Impact of Engineering Financial Market and Using Financial Derivatives on Financial Analysts Interest: Empirical Study from Amman Stock Exchange –Jordan
Bibliographic record
Abstract
This study aimed to measure the level of Amman Financial Market efficiency and show the role of financial derivatives in improving Markets’ efficiency. Generally, financial derivatives are considered an essential source of financing an economy. In addition, diversification of financial derivatives’ instruments which are circulated in a stock market as the main standard of measuring its development and efficiency, called as engineering. For the purpose of achieving the objectives of the study, the researcher selected some of the financial analysts’ that represent institutions in Amman Stock Exchange in Jordan, to serve as the sample of the study. Simple Random Sampling was used to select the sample to represent the whole population. Furthermore, the researcher developed a questionnaire to judge the respondents opinions and test the validity of the hypotheses. The questionnaire was designed by using a five -point -Likert scale (strongly agree, agree, neutral, disagree, strongly disagree). In total, the researcher distributed 100 questionnaires and 85 were returned from the selected sample of the study (85% from the whole questionnaires distributed). Results of the study showed that there was a strong bond between financial derivatives and efficiency of Amman Stock Market. Moreover, findings indicated that there is a statistical significant sign between financial analysts’ interests in returns of financial derivatives with their tools and efficiency of these markets.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".