Investigating Investment Style and Performance of ETFs funds investing in Artificial Intelligence (AI) sector
| Nikoletta Poutachidou
Department of Economics University of Thessaly Volos, Greece
nipoutachidou@uth.gr |
Alexandros Koulis[1]
Hellenic Open University, Patras. Greece
akoulis@eap.gr
|
Stephanos Papadamou
Department of Economics University of Thessaly Volos, Greece
Hellenic Open University, Patras. Greece stpapada@uth.gr
|
[1] Corresponding author.
Abstract
This study investigates the investment styles and performance of AI-driven Exchange-Traded Funds (ETFs) using daily data from 15 American AI ETFs over the period from February 1, 2019, to December 29, 2023. The analysis utilizes a multifaceted approach to style analysis, revealing that AI ETFs exhibit remarkably similar investment styles despite their varied market offerings. Performance metrics indicate that while AI ETFs provide substantial exposure to the AI industry, their returns are significantly influenced by both their investment styles and the strategic selection of assets within their portfolios. The consistency in investment styles across AI ETFs suggests a homogeneous approach to AI-driven investment strategies. However, the impact of active management on returns appears modest and statistically insignificant, highlighting the critical role of asset selection. The standard deviations of fund returns are consistently higher than those of their style benchmarks, reflecting greater return volatility. These findings underscore the importance of strategic asset selection in AI ETF investments, suggesting that investors should focus on the specific components of AI ETFs to ensure alignment with their investment goals.
Keywords: Style Analysis, Fund Performance, AI, ETFs
JEL Codes: G20, G23