The Gap between the Returns that Calculated by Capital Asset Pricing Model and the Actual Returns in Abu Dhabi Securities Exchange (ADX): Evidence from the United Arab Emirates
Bibliographic record
Abstract
This study aimed to compare the Historical Returns (Rit) in companies listed in Abu Dhabi Securities Exchange (ADX) with the return which calculated by Capital Asset Pricing Model (E(Rit)) for the same companies and periods, and trying to figure out the level of dispersion, distortions and differences between them, and trying to figure out the strengths and weaknesses for the CAPM to explain the variances which happened in the Annual Return.The researcher used the time series analysis to achieve the target of this study, using Microsoft Office Excel software to introduce some figure and graphs which considered as output from Scatter charts, which are often used to find out if there's a relationship between variable X and Y to make judgment on the gap between the variables mentioned before.The researcher found that in the most of the study sample firms the capital asset pricing model could not to predict the returns were generated by companies in Abu Dhabi Securities Exchange (ADX), except in the banking sector, the result was amazing because the graphs which output from the time series analysis show the ability of CAPM to predict the Historical Returns, they were very closed and they Walking in the same direction without volatility.After the results appear in the time-series analysis researcher can says that there are weaknesses in the ability of CAPM to predict the returns in the financial markets which consistent with the (Fama & French, 1992) and with most studies conducted in this regard, but the model shows high ability to predict the returns in the banking sector. Therefore, the researchers can generalization this result on the financial markets in the United Arab Emirates. uage:EN-US;mso-fareast-language:ZH-CN;mso-bidi-language:AR-SA'>A critical demand for more nurses 30%-40% in certain units due to high work load. Most of the nurses were not satisfied about monitory compensation, participation in decision making and inadequate supplies.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".