THE CONVERGENCE OF NEWS ANALYTICS AND SOCIAL MEDIA IN AI-DRIVEN TRADING: A COMPREHENSIVE REVIEW

Abstract

The rapid evolution of artificial intelligence and machine learning has revolutionized the landscape of algorithmic trading, particularly in the realm of news and social media analysis. This comprehensive study examines the development, implementation, and impact of AI-driven trading algorithms that leverage real-time news and social media data to inform investment decisions. By synthesizing cutting-edge research in natural language processing, sentiment analysis, and high-frequency trading, we provide a critical analysis of the technical architecture, performance metrics, and ethical implications of these systems. Our investigation explores the challenges of data quality, algorithmic bias, and regulatory compliance. Furthermore, we discuss the future directions of this technology, including the integration of alternative data sources and advancements in deep learning. This article contributes to the growing body of literature on the intersection of finance and technology, offering insights for academics, practitioners, and policymakers navigating the complex terrain of AI-powered trading in the digital age.