Examining macroeconomic determinants of the stock market performance: Evidence from Saudi Arabia
Bibliographic record
Abstract
This research examines Saudi Arabia's stock market performance from 1991 to 2021 while exploring the long-term and immediate effects of economic factors. The study employs Johansen cointegration tests alongside the Vector Error Correction Model (VECM) to analyse the influence of Gross Domestic Product (GDP), inflation, Foreign Direct Investment (FDI), trade balance and government expenditure on stock market performance. The research shows that macroeconomic variables affect market capitalisation results since GDP growth and trade openness lead to positive stock market performance; however, FDI and inflation create inconsistent effects. Results indicate the stock market maintains a long-term equilibrium between variables, leading to long-term correction of market deviations. The present study extends the discussion in the literature by offering a comprehensive and empirical analysis of the Saudi Equity Market as related to the economic diversification process, with a focus on the Saudi Arabia Vision 2030. This research calls for a solid and pro-active fiscal framework to stem inflation, attract good quality foreign capital and enhance the financial institutions. They are very useful in the formulation of strategic development plans that would foster long-term sustainable growth and increase investor confidence. The findings of this study are useful for policymakers, investors, and other analytical experts interested in the relationship between economic determinants and capital market returns, specifically within the context of Saudi Arabia, a young economy in the group of commodity-exporting countries. This study contributes original empirical evidence on the long- and short-run effects of macroeconomic indicators on stock market performance in Saudi Arabia. By applying the VECM approach across a 31-year period, it offers valuable insights for policymakers and investors in managing financial markets during economic diversification under Vision 2030.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".