applying mandatory and voluntary disclosures (MD&VD) in accordance with selected IAS and IFRS (independent variables) on increasing profitability measured by the net profit margin (the dependent variable) in banks listed at Damascus Stock Exchange (DSE). Data from a sample of 11 banks and their financial statements, including all notes, during period from 2010 till 2014 were gathered and processed using the statistical package of social sciences. The main results showed different significant impacts of applying mandatory and voluntary accounting disclosures under selected IFRS on increasing profitability in Syrian banks. However, when testing the separate application of each selected IAS and IFRS, results showed no such impacts, except for IAS18, on increasing profitability in Syrian banks. The main conclusion indicated that there is a need to develop IFRS internationally to lowering flexibility in selected IFRS aiming at increasing mandatory accounting disclosures, and some examples were provided for this purpose. The research also concluded that the Syrian SEC should harmonize with other governmental agencies to commit full application of all IAS and IFRS and, on the other hand, Syrian banks should be encouraged to disclose numerical and descriptive accounting disclosures in line with the IAS and IFRS within the Syrian 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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".