Determinants of corporate performance: Empirical evidence from the insurance companies listed on Abu Dhabi securities exchange (ADX)
Bibliographic record
Abstract
The purpose of this study is to identify factors that impact the performances of the insurance companies listed on ADX. Factors employed in this study include liquidity, general and administration expenses, risk, size, tangibility and age. The annual financial statements of all seventeen insurance companies listed on ADX covering the period 2013-2019 were sampled and analyzed through a panel regression. The analysis indicates that corporate age is the most significant positive factor that determine the profitability of the insurance companies listed on ADX. The durability of the insurance company in the GCC countries suggests that the firm has created good image, attract more customers, increased revenues to cover expenses and make profit. Thus, age is an important positive factor of the performance of insurance companies listed on ADX. Moreover, it is obvious that dissatisfied customer with the service of an insurance company will not only cease dealing with it, they deliver bad news about it by using the word of mouth and the powerful social media that play efficient role in formulating the image about the company. The outcome of this study might help investors in formulating their decision to invest in an insurance company. For instance, it helps them to focus on the age of the insurance company before they make their decision.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".