Insider trading and regulatory inconsistencies have important historical challenges to the integrity and stability of global financial markets. These issues challenge trust, transparency, and fairness are requiring solutions. In this study, we introduce a novel artificial intelligence (AI)-driven system that carefully addressing these challenges. The proposed system employs machine learning models for insider trading detection, natural language processing (NLP) for sentiment analysis, and graph neural networks (GNNs) to detect irregular patterns in blockchain transactions. Moreover, reinforcement learning techniques are utilized here to complement regulatory standards dynamically, enhancing policy flexibility and market agreement. Explainable AI (XAI) were used here as well to ensure the transparency and trust in decision-making processes, this helps stakeholders to take actions. Experimental evaluations prove the system efficiency, with promising precision and recall percentages, enhanced governance in decentralized systems, and robust cross-jurisdictional regulatory alignment. This research contributes to knowledge by proving the transformative prospective of AI in strengthening regulatory frameworks and improving governance mechanisms in financial systems. The achievements here provide a roadmap for policymakers, financial institutions, and technology developers to build reasonable, efficient, and resistant markets.El tráfico de información privilegiada y las inconsistencias regulatorias han sido desafíos históricos importantes para la integridad y estabilidad de los mercados financieros globales. Estos problemas desafían la confianza, la transparencia y la equidad y requieren soluciones. En este estudio, presentamos un nuevo sistema impulsado por inteligencia artificial (IA) que aborda cuidadosamente estos desafíos. El sistema propuesto emplea modelos de aprendizaje automático para la detección de tráfico de información privilegiada, procesamiento del lenguaje natural (NLP) para el análisis de sentimientos y redes neuronales gráficas (GNN) para detectar patrones irregulares en transacciones de blockchain. Además, aquí se utilizan técnicas de aprendizaje de refuerzo para complementar los estándares regulatorios de forma dinámica, mejorando la flexibilidad de las políticas y el acuerdo del mercado. Aquí también se utilizó IA explicable (XAI) para garantizar la transparencia y la confianza en los procesos de toma de decisiones, lo que ayuda a las partes interesadas a tomar medidas. Las evaluaciones experimentales prueban la eficiencia del sistema, con porcentajes prometedores de precisión y recuperación, una gobernanza mejorada en sistemas descentralizados y una sólida alineación regulatoria interjurisdiccional. Esta investigación contribuye al conocimiento al demostrar la perspectiva transformadora de la IA en el fortalecimiento de los marcos regulatorios y la mejora de los mecanismos de gobernanza en los sistemas financieros. Los logros aquí alcanzados proporcionan una hoja de ruta para que los responsables de las políticas, las instituciones financieras y los desarrolladores de tecnología construyan mercados razonables, eficientes y resistentes.

In this article, the co-movement between GCC and US stock market returns was investigated using the wavelet coherence method. The Dynamic Conditional Correlation GARCH (DCC-GARCH) modelling is then applied on time-varying components in order to provide a point of comparison with the results extracted from wavelet analysis. The investigation was conducted on the weekly stock index prices of two USA stock markets, namely Dow Jones and S&P 500 and six GCC stock markets, namely the United Arab Emirates, Saudi Arabia, Qatar, Oman, Kuwait, and Bahrain. The data were retrieved from Thomson Reuters’s data stream and the sample duration was from 7 January 2007 to 24 June 2018. As a result, a definite co-movement between several GCC stock markets and those of the US stock markets for a long term was found. Moreover, the results also displayed signs of the significant disparity between the co-movements of the stock markets throughout the scales of time during economic decline. This phenomenon was possibly expected during the economic decline, where a significant divergence occurred as opposed to co-movement. The implications of the findings for global investors were considerable due to the indication from long-term co-movement that these investors would not be capable of gaining simultaneous profit from time and portfolio being diversified. In fact, the results showed the major difference in the opportunities for international portfolio diversification throughout these markets in terms of scale and time.

This study investigates the relationship among electricity consumption, per capita GDP, and CO₂ emissions for the Kingdom of Saudi Arabia. Trade openness and urbanisation are included within a multivariate approach. Using data covering the 1971–2018 period, the wavelet-coherence analysis is applied to assess the interactions amongst variables. All variables except trade openness appear highly correlated with CO₂ emissions. This is further confirmed by our causality testing procedure (i.e. the Johansen and Juselius test for cointegration and the Granger Causality test applied on a Vector Auto-Regressive model) which shows that electricity consumption, per capita income and urbanisation are crucial determinants of CO₂ emissions. Accordingly, adequate policy measures are proposed to turn the Saudi power sector towards a sustainable path.