The effect of COVID-19 on the Egyptian exchange using principal component analysis

Heba M Ezzat

doi:10.1108/jhass-08-2021-0135

Abstract

PurposeSince the beginning of 2020, economies faced many changes as a result of coronavirus disease 2019 (COVID-19) pandemic. The effect of COVID-19 on the Egyptian Exchange (EGX) is investigated in this research.Design/methodology/approachTo explore the impact of COVID-19, three periods were considered: (1) 17 months before the spread of COVID-19 and the start of the lockdown, (2) 17 months after the spread of COVID-19 and the during the lockdown and (3) 34 months comprehending the whole period (before and during COVID-19). Due to the large number of variables that could be considered, dimensionality reduction method, such as the principal component analysis (PCA) is followed. This method helps in determining the most individual stocks contributing to the main EGX index (EGX 30). The PCA, also, addresses the multicollinearity between the variables under investigation. Additionally, a principal component regression (PCR) model is developed to predict the future behavior of the EGX 30.FindingsThe results demonstrate that the first three principal components (PCs) could be considered to explain 89%, 85%, and 88% of data variability at (1) before COVID-19, (2) during COVID-19 and (3) the whole period, respectively. Furthermore, sectors of food and beverage, basic resources and real estate have not been affected by the COVID-19. The resulted Principal Component Regression (PCR) model performs very well. This could be concluded by comparing the observed values of EGX 30 with the predicted ones (R-squared estimated as 0.99).Originality/valueTo the best of our knowledge, no research has been conducted to investigate the effect of the COVID-19 on the EGX following an unsupervised machine learning method.

Highlights

Since the end of Year 2019, economies faced many challenges due to the imposed lockdown and public fear
This is achieved by applying an unsupervised machine learning method, which is the principal component analysis (PCA)
PCA is a dimensionality reduction technique, which aims to find a linear combination of the original variables that explains most of the data variability

Summary

Introduction

Since the end of Year 2019, economies faced many challenges due to the imposed lockdown and public fear. Financial markets play significant role in countries’ economies (Mishkin, 2010). Financial markets are very fragile to sudden changes (Cont, 2001). Inspecting the effect of coronavirus diseases 2019 (COVID-19) on financial markets is of significant importance. Many studies were run to investigate the effect of the pandemic on the stock market of different countries [c.f. Awad (2020) and Elayed and Abdelrhim (2020) investigated the impact of number of COVID-19 cases on the EGX. Authors above studied the contribution of individual stocks to EGX 30 (which includes the major 30 individual stocks in terms of activity and liquidity traded in the EGX) to identify stable sectors in the EGX

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