Persistence in Stock Returns : Robotics and AI ETFs Versus Other Assets

This paper examines the long-memory properties of the returns of exchange-traded funds (ETFs) that provide exposure to companies operating in the fields of artificial intelligence (AI) and robotics listed on the US market, along with other assets such as the WTI crude oil price (West Texas Intermedi...

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Detalles Bibliográficos
Autores: Belhouichet, Fekria, Caporale, Guglielmo Maria, Gil-Alana, Luis Alberiko
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universidad Francisco de Vitoria
Repositorio:DDFV. Repositorio Institucional de la Universidad Francisco de Vitoria
Idioma:inglés
OAI Identifier:oai:ddfv.ufv.es:10641/7110
Acceso en línea:https://hdl.handle.net/10641/7110
Access Level:acceso abierto
Palabra clave:AI ETFs
fractional integration
long memory
persistence
robotics ETFs
trends
Accounting
Business, Management and Accounting (miscellaneous)
Finance
Economics and Econometrics
Yes
yes
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oai_identifier_str oai:ddfv.ufv.es:10641/7110
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repository_id_str
spelling Persistence in Stock Returns : Robotics and AI ETFs Versus Other AssetsBelhouichet, FekriaCaporale, Guglielmo MariaGil-Alana, Luis AlberikoAI ETFsfractional integrationlong memorypersistencerobotics ETFstrendsAccountingBusiness, Management and Accounting (miscellaneous)FinanceEconomics and EconometricsYesyesThis paper examines the long-memory properties of the returns of exchange-traded funds (ETFs) that provide exposure to companies operating in the fields of artificial intelligence (AI) and robotics listed on the US market, along with other assets such as the WTI crude oil price (West Texas Intermediate), Bitcoin, the S&P 500 index, 10-year US Treasury bonds, and the VIX volatility index. The data frequency is daily and covers the period from 1 January 2023 to 23 June 2025. The adopted fractional integration framework is more general and flexible than those previously used in related studies and allows for a detailed assessment of the degree of persistence in returns. The results indicate that all return series exhibit a high degree of persistence, regardless of the error structure assumed, and that, in general, a linear model adequately captures their dynamics over time. These findings suggest that newly developed AI- and robotics-themed ETFs do not provide investors with additional hedging or diversification benefits compared to more traditional assets, nor do they create new challenges for policymakers concerned with financial stability.Facultad de Derecho, Empresa y Gobierno20252025-11-0120252025-11-01journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10641/7110reponame:DDFV. Repositorio Institucional de la Universidad Francisco de Vitoriainstname:Universidad Francisco de VitoriaInglésengopen accesshttp://purl.org/coar/access_right/c_abf2http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:ddfv.ufv.es:10641/71102026-06-11T12:44:57Z
dc.title.none.fl_str_mv Persistence in Stock Returns : Robotics and AI ETFs Versus Other Assets
title Persistence in Stock Returns : Robotics and AI ETFs Versus Other Assets
spellingShingle Persistence in Stock Returns : Robotics and AI ETFs Versus Other Assets
Belhouichet, Fekria
AI ETFs
fractional integration
long memory
persistence
robotics ETFs
trends
Accounting
Business, Management and Accounting (miscellaneous)
Finance
Economics and Econometrics
Yes
yes
title_short Persistence in Stock Returns : Robotics and AI ETFs Versus Other Assets
title_full Persistence in Stock Returns : Robotics and AI ETFs Versus Other Assets
title_fullStr Persistence in Stock Returns : Robotics and AI ETFs Versus Other Assets
title_full_unstemmed Persistence in Stock Returns : Robotics and AI ETFs Versus Other Assets
title_sort Persistence in Stock Returns : Robotics and AI ETFs Versus Other Assets
dc.creator.none.fl_str_mv Belhouichet, Fekria
Caporale, Guglielmo Maria
Gil-Alana, Luis Alberiko
author Belhouichet, Fekria
author_facet Belhouichet, Fekria
Caporale, Guglielmo Maria
Gil-Alana, Luis Alberiko
author_role author
author2 Caporale, Guglielmo Maria
Gil-Alana, Luis Alberiko
author2_role author
author
dc.contributor.none.fl_str_mv Facultad de Derecho, Empresa y Gobierno
dc.subject.none.fl_str_mv AI ETFs
fractional integration
long memory
persistence
robotics ETFs
trends
Accounting
Business, Management and Accounting (miscellaneous)
Finance
Economics and Econometrics
Yes
yes
topic AI ETFs
fractional integration
long memory
persistence
robotics ETFs
trends
Accounting
Business, Management and Accounting (miscellaneous)
Finance
Economics and Econometrics
Yes
yes
description This paper examines the long-memory properties of the returns of exchange-traded funds (ETFs) that provide exposure to companies operating in the fields of artificial intelligence (AI) and robotics listed on the US market, along with other assets such as the WTI crude oil price (West Texas Intermediate), Bitcoin, the S&P 500 index, 10-year US Treasury bonds, and the VIX volatility index. The data frequency is daily and covers the period from 1 January 2023 to 23 June 2025. The adopted fractional integration framework is more general and flexible than those previously used in related studies and allows for a detailed assessment of the degree of persistence in returns. The results indicate that all return series exhibit a high degree of persistence, regardless of the error structure assumed, and that, in general, a linear model adequately captures their dynamics over time. These findings suggest that newly developed AI- and robotics-themed ETFs do not provide investors with additional hedging or diversification benefits compared to more traditional assets, nor do they create new challenges for policymakers concerned with financial stability.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-11-01
2025
2025-11-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10641/7110
url https://hdl.handle.net/10641/7110
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2

http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2

http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:DDFV. Repositorio Institucional de la Universidad Francisco de Vitoria
instname:Universidad Francisco de Vitoria
instname_str Universidad Francisco de Vitoria
reponame_str DDFV. Repositorio Institucional de la Universidad Francisco de Vitoria
collection DDFV. Repositorio Institucional de la Universidad Francisco de Vitoria
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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