Which COVID-19 information really impacts stock markets?

Jan Jakub Szczygielski; Ailie Charteris; Princess Rutendo Bwanya; Janusz Brzeszczyński

doi:10.1016/j.intfin.2022.101592

Which COVID-19 information really impacts stock markets?

Jan Jakub Szczygielski, Ailie Charteris, Princess Rutendo Bwanya, Janusz Brzeszczyński^*

^*Corresponding author for this work

Accounting and Financial Management Department

Research output: Contribution to journal › Article › peer-review

28 Citations (Scopus)

24 Downloads (Pure)

Abstract

Information about COVID-19 abounds, but which COVID-19 data actually impacts stock prices? We investigate which measures of COVID-19 matter most by applying elastic net regression for measure selection using a sample of the 35 largest stock markets. Out of 24 measures, COVID-19 related Google search trends, the stringency of government responses and media hype prevail. These measures proxy for COVID-19 related uncertainty, the economic impact of lockdowns and panic-driven media attention respectively, summarizing key aspects of COVID-19 that move stock markets. Moreover, geographical proximity to the virus’s outbreak and a country’s development level also matter in terms of impact.

Original language	English
Article number	101592
Journal	Journal of International Financial Markets, Institutions and Money
Volume	84
Early online date	27 May 2022
DOIs	https://doi.org/10.1016/j.intfin.2022.101592
Publication status	Published - 1 Apr 2023

Keywords

COVID-19
pandemic
returns
global stock markets
elastic net regression
machine learning

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1016/j.intfin.2022.101592

Which COVID information really impacts stock marketsAccepted author manuscript, 1.39 MBLicence: CC BY-NC-ND

Cite this

@article{455966b81b654797988dc45f10d88d5f,

title = "Which COVID-19 information really impacts stock markets?",

abstract = "Information about COVID-19 abounds, but which COVID-19 data actually impacts stock prices? We investigate which measures of COVID-19 matter most by applying elastic net regression for measure selection using a sample of the 35 largest stock markets. Out of 24 measures, COVID-19 related Google search trends, the stringency of government responses and media hype prevail. These measures proxy for COVID-19 related uncertainty, the economic impact of lockdowns and panic-driven media attention respectively, summarizing key aspects of COVID-19 that move stock markets. Moreover, geographical proximity to the virus{\textquoteright}s outbreak and a country{\textquoteright}s development level also matter in terms of impact.",

keywords = "COVID-19, pandemic, returns, global stock markets, elastic net regression, machine learning",

author = "{Jakub Szczygielski}, Jan and Ailie Charteris and {Rutendo Bwanya}, Princess and Janusz Brzeszczy{\'n}ski",

year = "2023",

month = apr,

day = "1",

doi = "10.1016/j.intfin.2022.101592",

language = "English",

volume = "84",

journal = "Journal of International Financial Markets, Institutions and Money",