The Impact of Corporate Governance on the Profitability of Insurance Firms in Syria
Bibliographic record
Abstract
This paper investigates the impact of corporate governance on the profitability of joint-stock insurance firms listed and unlisted in the ‘Damascus Securities Exchange’ in Syria during the period from 2013 to 2019. Research data was collected from the financial reports of the insurance firms and the reports of the ‘Syrian Insurance Supervisory Commission’ and the ‘Syrian Commission of Financial Markets and Securities’. A model was then proposed for corporate governance of insurance firms. The proposed model considers eight independent variables (board size, independence of board members, non-executive board members, solvency, ownership, firm size, firm age, and joint-stock) and two dependent variables (return on equity and return on shares). Multi regression analysis for the ‘Balanced Panel Data’ is used to analyze the relationship between the governance of insurance firms and their profitability. The analysis of the results revealed that some independent variables (such as firm size, ownership, and none-executive members) have a significant positive impact on the return on equity while the ‘firm size’ and ‘ownership’ independent variables have a significant positive impact on the return on shares. The ‘solvency’ variable has a significant negative impact on the return on equity and return on shares. On the other hand, the ‘firm age’ variable has a significant negative impact on the return on equity while the ‘joint-stock’ variable is considered statistically significant with a positive impact on the return on shares. It was also observed that the ‘independence of board members’ variable has no impact on the dependent variables.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".