A Study of the Effect of Macroeconomic Variables on Stock Market: Saudi Perspective
Bibliographic record
Abstract
As we know investment in the Saudi stock market (TASI) is gaining popularity particularly since 2001 (Report of Aljazira Capital – 2010), it is important to know the impact of different macro-economic variables on the returns of the Saudi stock market. In this regard along with an extensive literature review we also referred specific recent few important studies which considered the Saudi stock return as a dependent variable and other economic factors as the independent variables [Kalyanaraman and Al-Tuwajri (2014), Arouri and Rault (2010) and Onour (2008)]. This paper examines the three important factors influencing the returns in the Saudi Stock Exchange (TASI) based on the macroeconomic variables of Saudi Economy. The dependent variable taken here is the Saudi index that is TADAWUL All Stock Index (TASI) and the three independent variables considered for our study here are the Oil WTI, Saudi Exports and the PE Ratio. Correlation analysis revealed that Saudi Exports and the PE Ratio were found to be highly correlated with TASI at 1% level of significance whereas Oil WTI and TASI are significantly correlated at 5% level. Step-wise regression analysis of the data revealed that the multiple regression models is significant at 1% level and the variable PE Ratio was the most important determinant of TASI followed by Oil WTI and Saudi Exports. Further the three independent variables explain about 93% of variation in the TASI Last Price.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".