Connectedness of AI and Islamic stocks: Evidence from frequency-domain quantile regressions
Bibliographic record
Abstract
This study explores the dynamic relationship between artificial intelligence (AI)-based stocks and Islamic stock indices. Motivated by the rising prominence of AI in global finance and the ethical appeal of Islamic investing, the study investigates whether AI stocks offer hedging, diversification, or safe haven potential relative to Islamic assets. Daily data from January 1, 2019, to April 13, 2024, is employed, covering the NASDAQ CTA Artificial Intelligence and Robotics Index (as the proxy for AI stocks), along with the Dow Jones Islamic Market World Index, United States and Canada. Using the Empirical Mode Decomposition (EEMD) with Quantile Regression techniques, the analysis captures the asymmetric relationships across different investment horizons and market states. The results reveal that AI stocks generally move in tandem with Islamic stock indices during stable and bullish markets, offering limited diversification benefits. However, in bearish markets, particularly over the long term, AI stocks exhibit a negative relationship with the global and U.S. Islamic indices, indicating potential safe haven or hedging roles. No such hedging benefit is observed concerning the Canadian Islamic index. Additionally, Islamic indices do not serve as effective hedges for AI stocks across any market regime. These results offer detailed insights into the asymmetric co-movement patterns between AI-related stocks and Islamic equities. It offers practical implications for ethical and tech-focused investors, suggesting that AI stocks may enhance portfolio resilience under specific market conditions. Policymakers and financial product designers can also leverage these insights to integrate emerging technologies into Shariah-compliant investment strategies better.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".