Performance Evaluation of AI-Themed ETFs Versus Broad Market Benchmarks

Abstract

In recent years, AI-powered robo-advisors have emerged as low-cost alternatives to traditional human financial advisors, utilizing algorithmic strategies to optimize portfolio allocation and manage risk. As these platforms gain traction, evaluating their effectiveness in achieving strong, risk-adjusted returns is critical.

This study compares performance metrics across 12 ETFs, including diversified holdings typically used by robo-advisors (e.g., VTI, BND, AGG, IEFA, SCHB) and ETFs focused on the AI and technology sector (e.g., AIQ, ARKQ, ROBT, ARTY). Daily price data from January 2024 to July 2025 was analyzed using Google Sheets. We calculated daily returns, cumulative returns, annualized volatility, and Sharpe ratios. For example, ROBT produced a cumulative return of 1.63% with annualized volatility of 25.85%, while AGG had a 0.07% cumulative return with lower volatility at 5.10%. Risk-adjusted performance, measured by the Sharpe ratio (Rf = 0), ranged from -0.11 (SCHF) to +0.23 (ROBT), showing that while AI-themed ETFs may boost returns, they introduce higher risk. Meanwhile, bond-focused ETFs like AGG and SCHZ provided more stable performance with reduced downside risk.

Our findings support the hypothesis that robo-advised portfolios, composed of diversified ETF baskets, can achieve comparable or slightly lower returns than actively managed portfolios, but with significantly lower volatility and cost. This reinforces the case for robo-advisors as effective and efficient tools for portfolio management, particularly for passive investors who prioritize stability and fees over human oversight.