Paper Contents
Abstract
This research investigates the emerging trends and methodologies in retail and institutional investing, focusing on the integration of artificial intelligence and advanced analytical techniques in investment analysis. The study employs an extended Technology Acceptance Model (TAM) framework analysed through Partial Least Squares Structural Equation Modelling (PLS-SEM) to examine the relationships between technological innovation, predictive performance, risk management, decision-making efficiency, and ethical governance in investment strategies.The research utilizes a mixed-methods approach, collecting data from 220 investment professionals through a structured questionnaire. The sampling framework encompasses diverse stakeholders including institutional investors, financial analysts, and technology specialists, ensuring comprehensive representation of the investment ecosystem. The study examines five key hypotheses related to AI impact, data integration, risk assessment transformation, technological efficiency, and ethical AI adoption in investment analysis.Findings reveal significant correlations between advanced technological adoption and improved investment outcomes, with AI-driven strategies demonstrating superior predictive accuracy (>15% improvement) and risk management capabilities (25% reduction in portfolio volatility). The research contributes to the existing body of knowledge by providing empirical evidence of the transformative potential of AI in investment analysis while highlighting the critical importance of ethical considerations and governance frameworks.The study presents practical implications for investment professionals and institutions, offering insights into the effective integration of advanced technologies while maintaining ethical standards. Additionally, it provides a foundational framework for future research in technological innovation in investment analysis, particularly in the areas of quantum computing and federated learning applications.
Copyright
Copyright © 2025 Raghunandan M. This is an open access article distributed under the Creative Commons Attribution License.
