Economic Forces and the Stock Market Performance in Developing Countries: Evidence From Sudan
Bibliographic record
Abstract
This paper is an attempt to empirically investigate the determinants of the stock market performance in Sudan. It aims at identifying the short and long run relationships between the Khartoum Stock Exchange all- share price index (KSI) as an indicator of market performance and some selected micro and macro-economic factors. The inflation rate, cost of capital, foreign exchange rate, broad money supply, and crude oil price are chosen as proxies for macroeconomic factors. The market oriented indicators used include market capitalization, market trading system, and market trading volume. The study covers the period 2003-2017. The paper employs the Multivariate Time Series Regression Analysis to estimate the short run relationship between the selected independent variables and the KSE price index. Soren Johansen’s Cointegration Test and Vector Error Correction Model (VECM) have been employed to identify the long run equilibrium relationship among the variables. To estimate the causal relationship between the selected variables Toda-Yamamoto (T-Y) Granger Causality Test has been utilized. The study documents that the Khartoum Stock Exchange performance is significantly affected both by micro and macroeconomic factors. In the long run, all the independent variables with the exception of the cost of capital, have a significant positive relationship with KSI. However, in the short run the determinants of the stock market performance are market capitalization, market trading volume, money Supply, and cost of capital.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".