Google search volumes and the financial markets during the COVID-19 outbreak

Costola, Michele; Iacopini, Matteo; Santagiustina, Carlo R. M. A.

doi:10.1016/j.frl.2020.101884

Cited by 61 publications

(52 citation statements)

References 26 publications

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“…As to the COVID-19 disease specifically, results are in line with studies that have demonstrated the correlation between internet user attention (typically Google metrics)as a gauge of panic and fearand either market returns (Costola et al, 2020;Vasileiou, 2020;Iyke and Ho, 2021;Shear et al, 2021;Smales, 2021;Subramaniam and Chakraborty, 2021) or their volatility (Ly ocsa et al, 2020). The present study evaluates the impact of SM user attention on the local Australian financial market, here proxied by the topic-related number of Tweets and similarly to Valle-Cruz et al (2021)their negative content.…”

Section: Resultssupporting

confidence: 86%

“…These studies, none of them dedicated uniquely to the Australian market, typically use Google metrics (GSVs or GTs) with search terms such as "coronavirus" as a proxy of investor attention and fear (Costola et al, 2020;Vasileiou, 2020;Iyke and Ho, 2021;Ly ocsa et al, 2020;Shear et al, 2021;Smales, 2021;Subramaniam and Chakraborty, 2021).…”

Section: Discussionmentioning

confidence: 99%

“…Shear et al (2021) also used abnormal GSV of the word "coronavirus" as a proxy of investors' attention, finding that the negative impact of investors' attention on stock market returns was stronger in countries where investors possessed higher "uncertainty avoidance" cultural values. See also: Smales (2021), Iyke and Ho (2021) and Costola et al (2020), the last study was based on Google Trends.…”

Section: Introductionmentioning

confidence: 99%

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Australian market response to COVID-19 as moderated by social media

Maia

Tommasetti

Macedo

2021

PAR

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Purpose Emerging as a black swan event that stifled the global economy, COVID-19 is the first social media (SM) pandemic. In an unsocial age due to social distancing, SM relevance is intuitively magnified during a pandemic. This study aims to investigate the direct and moderating impact of Twitter on the Australian stock market during the COVID-19 info-pandemic. Design/methodology/approach As a natural experiment, a time-series regression measures the effect of the COVID-19 virus, proxied by the active cases and the marginal impact of SM user attention and sentiment on the Australian stock market. Findings Results show that SM user attention and sentiment to COVID-19 related fear topics are significant in explaining market returns and in predicting their volatility. It demonstrates that SM plays a role between COVID-19 and Australian stock market performance by amplifying the pandemic impact. Originality The study goes beyond a purely empirical investigation of the catalyst (i.e. the pandemic), thus contributing to current theoretical debates on the impact of SM on investor behaviour. Practical implications Policymakers and market participants could benefit from the empirical findings of this research in the case of analogous epidemics.

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Section: Resultssupporting

confidence: 86%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Australian market response to COVID-19 as moderated by social media

Maia

Tommasetti

Macedo

2021

PAR

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“…Zaremba et al [2] showed that, for the countries which have relatively low unemployment rates and plenty of firms with conservative investment policies and low P/E ratio, stock markets are more likely to be immune to the healthcare crisis. Additional works also explore how the government policy and reports about COVID-19 influence the financial market [3][4][5], for example, Pandey and Kumari (2021) [6,7]. Moreover, researchers show that there are different impacts on different specific markets.…”

Section: Introductionmentioning

confidence: 99%

Chinese Stock Market’s Reaction to COVID‐19 in the Short and Long Run

Wang

2022

Complexity

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We study the impact of COVID-19 on Chinese stock market which can be seen as a complex system. We use the event study method to evaluate its performance change in terms of the return rate, turnover rate, etc. We show that the abnormal return of stock market was significantly negative after the outbreak of COVID-19 and did not turn positive until May 2020. Moreover, the five-factor model is used to estimate the ordinary returns of different industries and show that abnormal returns for medical and food industries were significantly positive, while energy and public utility industries had significantly negative abnormal returns which persisted for a long time. COVID-19 had lag effects on clothes industry, finance industry, transportation industry, and IT industry. We also find that energy and finance industries had negative abnormal turnover rates during the sample period, while other industries, such as healthcare and telecommunications service industries, had positive abnormal turnover rates.

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“…For instance, Garcia (2013) studies the effect of the financial news from the New York Times during the twentieth century and shows its predictive ability on daily stock returns, particularly during recession periods. Recently, the availability of social media data from the Google search engine (Costola et al, 2020) and tweets (Iacopini and Santagiustina, 2021) has provided several insights about investor psychology and its impact on financial stock returns. Among others, Caporin and Poli (2017) and Xing et al (2018) provide interesting surveys on firm-specific news and sentiment; Caporin and Poli (2017) show that augmented-data models including news variables provide superior forecasts.…”

Section: Introductionmentioning

confidence: 99%

Impact of Public News Sentiment on Stock Market Index Return and Volatility

Anese¹,

Corazza

Costola

et al. 2021

SSRN Journal

Self Cite

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Recent advances in natural language processing have contributed to the development of market sentiment measures through text content analysis in news providers and social media. The effectiveness of these sentiment variables depends on the implemented techniques and the type of source on which they are based. In this paper, we investigate the impact of the release of public financial news on the S&P 500. Using automatic labeling techniques based on either stock index returns or dictionaries, we apply a classification problem based on long short-term memory neural networks to extract alternative proxies of investor sentiment. Our findings provide evidence that there exists an impact of those sentiments in the market on a 20-minute time frame.We find that dictionary-based sentiment provides meaningful results with respect to those based on stock index returns, which partly fails in the mapping process between news and financial returns.

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Google search volumes and the financial markets during the COVID-19 outbreak

Cited by 61 publications

References 26 publications

Australian market response to COVID-19 as moderated by social media

Australian market response to COVID-19 as moderated by social media

Chinese Stock Market’s Reaction to COVID‐19 in the Short and Long Run

Impact of Public News Sentiment on Stock Market Index Return and Volatility

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