Consequences of COVID-19 on Banking Sector Index: Artificial Neural Network Model

Hamzeh F. Assous and Dania Al-Najjar
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Hamzeh F. Assous: Department of Finance, School of Business, King Faisal University, Alahsa 31982, Saudi Arabia
Dania Al-Najjar: Department of Finance, School of Business, King Faisal University, Alahsa 31982, Saudi Arabia

IJFS, 2021, vol. 9, issue 4, 1-16

Abstract: The World Health Organization officially declared COVID-19 a global pandemic on 11 March 2020. In this study, we examine the effect of COVID-19 indicators and policy response on the Saudi banking index. COVID-19 variables that were applied are: new confirmed and fatal COVID-19 cases in Saudi Arabia; lockdowns; first and second decreases in interest rates; regulations, and oil prices. We implemented the analysis by running a stepwise regression analysis then building an artificial neural network (ANN) model. According to regression findings, oil prices and new confirmed cases have had a significant positive effect on the Saudi banking index. Nevertheless, the lockdown announcements in Saudi Arabia and the first decrease in interest rates had a significant negative effect on the Saudi banking index. To enhance the performance of the linear regression model, the ANN model was built. Findings showed that the ranking of the variables in terms of their importance is: oil price, number of confirmed cases, lockdown announcements, decrease in interest rates, and lastly, regulations.

Keywords: Saudi banking sector; COVID-19; ANN; decrease in interest rates; lockdown; regulations; confirmed cases; death cases (search for similar items in EconPapers)
JEL-codes: F2 F3 F41 F42 G1 G2 G3 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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