The Impact of the Cryptocurrency Market on Islamic vs. Conventional Stock Returns: Evidence from Gulf Cooperation Council Countries
Bibliographic record
Abstract
The rapid rise and widespread global adoption of cryptocurrencies in recent years has fundamentally transformed the international financial landscape, with digital assets increasingly being recognized for their potential to influence the stability and performance of traditional capital markets. Against this backdrop, this study aims to empirically investigate the impact of cryptocurrency returns on Islamic vs. conventional stock returns in Gulf Cooperation Council (GCC) countries. The salient distinctions between Islamic and conventional stock markets include fundamental differences in principles, investment allocations, and risk profiles, underscoring the importance of examining the impact of cryptocurrency returns on these distinct equity segments. Daily data were collected from stock indices in five GCC countries over the period 2016–2019, including two sub-periods: before and after the 2017 crypto crash. Pooled OLS, fixed effects, random effects, and generalized linear models (GLMs) were used to analyze the data collected during the study. With the GCC increasingly focusing on cryptocurrency markets, there is growing concern about these markets’ potential impact on regional stocks. This study addresses the important questions of whether the impacts of the cryptocurrency market on Islamic vs. conventional stock markets differ throughout the GCC region and how these impacts have evolved since the crypto crash period. The findings reveal that cryptocurrency returns had a negative impact on both GCC Islamic and conventional stock market returns for the full sample period (2016–2019), and the negative effect was far more pronounced for conventional stocks. For the two sub-periods before and after the crash, only the cryptocurrency market and conventional GCC stocks remained negatively correlated, while the cryptocurrency market and the GCC Islamic stock markets became uncorrelated. Thus, for the calmer sub-periods before and after the crypto crash, the rise in cryptocurrency returns may have enticed GCC investors away from conventional stocks, perhaps resulting in a decline in their investment in these stocks. Meanwhile, those who invest in Islamic stocks may not be exposed to this temptation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".