An AI-optimized strategy for intelligent risk mapping of Islamic and conventional sustainable markets: Assessing the enduring dynamics of technological risk spillovers
حسن حیدری
- 发表年份
- 2025
- 引用次数
- 4
摘要
This study explores the lasting impact of industries influenced by Robotic-Artificial Intelligence-Cloud (RAIC) technologies on risk management in both conventional and Islamic sustainable markets, employing a novel AI-driven framework. By utilizing the Quantile-based Total Connectedness Index (QTCI) to gauge market interconnectedness and Long Short-Term Memory (LSTM) neural networks to evaluate risk persistence, the research investigates how sectors such as autonomous vehicles, cybersecurity, cleantech, and future payments influence financial stability across different market conditions (bull, bear, and normal). The findings reveal divergent risk dynamics: Islamic markets are more sensitive to technological disruptions, particularly in robotics and cybersecurity, while conventional markets show more stable integration with sectors like smart grids and space technologies. Cleantech shows a tendency to coincide with decreased market volatility during bear markets, while future payments demonstrate widespread interconnectedness across all market conditions. AI-driven analysis highlights those Islamic markets excel in risk mitigation during stable conditions but conventional markets exhibit greater adaptability in the face of change. The QTCI-LSTM hybrid approach identifies differences in risk persistence, showing that technologies like genetic engineering and nanotechnology have transient effects in Islamic markets but more enduring roles in conventional markets. The study offers policy recommendations for sector-specific strategies, advocating for enhanced resilience in volatile sectors during bull markets, prioritizing cleantech during downturns, and encouraging cross-market collaboration. This work contributes to sustainable finance literature by integrating AI-powered persistence analysis with traditional risk metrics. The findings offer insights for policymakers managing technological integration in evolving markets.
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