التنبؤ بعوائد الأسهم للشركات المدرجة في سوق دمشق للأوراق المالية باستخدام معلومات أساس الاستحقاق
Bibliographic record
Abstract
هدف البحث الحالي إلى التعرف على إمكانية استخدام معلومات أساس الاستحقاق (الاستهلاك والمؤونات) في التنبؤ بالعوائد السوقية لأسهم للشركات المدرجة في سوق دمشق للأوراق المالية. طُبقت الدراسة على عينة مكونة من (11) شركة مدرجة في سوق دمشق للأوراق المالية، وذلك خلال الفترة الزمنية الممتدة من الربع الأول لعام 2010 ولغاية الربع الثاني لعام 2014. تم اختبار الفرضيات باستخدام أسلوب الانحدار البسيط والمتعدد. توصل البحث إلى أنه لا يمكن لمعلومات أساس الاستحقاق التنبؤ بعوائد الفترة التالية لأسهم الشركات المدرجة في سوق دمشق للأوراق المالية إلا بعد إدخال المتغيرات الضابطة المتمثلة بالقيمة الدفترية إلى القيمة السوقية، المخاطر المنتظمة ومعدل الربح إلى السعر السوقي. This research aims to identify the possibility of using the information of accruals (Depreciation and Provision) basis in predicting stock market returns for companies listed at Damascus Security Exchange. The study is applied on (11) companies during the period from the first quarter of 2010 until the second quarter of 2014. The hypotheses are tested using approach of simple and multiple linear regression. The research conclude that the information of accruals basis can’t predict stock market returns for next period for companies listed at Damascus Security Exchange, unless the control variables are entered, Book to Market ratio, Beta & Earning to Price ratio.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.168 | 0.135 |
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".