Informational Role of Social Media: Evidence from Twitter Sentiment

Gu, Chen; Kurov, Alexander

doi:10.2139/ssrn.3206093

Cited by 14 publications

(16 citation statements)

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“…Preis et al [ 21 ] analyze changes in finance-related Google Trend query volumes and find evidence that these changes might be able to anticipate future trend patterns. Gu [ 22 ] find that Twitter sentiment predicts stock returns without subsequent reversals and argue that this finding provides evidence consistent with the view that Twitter messages contain information not reflected in stock prices.…”

Section: Literature Reviewmentioning

confidence: 58%

Social sentiment segregation: Evidence from Twitter and Google Trends in Chile during the COVID-19 dynamic quarantine strategy

Díaz

Henríquez

2021

PLoS ONE

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The Chilean health authorities have implemented a sanitary strategy known as dynamic quarantine or strategic quarantine to cope with the COVID-19 pandemic. Under this system, lockdowns were established, lifted, or prolonged according to the weekly health authorities’ assessment of municipalities’ epidemiological situation. The public announcements about the confinement situation of municipalities country-wide are made typically on Tuesdays or Wednesdays before noon, have received extensive media coverage, and generated sharp stock market fluctuations. Municipalities are the smallest administrative division in Chile, with each city broken down typically into several municipalities. We analyze social media behavior in response to the confinement situation of the population at the municipal level. The dynamic quarantine scheme offers a unique opportunity for our analysis, given that municipalities display a high degree of heterogeneity, both in size and in the socioeconomic status of their population. We exploit the variability over time in municipalities’ confinement situations, resulting from the dynamic quarantine strategy, and the cross-sectional variability in their socioeconomic characteristics to evaluate the impact of these characteristics on social sentiment. Using event study and panel data methods, we find that proxies for social sentiment based on Twitter queries are negatively related (more pessimistic) to increases in the number of confined people, but with a statistically significant effect concentrated on people from the wealthiest cohorts of the population. For indicators of social sentiment based on Google Trends, we found that search intensity during the periods surrounding government announcements is positively related to increases in the total number of confined people. Still, this effect does not seem to be dependent on the segments of the population affected by the quarantine. Furthermore, we show that the observed heterogeneity in sentiment mirrors heterogeneity in stock market reactions to government announcements. We provide evidence that the observed stock market behavior around quarantine announcements can be explained by the number of people from the wealthiest segments of the population entering or exiting lockdown.

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Section: Literature Reviewmentioning

confidence: 58%

Social sentiment segregation: Evidence from Twitter and Google Trends in Chile during the COVID-19 dynamic quarantine strategy

Díaz

Henríquez

2021

PLoS ONE

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“…Further, negative comments tended to have relatively more impact than positive comments (Xun and Guo, 2017). Yet, social media sentiment studies have often focused on one tool, such as TW (Duz Tan and Tas, 2021;Gu and Kurov, 2020). Thus, the sentiment (positive/neutral/negative) in the general social media discussions (all tools/media combined) related to the company deserves analysis.…”

Section: Value-relevance and Social Mediamentioning

confidence: 99%

“…Web blogs) as an indicator of firm equity value. TW sentiment has been suggested as a value-relevant indicator for stock performance with a positive link between the two (Duz Tan and Tas, 2021; Gu and Kurov, 2020). This finding may be related to the notion that TW reaches a mass audience (Gu and Kurov, 2020) and reduces information asymmetry (Blankespoor et al , 2014).…”

Section: Value-relevance and Social Mediamentioning

confidence: 99%

The value-relevance of social media activity of Finnish listed companies

Rautiainen

Jokinen²

2022

IJAIM

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Purpose The use of social media tools by companies is common, but the links between the use of multiple social media tools by companies and stock price changes are largely unknown. Therefore, this study aims to analyze the value-relevance of social media activities on Facebook (FB), Instagram (IG), LinkedIn (LI), Twitter (TW) and YouTube (YT). Design/methodology/approach Stock market data and hand-picked social media data in this study were collected from Finland, a small language area with consistent International Financial Reporting Standards (IFRS) reporting practices, in the expectation of better comparability and lower noise in the data.This study uses correlation, regression and factor analyses for a sample of 105 Finnish public limited companies listed on the Nasdaq Helsinki stock exchange. Findings This paper finds evidence that social media activity is an important area of analysis and that the activity and popularity of a company in social media are value-relevant variables in forecasting stock prices. Practical implications Not all social media activities are necessarily equally important for managers and investors. Focus on visual messages in social media is recommended. Originality/value The findings of this study highlight the value-relevance of using multiple visual social media channels, particularly IG and YT. This paper suggests avenues for future research and for analyzing social media information.

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“…A growing body of literature examines the effect of investor sentiment on stock returns [37][38][39][40][41][42]. In the last decade, with the development of social media and thanks to the advances in NLP (natural language processing) techniques and machine learning, online sentiments of retail investors have drawn more and more attention of researchers and practitioners [43][44][45][46]. Bollen et al [47] derived moods of individual investors from Tweets and found that some mood dimensions have prediction powers while others do not.…”

Section: E Role Of Investor Sentimentmentioning

confidence: 99%

The Influence of Research Reports on Stock Returns: The Mediating Effect of Machine-Learning-Based Investor Sentiment

Wang

2021

Discrete Dynamics in Nature and Society

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This paper investigates whether and how the research reports issued by securities companies affect stock returns from the perspective of investor sentiment in China. By collecting research reports and investor comments from a popular Chinese investor community, i.e., East Money, we derive two indices that represent the information contained in research reports: one is the attention of research reports and the other is the average stock rating given by research reports; then we develop an investor sentiment indicator using the machine learning method. Based on behavioral finance theory, we hypothesize that research reports have a significant effect on stock returns and investor sentiment plays a mediating role in it. The empirical analysis results confirm the above hypotheses. Specifically, the average stock rating given by research reports can better predict future stock returns, and investor sentiment plays a partial mediating role in the relationship between stock rating and stock returns.

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Informational Role of Social Media: Evidence from Twitter Sentiment

Cited by 14 publications

References 0 publications

Social sentiment segregation: Evidence from Twitter and Google Trends in Chile during the COVID-19 dynamic quarantine strategy

Social sentiment segregation: Evidence from Twitter and Google Trends in Chile during the COVID-19 dynamic quarantine strategy

The value-relevance of social media activity of Finnish listed companies

The Influence of Research Reports on Stock Returns: The Mediating Effect of Machine-Learning-Based Investor Sentiment

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