Exploring the coherency and predictability between the stocks of artificial intelligence and energy corporations

Christian Urom, Gideon Ndubuisi (), Hela Mzoughi and Khaled Guesmi
Additional contact information
Christian Urom: Paris School of Business
Gideon Ndubuisi: Delft University of Technology (TU Delft)
Hela Mzoughi: Paris School of Business
Khaled Guesmi: Paris School of Business

Financial Innovation, 2024, vol. 10, issue 1, 1-31

Abstract: Abstract This paper employs wavelet coherence, Cross-Quantilogram (CQ), and Time-Varying Parameter Vector-Autoregression (TVP-VAR) estimation strategies to investigate the dependence structure and connectedness between investments in artificial intelligence (AI) and eight different energy-focused sectors. We find significant evidence of dependence and connectedness between the stock returns of AI and those of the energy-focused sectors, especially during intermediate and long-term investment horizons. The relationship has become stronger since the COVID-19 pandemic. More specifically, results from the wavelet coherence approach show a stronger association between the stock returns of energy-focused sectors and AI, while results from the CQ analysis show that directional predictability from AI to energy-focused sectors varies across sectors, investment horizons, and market conditions. TVP-VAR results show that since the COVID-19 outbreak, AI has become more of a net shock receiver from the energy market. Our study offers crucial implications for investors and policymakers.

Keywords: Artificial intelligence; Energy-firms; Quantile-dependence; Spillover (search for similar items in EconPapers)
JEL-codes: G10 G15 O3 Q01 Q02 Q42 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1186/s40854-024-00609-3 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:fininn:v:10:y:2024:i:1:d:10.1186_s40854-024-00609-3

Ordering information: This journal article can be ordered from
http://www.springer. ... nomics/journal/40589

DOI: 10.1186/s40854-024-00609-3

Access Statistics for this article

Financial Innovation is currently edited by J. Leon Zhao and Zongyi

More articles in Financial Innovation from Springer, Southwestern University of Finance and Economics
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().