• ISSN: 2010-023X
    • Frequency: Bimonthly
    • DOI: 10.18178/IJTEF
    • Editor-in-Chief: Prof.Tung-Zong (Donald) Chang
    • Executive Editor: Ms. Cherry L. Chen
    • Abstracting/ Indexing: Engineering & Technology Digital Library, ProQuest, Crossref, Electronic Journals Library, DOAJ , EBSCO, and Ulrich's Periodicals Directory
    • E-mail: ijtef@ejournal.net
IJTEF 2016 Vol.7(4): 93-96 ISSN: 2010-023X
DOI: 10.18178/ijtef.2016.7.4.505

Measuring Contagion between Oil Prices and Stock Markets in the GCC Countries Using a Hybrid Artificial Neural Network Model

M. El Shazly and A. Lou
Abstract—By combining artificial intelligence and genetic training algorithms this paper constructs a hybrid model that measures the degree of contagion between oil prices and stock markets for the Gulf Cooperation Council countries. The model’s architecture captures the strength of the pulse that is being transmitted between the oil market and the six markets of the United Arab Emirates, Bahrain, Kuwait, Oman, Qatar and Saudi Arabia over the period 2008-2015. Sensitivity reports suggest that the degree of spillover between oil and global equity markets varies by country and over time. This research seeks to provide insights related to the strength of transmissions and to answer questions that deal with symmetry and diversification. By improving the measurements of the connection strengths that link markets together, more prudent management may be adopted that would enhance the effectiveness of policy implementation.

Index Terms—Artificial intelligence, contagion between oil and equity markets, GCC countries.

Mona El Shazly is with the Columbia College, USA (e-mail: melshazly@columbiasc.edu).

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Cite: M. El Shazly and A. Lou, "Measuring Contagion between Oil Prices and Stock Markets in the GCC Countries Using a Hybrid Artificial Neural Network Model," International Journal of Trade, Economics and Finance vol.7, no.4, pp. 93-96, 2016.

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