The Investment Styles and Performance of AI-Related ETFs: Analyzing the Impact of Active Management

Nikoletta Poutachidou and Alexandros Koulis ()
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Nikoletta Poutachidou: Department of Economics, University of Thessaly, 38221 Volos, Greece
Alexandros Koulis: School of Social Sciences, Hellenic Open University, 26331 Patras, Greece

FinTech, 2025, vol. 4, issue 2, 1-17

Abstract: This paper studies the performance of ETFs that invest in companies involved in artificial intelligence (AI) technologies, such as firms focused on AI research, development, and applications. Using daily data from 15 American ETFs focused on AI-related companies over the period from 1 February 2019 to 29 December 2023, this paper investigates their investment style characteristics through a returns-based style analysis (RBSA). This study offers detailed insights into the degree of active versus passive management and highlights strategic patterns that may guide investment decisions in AI-themed financial products. We highlight that asset selection drives fund performances more than active management strategies, offering practical insights for investors and policymakers.

Keywords: style analysis; fund performance; AI; ETFs (search for similar items in EconPapers)
JEL-codes: C6 F3 G O3 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jfinte:v:4:y:2025:i:2:p:20-:d:1667381

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