Impact of Geopolitical Risks on Herding Behavior in Some MENA Stock Markets
Bibliographic record
Abstract
In this study, we examine the herding behavior in MENA stock markets in response to global geopolitical risk by using daily data, ranging from 4 January 2011 to 31 December 2023, on stock-listed companies in some MENA countries (Egypt, Jordan, Lebanon, Morocco, Saudi Arabia, and Tunisia) and the daily geopolitical risk index. In our analysis, we consider that investors’ behavior varies depending on the global economic and political period conditions. We use quantile regression analysis to investigate the effect of asymmetry on herding behavior among investors during bearish and bullish market conditions. The results show that herding behavior is evident in all stock markets, except for the Lebanon market, at a lower 5% quantile during down-market periods. A significant estimated coefficient of geopolitical risk was detected on the dispersion of stock returns, except for the stock markets of Morocco and Saudi Arabia. We found that a high level of geopolitical risk contributes to an increase in dispersion in the Lebanese stock market whereas it is associated with a high probability of increasing herding in the Jordanian and Tunisian stock markets. This paper contributes to the existing literature by explaining the impact of geopolitical risks on herding behavior in six MENA countries. This can be considered to be an empirical contribution as we propose to introduce the effect of geopolitical risks on the basis model of herding. Our findings can have significant implications for investors and policymakers in financial markets.
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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 | 0.003 |
| 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 | 0.000 |
| 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".