AI-Powered ETFs: A Quantitative Analysis of Performance, Risk, and Volatility in Financial Markets

Abstract

The rise of artificial intelligence (AI) in financial markets has prompted a significant shift in investment strategies, particularly in the management of Exchange-Traded Funds (ETFs). This study investigates the performance of ETFs utilizing AI-assisted strategies compared to traditional ETF management approaches. By hand-collecting a list of ETFs that employ AI algorithms in their investment strategies, we assess the return on investment, risk-adjusted returns, and volatility of these ETFs. The findings suggest that AI-assisted ETFs tend to outperform traditional ETFs in terms of higher Sharpe ratios and reduced portfolio volatility, highlighting the potential of AI integration in enhancing ETF performance. The effects are more pronounced in recent years, coinciding with significant advancements in AI. This paper contributes to the emerging field of AI in finance by demonstrating the tangible benefits of AI algorithms in real-world investment scenarios and setting a benchmark for future innovations in ETF management. The implications of this research are significant, suggesting potential trends toward more AI-integrated financial strategies in managing diversified investment portfolios.