Can internet concern about COVID-19 help predict stock markets: new evidence from high-concern and low-concern periods

Jiqin Ren, Yuanxuan Guo, Jingjing Li and Jingjing Li

Applied Economics, 2024, vol. 56, issue 35, 4155-4176

Abstract: The unprecedented outbreak of Corona Virus Disease 2019 (COVID-19) has resulted in extreme volatility in stock markets. This study mainly examines the predictive ability of the Internet concern about COVID-19 on stock index returns, based on the framework of GARCH type models. Instead of using the whole sample period, we divide the Internet concern about COVID-19 into high-concern and low-concern periods by breakpoint test method and then examine its predictive ability for stock returns in different periods, respectively. Using stock indexes of 10 countries and abnormal Google search volume of ‘coronavirus’ as study samples, the results reveal that (1) the Internet concern about COVID-19 has a negative impact on the stock index returns in the whole and high-concern periods, while its influence in the low-concern period is mixed; (2) the Internet concern about COVID-19 improves the prediction accuracy of stock index returns in the high-concern period, while seems to lose its powerful predictive ability in the whole and low-concern periods.

Date: 2024
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DOI: 10.1080/00036846.2023.2210820

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