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

Author

Listed:
  • Christian Urom

    (Paris School of Business)

  • Gideon Ndubuisi

    (Delft University of Technology (TU Delft))

  • Hela Mzoughi

    (Paris School of Business
    University of Tunis El Manar)

  • Khaled Guesmi

    (Paris School of Business)

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.

Suggested Citation

  • Christian Urom & Gideon Ndubuisi & Hela Mzoughi & Khaled Guesmi, 2024. "Exploring the coherency and predictability between the stocks of artificial intelligence and energy corporations," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-31, December.
    • Handle: RePEc:spr:fininn:v:10:y:2024:i:1:d:10.1186_s40854-024-00609-3
    DOI: 10.1186/s40854-024-00609-3
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    More about this item

    Keywords

    Artificial intelligence; Energy-firms; Quantile-dependence; Spillover;
    All these keywords.

    JEL classification:

    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • Q01 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Sustainable Development
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources

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