Daily happiness and stock returns: Some international evidence

Zhang, Wei; Xiao, Liquan; Shen, Dehua; Teglio, Andrea

doi:10.1016/j.physa.2016.05.026

Cited by 82 publications

(34 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The sentiment of investors could also predict the evolution of indices a few days in advance [33]. For the data of this investigation, the period in which the markets reacted, in the COVID-19 season, was 4 to 13 days after the information was shared and disseminated on Twitter; unlike the H1N1 season, which was 1-2 days [17].…”

Section: Discussionmentioning

confidence: 99%

“…Twitter account with high correlations was The New York Times (46.8 million followers) because it covered both nancial and economic topics, such as music, culture, sports, art, and entertainment; which generated different kinds of sentiments in investors [33]; and it is a traditional means of communication among them.…”

Section: Discussionmentioning

confidence: 99%

“…According to Zhang et al [33], the daily happiness generated on Twitter in uences the performance and volatility of international indices. In this paper, they examined the relationships between the everyday feeling of happiness on Twitter and stock market performance in 11 international stock markets, dividing this feeling of happiness into quintiles from the least to the happiest days.…”

Section: Sentiment Analysis and Financementioning

confidence: 99%

See 2 more Smart Citations

Does Twitter affect Stock Market Decisions?Financial Sentiment Analysis in Pandemic Seasons: A Comparative Study of H1N1 and COVID-19

Valle-Cruz

Fernandez-Cortez

López-Chau

et al. 2020

Preprint

View full text Add to dashboard Cite

Abstract Backgroud:Investors are always playing with the fears and desires of buyers and sellers. Stock exchange markets are not the exception. Financial sentiment analysis allows us to understand the effect of reactions and emotions on social media in the stock market. In this research, we analyze Twitter data and financial indices to answer the question: How do polarity generated by the posts on Twitter influence financial indices behavior in pandemic seasons? Methods:The study is based on the sentiment analysis of influential Twitter accounts in this field and its relationship with the behavior of important financial indices. To achieve this, we tested four lexicons to detect polarity on Twitter. Results:Our findings shows that the period in which the markets reacted was 6 to 13 days after the information was shared and disseminated on Twitter in the COVID-19 season, and 1 to 2 day for H1N1 season. Furthermore, in our analysis, we found that the lexicons that got the best results for sentiment analysis on Twitter were S140 and Affin.Conclusions:Financial sentiment analysis is an important technique to forecasting stock market and polarity is the most widely used technique in the financial area. There is a relationship between the polarity in Twitter and the financial indexes behavior. The most influential Twitter accounts during the pandemic season were The New York Times, Bloomberg, CNN News, and Investing, presenting a very high relation between sentiments on Twitter and the stock market behavior.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Sentiment Analysis and Financementioning

confidence: 99%

See 1 more Smart Citation

Does Twitter affect Stock Market Decisions?Financial Sentiment Analysis in Pandemic Seasons: A Comparative Study of H1N1 and COVID-19

Valle-Cruz

Fernandez-Cortez

López-Chau

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…We mainly find that the majority of these literatures focus on earnings announcement, the number of the headlines, and the expenditures on advertising, but only a few employ the information content. The second refers to the investigation on the relationships between new media news and return predictability as well as market dynamics [18][19][20][21][22][23][24][25][26][27][28][29][30][31]. This line of work has shifted research interests away from building more complicated models to attach more importance to data and its impacts on market dynamics.…”

Section: News and Stock Market Performancesmentioning

confidence: 99%

The Dynamic Cross‐Correlations between Mass Media News, New Media News, and Stock Returns

Zhang

Shen

et al. 2018

Complexity

Self Cite

View full text Add to dashboard Cite

We investigate the dynamic cross-correlations between mass media news, new media news, and stock returns for the SSE 50 Index in Chinese stock market by employing the MF-DCCA method. The empirical results show that (1) there exist power-law cross-correlations between two types of news as well as between news and its corresponding SSE 50 Index return; (2) the cross-correlations between mass media news and SSE 50 Index returns show larger multifractality and more complicated structures; (3) mass media news and new media news have both complementary and competitive relationships; (4) with the rolling window analysis, we further find that there is a general increasing trend for the cross-correlations between the two types of news as well as the cross-correlations between news and returns and this trend becomes more persistent over time.

show abstract

“…al., 2011; Zhang et al, 2016a), Google Trends (Da et al, 2011;Da et al, 2015), Baidu Index (Zhang et al, 2013), Baidu News (Zhang et al, 2014;Shen et al, 2016), online stock commentary column (Zhang et al, 2016c) and Sina Weibo (Jin et al, 2016). In particular, Da et al (2011) show that the search frequency from Google Trends can predict stock returns in the next weeks.…”

mentioning

confidence: 99%

Baidu index and predictability of Chinese stock returns

et al. 2017

Self Cite

View full text Add to dashboard Cite

A number of studies have investigated the predictability of Chinese stock returns with economic variables. Given the newly emerged dataset from the Internet, this paper investigates whether the Baidu Index can be employed to predict Chinese stock returns. The empirical results show that 1) the Search Frequency of Baidu Index (SFBI) can predict next day's price changes; 2) the stock prices go up when individual investors pay less attention to the stocks and go down when individual investors pay more attention to the stocks; 3) the trading strategy constructed by shorting on the most SFBI and longing on the least SFBI outperforms the corresponding market index returns without consideration of the transaction costs. These results complement the existing literature on the predictability of Chinese stock returns and have potential implications for asset pricing and risk management.

show abstract

Daily happiness and stock returns: Some international evidence

Cited by 82 publications

References 48 publications

Does Twitter affect Stock Market Decisions?Financial Sentiment Analysis in Pandemic Seasons: A Comparative Study of H1N1 and COVID-19

Does Twitter affect Stock Market Decisions?Financial Sentiment Analysis in Pandemic Seasons: A Comparative Study of H1N1 and COVID-19

The Dynamic Cross‐Correlations between Mass Media News, New Media News, and Stock Returns

Baidu index and predictability of Chinese stock returns

Contact Info

Product

Resources

About