Examining the Effect of Selective Macroeconomic Variables on The Stock Exchange’s Depth and Breadth (Case Study: Tehran Stock Exchange)
Bibliographic record
Abstract
One of the features of developed countries is the existence of effective financial markets, which not only play an important role in the economy, but also facilitate economic growth and a country's development. Stock exchangedevelopment is affected by many macroeconomic variables. In this survey, we mainly attempt to examine the effect of macroeconomic variables on the development of the Tehran Stock Exchange. To do so, national income, investment rate, financial intermediary development and macroeconomic instability are considered as macroeconomic variables, and depth and breadth are considered as indices of the stock exchange development. Necessary data were collected seasonally during 1998-2007. For statistical analysis, we first examined stationary of the variables by augmenting the Dickey-Fuller Unit Root Test. Then, the Johansen co-integration Test was used to estimate co-integration vectors. Finally, we used the Vector Error Correction Model to test the research model. Findings suggest that national income and investment rates have a positive, significant effect on the depth and breadth of the stock exchange. Also, financial intermediary development and macroeconomic instability have a negative, significant effect on the depth and breadth of the stock exchange.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".