The Islamic effect: Exploring the dynamics of Islamic events on sustainable performance of Islamic and conventional stock markets
Bibliographic record
Abstract
This study attempts to investigate the effects of Islamic events on both Islamic and conventional stock markets and analyze which market reacts more pronouncedly to these events. From 2012 to 2022, the research used daily stock return data from eight nations: Kuwait, India, Nigeria, Malaysia, Pakistan, Qatar, Saudi Arabia, and the United Arab Emirates. The study examines how Islamic holidays such as Ashura, Eid Meelad ul Nabi, Eid ul Azha, and Ramadan affect both Islamic and Western stock markets. The researchers use the Generalized Autoregressive Conditional Heteroscedastic (GARCH) model to analyze the data. The results of this analysis show that Islamic events in India, Nigeria, Malaysia, Pakistan, and Qatar have a strong and favorable link with Islamic stock returns. However, it was discovered that there is a little correlation between Islamic events and Islamic stock returns in the remaining three nations. The study also reveals a strong and favorable correlation between Islamic events and conventional stock performance in all countries. By offering a comparative analysis of the effect of Islamic events on Islamic stock markets and mainstream stock markets, these findings add to the body of current material. Every religion has its own set of rituals that its adherents observe, and these rituals frequently have an impact on different economic and non-economic activities. This study sheds light on the precise connection between these events and stock market performance by examining the impact of Islamic occasions on both Islamic and conventional stock markets.
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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 | 0.002 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".