A Study on the Performance of Actively and Passively Managed Artificial Intelligence Exchange Traded Funds

This study employs a sample of 25 active and 22 passive AI ETFs to examine several issues surrounding their performance, risk, pricing efficiency, and persistence in pricing discrepancies and their impact on ETFs’ performance combined with the respective impact of intraday volatility. The relationship between AI ETFs’ performance and market factors concerning size, value, profitability, investment and momentum is evaluated too. The results indicate that the passive AI ETFs have outperformed active ones over their entire trade history, without, however, shouldering their investors with materially higher volatility. Moreover, both AI ETF groups trade at a persistent premium to their NAV. The concurrent premium positively affects return, while the one-period lagged premium is negatively related to return. In addition, a negative relationship between return and concurrent intraday volatility and a positive (but less strong) relationship between return and one-period lagged intraday volatility are found. Moreover, the majority of AI ETFs do not generate significant alphas. Finally, market factors effectively explain the performance of AI ETFs.

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Publication Details

Journal
Journal of risk and financial management
Published
2026-04-07
DOI
https://doi.org/10.3390/jrfm19040267
Primary Topic
Financial Markets and Investment Strategies
Type
article
Field-Weighted Citation Impact
0.00
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article

A Study on the Performance of Actively and Passively Managed Artificial Intelligence Exchange Traded Funds

Gerasimos G. Rompotis
Journal of risk and financial management
Financial Markets and Investment Strategies
article

A Study on the Performance of Actively and Passively Managed Artificial Intelligence Exchange Traded Funds

Gerasimos G. Rompotis
article en

Abstract

This study employs a sample of 25 active and 22 passive AI ETFs to examine several issues surrounding their performance, risk, pricing efficiency, and persistence in pricing discrepancies and their impact on ETFs’ performance combined with the respective impact of intraday volatility. The relationship between AI ETFs’ performance and market factors concerning size, value, profitability, investment and momentum is evaluated too. The results indicate that the passive AI ETFs have outperformed active ones over their entire trade history, without, however, shouldering their investors with materially higher volatility. Moreover, both AI ETF groups trade at a persistent premium to their NAV. The concurrent premium positively affects return, while the one-period lagged premium is negatively related to return. In addition, a negative relationship between return and concurrent intraday volatility and a positive (but less strong) relationship between return and one-period lagged intraday volatility are found. Moreover, the majority of AI ETFs do not generate significant alphas. Finally, market factors effectively explain the performance of AI ETFs.

Journal of risk and financial managementVol. 19(4)
National and Kapodistrian University of Athens (GR)
Openalex Percentile: Top 17%
Financial Markets and Investment Strategies
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