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15-09-2021

Using Social Media to Predict the Stock Market Crash and Rebound amid the Pandemic: The Digital ‘Haves’ and ‘Have-mores’

Authors: Chong Guan, Wenting Liu, Jack Yu-Chao Cheng

Published in: Annals of Data Science | Issue 1/2022

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Abstract

Since the 2019 novel Coronavirus disease (COVID-19) spread across the globe, risks brought by the pandemic set in and stock markets tumbled worldwide. Amidst the bleak economic outlook, investors’ concerns over the pandemic spread rapidly through social media but wore out shortly. Similarly, the crash only caused a relatively short-lived bear market, which bottomed out and recovered quickly. Meanwhile, technology stocks have grabbed the spotlight as the digitally advanced sectors seemed to show resilience in this Coronavirus-plagued market. This paper aims to examine market sentiments using social media to predict the stock market performance before, during and after the March 2020 stock market crash. In addition, using the Organisation for Economic Co-operation and Development Taxonomy of Sectoral Digital-intensity Framework, we identified market sectors that have outperformed others as the market sentiment was impacted by the unfolding of the pandemic. The daily stock performance of a usable sample of 1619 firms from 34 sectors was first examined via a combination of hierarchical clustering and shape-based distance measure. This was then tested against a time series of daily price changes through augmented vector auto-regression. Results show that market sentiments towards the pandemic have significantly impacted the price differences. More interestingly, the stock performance across sectors is characterized by the level of digital intensity, with the most digitally advanced sectors demonstrating resilience against negative market sentiments on the pandemic. This research is among the first to demonstrate how digital intensity mitigates the negative effect of a crisis on stock market performance.

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Title: Using Social Media to Predict the Stock Market Crash and Rebound amid the Pandemic: The Digital ‘Haves’ and ‘Have-mores’
Authors: Chong Guan
Wenting Liu
Jack Yu-Chao Cheng
Publication date: 15-09-2021
Publisher: Springer Berlin Heidelberg
Published in: Annals of Data Science / Issue 1/2022
Print ISSN: 2198-5804
Electronic ISSN: 2198-5812
DOI: https://doi.org/10.1007/s40745-021-00353-w

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