أثر تصنيف الاستدامة البيئية والاجتماعية والحوكمة في السعر السوقي للسهم : دراسة تطبيقية على البنوك الاسلامية العاملة في دول مجلس التعاون الخليجي
Bibliographic record
Abstract
ملخص البحث هدفت هذه الدراسة إلى بيان أثر تصنيف الاستدامة البيئية والاجتماعية والحوكمة (ESG) من قبل شركة Sustainayltics في السعر السوقي لسهم البنوك الإسلامية العاملة في دول مجلس التعاون الخليجي خلال الفترة من 2022-2024. واشتملت عينة الدارسة على (12) بنكا إسلاميا حاصلا على تصنيف من قبل الشركة ،واتبعت الدراسة منهجا وصفيا تحليلا باستخدام معادلة الانحدار الخطية البسيطة . وقد توصلت الدراسة إلى وجود أثر إيجابي وبعلاقة متوسطة لتصنيف الاستدامة والسعر السوقي، وأنه لا يوجد أثر ذا دلالة إحصائية لتصنيف الاستدامة والسعر السوقي لأسهم البنوك قيد الدراسة. وأوصت الدارسة بضرورة إفصاح البنوك الإسلامية عن تقارير الاستدامة، وعمل دراسات لفترة زمنية أطول وعينة تشمل بنوكا إسلامية أشمل، وإدخال متغيرات تؤثر على السعر السوقي لإثراء الدراسة. ABSTRACT This study was aimed at demonstrating the impact of the ESG classification on the market price of Islamic banks operating in the GCC from 2022-2024. The study sample included 12 Islamic banks with a rating was conducted by Sustainayltics .The study followed a descriptive analytical approach using a simple linear regression equation. The study found a positive impact and an intermediate relationship to the classification of sustainability and market price, there is no statistically significant impact of the sustainability classification and market price of the banks' shares under consideration. The study recommended that Islamic banks should have disclosed sustainability reports and longer-term studies with more comprehensive and variables sample affecting the market price in order to enrich the study.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.286 | 0.216 |
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".