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Impact of disease information (Ebola and COVID-19) on the pharmaceutical sector in Russia and USA

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Objective: identification of the relationship between the news coverage of global diseases and the dynamics of the return on shares of the pharmaceutical sector for Russia and the United States.

Material and methods. The empirical base of the study includes more than 700 thousand tweets on Ebola and COVID-19 in Russian and English, news of the RBC news agency. The sentiment of the text was assessed on the basis of five English and four Russian-language dictionaries, the influence of fundamental and textual variables on the profitability of pharmaceutical companies' shares was carried out using the ARMAX-GARCH econometric model.

Results. It has been proven that the dynamics of the stock index of pharmaceutical companies is explained by fundamental (economic) and sentimental factors. News of any epidemics negatively affects the pharmaceutical sector in the US and Russia, that is, there are no industries that benefit from this situation. Pandemic news affects US pharmaceutical companies more than Russian companies. The effect of news influence depends on the level of spread of the disease. News influences not only at the moment of their publication, but also after: there is a "delayed effect". Ebola news affects the American pharmaceutical market for 2 weeks, and the dynamics of the increase in influence can be traced. News on the COVID pandemic amplifies its impact during 1 week for the Russian pharmaceutical market and for 2 weeks for the US pharmaceutical companies. As for news sources, the elastic network has identified more significant variables based on publications from RBC; therefore, Internet publications generate more publicity, shaping a more significant overall sentiment in the markets.

Conclusion. The models developed in the framework of the study and the economic conclusions obtained have not only theoretical, but also practical significance, and can also be used for further research in this area. It is possible to give recommendations on the practical use of dictionaries to assess the sentiment of the text. In our study, the elastic network method chose the Loughran–McDonald dictionary for evaluating economic texts in English and the EcSentiThemeLex dictionary (designed in R and Python programming environments). Avenues for further investigation may include analysis of other sources of information about the pandemic.

About the Authors

E. A. Fedorova
Financial University under the Government of the Russian Federation
Russian Federation

Elena A. Fedorova – Dr. Econ. Sc., Professor, Department of the Corporate Governance and Finance. Scopus Author ID: 56585981200

49 Leningradskiy Prospect, Moscow 125993, Russia

D. O. Afanasyev
JSC Greenatom
Russian Federation

Dmitry O. Afanasyev – Information Systems Architect

10 bld. 1 Pervyy Nagatinskiy proezd, Moscow 115533, Russia

A. V. Sokolov
National Research University “Higher School of Economics”
Russian Federation

Alexander V. Sokolov – Student

20 Myasnitskaya Str., Moscow 101000, Russia

M. P. Lazarev
Financial University under the Government of the Russian Federation
Russian Federation

Mikhail P. Lazarev – PhD (Phys. Math.), Assistant Professor, Department of Financial and Investment Management

49 Leningradskiy Prospect, Moscow 125993, Russia


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For citations:

Fedorova E.A., Afanasyev D.O., Sokolov A.V., Lazarev M.P. Impact of disease information (Ebola and COVID-19) on the pharmaceutical sector in Russia and USA. FARMAKOEKONOMIKA. Modern Pharmacoeconomics and Pharmacoepidemiology. 2021;14(2):213–224. (In Russ.)

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ISSN 2070-4909 (Print)
ISSN 2070-4933 (Online)