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- Title
Exploring the coherency and predictability between the stocks of artificial intelligence and energy corporations.
- Authors
Urom, Christian; Ndubuisi, Gideon; Mzoughi, Hela; Guesmi, Khaled
- 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.
- Subjects
RATE of return on stocks; ARTIFICIAL intelligence; COVID-19 pandemic; INVESTORS; ENERGY industries
- Publication
Financial Innovation, 2024, Vol 10, Issue 1, p1
- ISSN
2199-4730
- Publication type
Article
- DOI
10.1186/s40854-024-00609-3