AI-Augmented versus Traditional Active Fund Management: A Comparative Performance Analysis Since the Post-Pandemic Recovery (2021–2025)
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In this paper, I examine whether AI-managed equity funds outperform traditional actively managed equity funds on a risk-adjusted basis over the period since the easing of COVID-19 lockdowns. I compare two AI-driven funds, AIEQ and QRFT, against a traditional actively managed fund, Fidelity Contrafund (FCNTX), and the S&P 500 benchmark, using publicly reported annual total returns from 2021 through 2025. I calculate CAGR, volatility, and Sharpe ratio for each fund, estimate beta and alpha through a CAPM-style regression against the S&P 500, and test the difference in mean annual returns between the AI-managed and traditional groups using a Welch’s t-test. I find that the traditional active fund outperformed the AI-managed funds on both an absolute basis (15.54% CAGR vs. 8.42%) and a risk-adjusted basis (Sharpe ratio of 0.61 vs. 0.41), while also charging a lower expense ratio (0.39% vs. 0.75%). Contrary to my initial expectation, the traditional fund also exhibited the highest volatility of the three actively managed funds in my sample. The difference in mean returns between the AI-managed and traditional groups was not statistically significant (t = −0.592, p = 0.574), a result I attribute in part to my small sample of three funds and five years of data. All three actively managed funds underperformed the S&P 500 on a risk-adjusted basis. I conclude that, in this sample, the “AI” label was not associated with either superior returns or lower cost for investors, and I discuss the practical implications of this finding alongside the study’s limitations and directions for future research with a larger sample of AI-managed funds.
Keywords
Active Management, Artificial Intelligence, Exchange-Traded Funds, Fund Management, Risk-Adjusted Performance, Sharpe Ratio