Fear of COVID-19 Effect on Stock Markets: A Proposal for an Algorithmic Trading System Based on Fear

被引：0

作者：

Paule-Vianez, Jessica ^{[1
]}

Orden-Cruz, Carmen ^{[1
]}

Gomez-Martinez, Raul ^{[1
]}

Escamilla-Solano, Sandra ^{[1
]}

机构：

[1] Rey Juan Carlos Univ, Business Econ Dept, Madrid 28933, Spain

来源：

JOURNAL OF THEORETICAL AND APPLIED ELECTRONIC COMMERCE RESEARCH | 2023年 / 18卷 / 02期

关键词：

COVID-19; fear; stock returns; Google Trends; algorithmic trading system; BIG DATA; BEHAVIOR; SEARCHES; MEDIA; RISK; NEWS;

D O I：

10.3390/jtaer18020058

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

This study analyzes the fear of COVID-19 effect on European stock market returns. For this purpose, the search volumes (SV) collected by Google Trends (GT) and Wikipedia were used as proxies of fear of COVID-19. In a sample from 13 European stock markets, fear of COVID-19 was found to be associated with negative European stock returns. Our research employed this observation to propose an algorithmic trading system based on fear of COVID-19. Back-testing results show the possibility of extraordinary returns based on this system. These findings have important implications for political authorities, the mass media, and investors.

引用

页码：1142 / 1156

页数：15

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