Type of Traders’ Effect on Risk and Return: The Case of Egyptian Stock Exchange
Bibliographic record
Abstract
This research paper aims to estimate the effect of investor categories (Foreigners, Arab, Egyptian institutions and individuals) trading volume, value and number of transactions on capital market returns and volatility. We depend on data Foreigners, Arabian and Egyptian trading volume, values and number of transaction of buying and selling for institutions and individuals and capital market values for the period from January 1st 2009 to December 31 2013. We used descriptive statistics to identify normal distribution of data. Then, performing lead lag structure approach to obtain the optimum lag for the independent variable which has the maximum correlation with the dependent variable. Next, Garch model utilized to estimate the effect of trading volume, value, number of transactions on capital market return and volatility. Finally, the same model utilized to estimate the effect of investor categories on capital market return and volatility for the six periods starting from January 1st 2009 to December 31 2013 which represents the whole period and five yearly periods for the same period. We found that institutions are the main source of volatility in the Egyptian stock market. Garch models showed weak effect on volatility for all periods. In the light of this study Foreigners and trading value items are the main source of effect on volatility. Finally, consistent with Chou (1988), the findings of GARCH model indicated that volatility persistence is less than unity which revealed that the Egyptian stock market could absorb shocks across time.
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".