Impact of Fintech on Portfolio Optimization, Investor Behavior and Financial Market Dynamics

AI-driven fintech solutions are being next generation methods being adopted wisely and widely. This emergence of FinTech has opened numerous unexplored research opportunities, particularly in the areas of portfolio optimization, investor behavior in the FinTech context, and market dynamics. Notably, the impact of AI-driven portfolio optimization on actual market performance remains unclear. The current study aims to evaluate the performance of AI-driven portfolio optimization in comparison with traditional approaches, within the context of FinTech innovations. The research focuses on retail investors using FinTech platforms and financial market analysts, utilizing 100 days of secondary data. Results indicate that AI-powered portfolio optimization slightly outperforms traditional methods in terms of returns. AI solutions provide comparatively more efficient and cost-effective outcomes while also reducing downside risk. A comparative analysis of performance metrics for AI-driven and traditional portfolio optimization approaches was conducted. Interestingly, traditional optimization methods showed higher CAGR and Sharpe ratios, suggesting better long-term compounded growth and risk-adjusted returns. However, the complete impact of FinTech innovations on investment return optimization, investor behavior, and portfolio performance remains largely unexplored. Future research could expand on these findings by investigating the influence of behavioral biases, algorithmic trading, risk dynamics, and evolving regulatory frameworks and policies in the FinTech ecosystem.