AI-heavy assets are more closely aligned with Shariah and ESG portfolios than with conventional investments, with the strongest ties to Shariah funds; during downturns AI tends to transmit downside shocks to sustainable investments, especially at longer horizons.

Main Finding

AI-related assets show stronger alignment with socially responsible investments than with conventional investments over May 18, 2018–April 14, 2023. The strongest synergy is with Shariah‑compliant investments (likely reflecting their higher information‑technology composition). Co-movement between AI and all investment categories increases from short (weekly) to long (yearly) horizons in bear and normal markets, but the opposite holds in bull markets. At the distribution tails, AI acts as a significant shock transmitter (especially in downside tails), affecting Islamic, then ESG, then conventional investments most strongly. Bear markets are when AI most effectively influences sustainable investment opportunities; in normal and bull markets AI is more likely to receive shocks.

Key Points

• Methods: Quantile cross‑spectral analysis (captures dependence across frequencies/time horizons and distributional quantiles) and quantile‑VAR (identifies directional spillovers across quantiles/tails).
• Comparative scope: AI vs Shariah‑compliant (Islamic), ESG‑oriented, and conventional investments.
• Alignment ranking: AI ↔ Shariah (strongest) > AI ↔ ESG > AI ↔ Conventional.
• Horizon dynamics: Coherence (co‑movement) intensifies from weekly to yearly horizons in bear and normal markets; decreases from short to long horizons in bull markets.
• Tail behavior: AI generates notable downside (lower‑tail) spillovers — largest effect on Islamic investments, then ESG, then conventional.
• Market‑state asymmetry: Bear markets amplify AI’s influence on sustainable investments; in normal and bull markets AI is comparatively more susceptible to incoming shocks.
• Interpretation: The greater compatibility with Shariah investments is potentially driven by those portfolios’ heavier IT exposure.

Data & Methods

• Data period: May 18, 2018 – April 14, 2023 (market‑level/asset‑class time series; paper compares AI exposures to Shariah, ESG, and conventional investment categories).
• Time‑scale analysis: Frequency decomposition from short (weekly) to long (yearly) horizons via quantile cross‑spectral methods to capture how dependence varies across time scales and market quantiles.
• Distributional/spillover analysis: Quantile‑VAR to estimate directional spillovers conditional on different parts of the return distribution (tails vs center), allowing identification of who transmits vs who receives shocks in bear/normal/bull states.
• Advantages of approach: Jointly accounts for frequency (time‑scale) and distributional (tail/quantile) heterogeneity in co‑movements and shock transmission — important for understanding risk and sustainability interactions that differ by horizon and market state.
• (Note: The abstract does not detail exact indices/proxies or model specifications; refer to full paper for variable definitions, estimation settings, and robustness checks.)

Implications for AI Economics

• Portfolio construction: Investors in sustainable or Shariah‑compliant products should consider AI exposure as a potentially complementary allocation, especially for long‑horizon strategies and during adverse market states.
• Risk management: Tail‑dependent spillovers from AI imply downside contagion risks to sustainable investments; stress testing and tail‑risk hedging should explicitly account for AI shocks.
• Policy & regulation: Regulators and standard‑setters should factor in how AI‑intensive sectors interact with sustainable finance—particularly in crises—when designing disclosure, systemic‑risk monitoring, and stability safeguards.
• Technology governance & ethics: Findings underscore an ethical/economic linkage: AI’s market behavior meaningfully interacts with values‑based finance, suggesting a role for technology ethicists and green‑economy planners in aligning AI development with sustainability objectives.
• Research directions: Future work should unpack mechanisms (e.g., portfolio composition, sectoral IT concentration), test alternative AI exposure definitions, and evaluate policy interventions that mitigate adverse tail spillovers while preserving positive synergies with sustainable finance.