Bibliographic record
Abstract
This research aims to explore the factors influencing the decision of investment in the Saudi Arabian stock market which are the following factors: the analysis and financial ratios, the history and reputation of the company, dividends, date of the company’s founding, company size, recommendations and opinions of analysts, the nature of the company's activity) and its impact on investment decision. A questionnaire has been used as a research tool, the study group consisted of all individuals investing in the Saudi stock market in 2018, and the sample was selected in an available way which is one type of non-random sample, with a total of 128 valid questionnaires for analysis out of 130 questionnaires. The research on its theoretical side touched on the efficiency and importance of the market, the types of financial markets, the formation and evolution of the Saudi stock market, the period of collapse of the Saudi stock market, the post-collapse Saudi stock market. While the tests of reliability and stability (Cronbach's alpha coefficient), frequencies, percentages, arithmetic averages and standard deviation, (T- test) for differences between two independent samples (independent sample T-test), one-way analysis of variance (one-way ANOVA) were used in the practical aspect of the research. The research has found an effect of the following factors: analysis and financial ratios, the history and reputation of the company, dividends, date of the company’s founding, company size, recommendations and opinions of analysts, the nature of the company's activity) on investment decision.The most influential factor on investment decision is the analysis and financial ratios, followed by the reputation and history of the company, dividends and its impact on investment decision ranked third, while date of the company’s founding ranked fourth in terms of influencing investment decision, while the nature of the company's activity ranked fifth in terms of impact, followed by company size in the sixth rank, and finally recommendations and opinions of analysts ranked seventh and last in terms of influencing the decision of investment in the Saudi stock market.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".