Stock markets performance during a pandemic: How contagious is COVID-19?

Background and Motivation: The coronavirus (“COVID-19”) pandemic, the subsequent policies and lockdowns have unarguably led to an unprecedented fluid circumstance worldwide. The panic and fluctuations in the stock markets were unparalleled. It is inarguable that real-time availability of news and social media platforms like Twitter played a vital role in driving the investors’ sentiment during such global shock. Purpose:The purpose of this thesis is to study how the investor sentiment in relation to COVID-19 pandemic influenced stock markets globally and how stock markets globally are integrated and contagious. We analyze COVID-19 sentiment through the Twitter posts and investigate its effect on financial securities movements. Methodology: In order to determine investor sentiment, we used text mining and Natural Language Processing (NLP) to conduct sentiment analysis on COVID-19 related tweets during the year of 2020 and got the daily polarity of those tweets. We employed a GARCH (1,1) model to study the impact of the investor sentiment, assessed by the COVID-19 related tweets, on the stock markets movements globally, in the conditional heteroscedasticity equation. The thesis uses six global stock market indices from developed markets. Duration of the study: 4th of January 2020 - 21st of December 2020 Conclusion: Our results from the GARCH (1,1) models suggest that the investors’ sentiment based on the COVID-19 tweets shows significant impact on the conditional heteroscedasticity of the developed markets indices, indicating an impact on volatility and trading volumes of the six developed market indices