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The Impact of COVID-19 on the Relationship between Non-Renewable Energy and Saudi Stock Market Sectors Using Wavelet Coherence Approach and Neural Networks

Author

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  • Ahmed A. Elamer

    (Brunel Business School, Brunel University London, Kingston Lane, Uxbridge, London UB8 3PH, UK
    Department of Accounting, Faculty of Commerce, Mansoura University, Mansoura 35516, Egypt)

  • Bassam A. Elbialy

    (Financial Management Department, College of Applied Studies and Community Service, Imam Abdulrahman Bin Faisal University, Dammam 34212, Saudi Arabia)

  • Kholoud A. Alsaab

    (Financial Management Department, College of Applied Studies and Community Service, Imam Abdulrahman Bin Faisal University, Dammam 34212, Saudi Arabia)

  • Mohamed A. Khashan

    (Business Administration Department, Mansoura University, Mansoura 35516, Egypt
    Department of Business Administration, College of Economics and Administrative Sciences, Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi Arabia)

Abstract

In this study, we examine the impact of COVID-19 on the relationship between non-renewable energy and Saudi stock market sectors for the period 11 January 2017–22 January 2022. We apply wavelet coherence and Radial Basis Function Neural Network (RBFNN) models. Our results provide evidence that COVID-19 led to an increase in the strength of the relationship between oil as a main non-renewable energy source and Saudi stock market sectors and affected the nature and direction of this relationship. The relationships between oil and commercial and professional services, materials, banks, energy, and transportation sectors are the most affected. Our results will help hedge funds, mutual funds, and individual investors, forecast the direction of Saudi stock market sectors and the use of oil for hedging or diversification during periods of uncertainty and crisis. It will also help decision and policymakers in Saudi Arabia to make the necessary decisions and actions to maintain the stability of the stock market sectors during these periods.

Suggested Citation

  • Ahmed A. Elamer & Bassam A. Elbialy & Kholoud A. Alsaab & Mohamed A. Khashan, 2022. "The Impact of COVID-19 on the Relationship between Non-Renewable Energy and Saudi Stock Market Sectors Using Wavelet Coherence Approach and Neural Networks," Sustainability, MDPI, vol. 14(21), pages 1-24, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:14496-:d:963428
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    References listed on IDEAS

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    2. Abdou, Hussein A. & Elamer, Ahmed A. & Abedin, Mohammad Zoynul & Ibrahim, Bassam A., 2024. "The impact of oil and global markets on Saudi stock market predictability: A machine learning approach," Energy Economics, Elsevier, vol. 132(C).

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