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Eben, Jesslyn (2020) Does Coronavirus Pandemic Sentiment Drive FTSE 100 Index Return? Capturing and Measuring Coronavirus Sentiment Through the Utilisation of Google Trends. [Dissertation (University of Nottingham only)]

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Abstract

COVID-19 or the coronavirus infectious disease has affected our lives greatly during this 2020. Even though it concerns medical attention, the impact of the mentioned pandemic draws concerns that affecting other sectors as well, especially on the economy. Quantifying the impact of the pandemic on the financial market has become great concerns for investors and policy makers, especially during its spreading period. In March 2020, the world experienced the most dramatic stock market plunge and even crashed for several days in response to the declaration of the disease as a pandemic. However, the dive was not instigated by poor corporate government, nor asset price bubble burst, and neither do the companies listed on the market has grown weaker. Looking into the phenomenon, it raises an empirical question whether it is possible that the dramatic fluctuation was due to investor sentiment.

This paper investigates the drive behind the market crash in form of quantifying sentiment. We find that the quickest way to obtain new information is through search engine which based the sentiment that drives investment decisions. We also discover that the sentiment on the coronavirus pandemic can be captured and quantified through the queries of the event using search engine. Building the sentiment index constrained by related field keywords, in this case coronavirus and medical-based keywords, is proven to be effective and it also provide evidence on significant negative relationship with the market returns from FTSE 100 index. Moreover, the model constructed by using the coronavirus FEARS index is proven to have prediction power of the FTSE 100 index return on the same day, showing that it could be used to lower the unprecedented risk and create opportunity in finding arbitrage during a global hysteria. The analysis of our empirical test not only revealed that our model is robust, but also show evidence of the magnitude quantity of uninformed noise traders in financial markets.

Item Type:	Dissertation (University of Nottingham only)
Depositing User:	Eben, Jesslyn
Date Deposited:	21 Dec 2022 15:18
Last Modified:	21 Dec 2022 15:18
URI:	https://eprints.nottingham.ac.uk/id/eprint/62052

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