Date of Award
Spring 2026
Degree Type
Honors College Thesis
Academic Program
Finance BSBA
Department
Finance, Real Estate, and Business Law
First Advisor
Srinidhi Kanuri
Advisor Department
Finance, Real Estate, and Business Law
Abstract
Artificial intelligence exchange-traded funds (AI ETFs) have expanded rapidly as investors seek targeted exposure to firms developing or benefiting from AI technologies. This study compares the performance of 50 AI ETFs with two broad-market ETFs, SPY (S&P 500) and RSP (equal-weight S&P 500), from January 2018 to January 2026. AI ETFs exhibit higher expense ratios and strong correlations with both benchmarks, reflecting their concentration in large-cap technology stocks. Over the full sample, AI ETFs generated higher cumulative returns but with substantially greater volatility. Sub-period analysis further decomposes the post-COVID period into pre-ChatGPT (April 2020–November 2022) and post-ChatGPT (December 2022–January 2026) windows, finding that AI ETF outperformance is concentrated in the post-ChatGPT era. However, Sharpe ratios indicate that SPY delivered the highest risk-adjusted performance over the full period. Overall, AI ETFs offer higher return potential but consistently expose investors to greater risk.
Copyright
Copyright for this thesis is owned by the author. It may be freely accessed by all users. However, any reuse or reproduction not covered by the exceptions of the Fair Use or Educational Use clauses of U.S. Copyright Law or without permission of the copyright holder may be a violation of federal law. Contact the administrator if you have additional questions.
Recommended Citation
Malhotra, Aryan, "Comparing the Risk and Return Characteristics of AI Exchange-Traded Funds To Benchmark Stock Market ETFs" (2026). Honors Theses. 1111.
https://aquila.usm.edu/honors_theses/1111