Open Access

ARTICLE

COVID-19 Pandemic Data Predict the Stock Market

by Abdulaziz Almehmadi

Faculty of Computing and Information Technology, University of Tabuk, Tabuk, Saudi Arabia

* Corresponding Author: Abdulaziz Almehmadi. Email:

Computer Systems Science and Engineering 2021, 36(3), 451-460. https://doi.org/10.32604/csse.2021.015309

Received 14 November 2020; Accepted 24 December 2020; Issue published 18 January 2021

Abstract

Unlike the 2007–2008 market crash, which was caused by a banking failure and led to an economic recession, the 1918 influenza pandemic triggered a worldwide financial depression. Pandemics usually affect the global economy, and the COVID-19 pandemic is no exception. Many stock markets have fallen over 40%, and companies are shutting down, ending contracts, and issuing voluntary and involuntary leaves for thousands of employees. These economic effects have led to an increase in unemployment rates, crime, and instability. Studying pandemics’ economic effects, especially on the stock market, has not been urgent or feasible until recently. However, with advances in artificial intelligence (AI) and the inter-connectivity that social media provides, such research has become possible. In this paper, we propose a COVID-19-based stock market prediction system (C19-SM2) that utilizes social media. Our AI system enables economists to study how COVID-19 pandemic data influence social media and, hence, the stock market. C19-SM2 gathers COVID-19 infection and death cases reported by the authorities and social media data from a geographic area and extracts the sentiments and events that occur in that area. The information is then fed to the support vector machine (SVM) and random forest and random tree classifiers along with current stock market values. Then, the system produces a projection of the stock market’s movement during the next day. We tested the system with the Dow Jones Industrial Average (DJI) and the Tadawul All Share Index (TASI). Our system achieved a stock market prediction accuracy of 99.71%, substantially higher than the 89.93% accuracy reported in the related literature; the inclusion of COVID-19 data improved accuracy by 9.78%.

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