Artificial Intelligence in Investment: Analysing Variations, Functions and Market Impact

Abstract

The integration of Artificial Intelligence (AI) in the investment domain has gained significant traction, yet the existing body of research remains fragmented, lacking a cohesive understanding of AI's varied forms, functions, and broader market implications. This study addresses this gap by systematically reviewing and consolidating scattered insights on AI applications in investment, organizing them into a structured and comprehensive framework for better understanding. Through a systematic review methodology, it categorizes 12 distinct AI variations based on their specific functions, including predictive analytics for market forecasting, automated trading systems, stock screening tools, and automated technical analyses. The market impact of AI is deductively analyzed based on a thorough understanding of its capabilities, definitions, and functionalities, revealing that while AI enhances trading performance through improved efficiency, accuracy, and liquidity, it also introduces challenges such as increased market volatility and potential marginalization of smaller investors, favoring larger institutions. By synthesizing fragmented literature into a cohesive reference, this study provides a clear and comprehensive understanding of AI's diverse functions in investment activities and their broader market implications. It offers critical insights for scholars and practitioners, advancing the discourse on equitable AI adoption and sustainable market practices. This research not only serves as a valuable reference for understanding AI in investment but also lays the groundwork for future innovations and policy interventions in the field.