Happiness Sentiment Research Articles

Solar energy and happiness sentiment: the pursuit of sustainable development under impacts of global volatility

Solar energy and happiness sentiment: the pursuit of sustainable development under impacts of global volatility

Twitter‘s happiness sentiment index impacts on financial markets: an integrated overview of empirical findings

Abstract This survey paper investigates the empirical findings of academic work that explores the nexus between the highly innovative Twitter happiness sentiment index and a range of financial assets. An integrated overview of econometric outcomes and the relevant investment policy implications are provided. It is revealed that investor happiness reinforces the safe haven abilities of gold. Moreover, major stock indices are highly influenced by the happiness index especially at higher quantiles. Reverse causality between the happiness index and stock indices is also detected but in a weaker level. This survey contributes to better understanding investment decisions based on behavioural finance and provides evidence about the nexus of investor sentiment estimation with the financial sector nowadays. JEL Classification numbers: G15, G40, Q02. Keywords: Investor happiness, Investor sentiment, Twitter, Survey, Gold, Stock prices.

Twitter’s daily happiness sentiment, economic policy uncertainty, and stock index fluctuations

This research applies quantile Granger causality and impulse-response analyses to evaluate the causal linkages among Twitter’s daily happiness sentiment, economic policy uncertainty (EPU), and S&P 500 indices across the U.S. stock market cycles. We present notable evidence of bi-directional causality among cyclical components of Twitter’s daily happiness sentiment, economic policy uncertainty, and S&P 500 indices for most quantiles. The causal linkage of Twitter’s daily happiness sentiment and S&P 500 indices identified in this study reconciles the so-called Easterlin Paradox and Easterlin Illusion arguments from previous studies on income-happiness relationship. Moreover, given a high (low) EPU level, the positive (negative) impulse-response effects between the Twitter’s daily happiness sentiment and the S&P 500 indices are justified during a stock market bust cycle, but the signs of these correlations change to negative (positive) during a stock market boom cycle. These findings imply that investors’ hedging strategies can be linked to the surveillance of the Twitter’s daily happiness sentiment index.

The relationship between global stock and precious metals under Covid-19 and happiness perspectives

In this paper, we examine the relationship between global stock markets, as respectively represented by the FTSE All-World Series and the MSCI Emerging Markets indexes, and the S&P GSCI Precious Metals index from 01 September 1999 to 03 May 2021. We employ the conditional correlation multivariate generalized autoregressive conditional heteroskedasticity (MGARCH) to investigate this stock-precious metals nexus in terms of return and volatility spillovers. The study assesses impacts of the Covid-19 pandemic on the stock-precious metals nexus and further examine this relationship by supplementing the Twitter’s Daily Happiness Sentiment index to the methodological framework for the period from 01 January 2020 to 03 May 2021. We find that precious metals positively influence stock markets before the Covid-19 outbreak and firmly play a valuable role due to their hedge and safe haven characteristics. In contrast, the bivariate GARCH framework does not provide statistically significant evidence on the stock-precious metals nexus during the Covid-19 pandemic. Meanwhile, the tri-variate GARCH approach with stock markets, precious metals, and happiness sentiment indexes reveals sufficiently complicated interactions between these return series. Prominently, past change in the happiness index negatively affects the stock returns but positively drives the performance of precious metals. These findings indirectly demonstrate the stock-precious metals nexus under impacts of the Covid-19 pandemic and reflect the demand of precious metals during crisis periods. Accordingly, we suggest a reasonable method of adjusting the proxies when no interaction effect is significantly found during unprecedented outbreaks.

Does happiness forecast implied volatility? Evidence from nonparametric wave-based Granger causality testing

Past studies on the causal relationship between online sentiment and implied volatility indices report mixed results. In this paper, we employ a daily happiness index extracted from Twitter as a proxy for online sentiment to investigate the relationship between sentiment and the Chicago Board of Option Exchange volatility indices. Applying a nonparametric wave-based Granger causality test, allows us close examination of frequencies and periods of causality. Investigating four different subsample periods, we evidence that daily happiness sentiment consistently manifests short-term causality of implied volatility indices. This is evidenced over our sample period as a whole, and for three out of four investigated subperiods—the exception being a subperiod of three years following the Global Financial Crisis. Additional Diebold and Yilmaz (2012) spillover testing evidences that the causal relationship between happiness and implied volatility is strongest for stable low happiness subperiods. Importantly, we also evidence intervals where spillovers from happiness to implied volatility spike sharply. This suggests that the relationship between happiness and implied volatility is heavily conditioned by exogenous factors. Consequently, while we identify consistent short-term causality of happiness to implied volatility measures, economically meaningful forecasting of implied volatility from happiness remains elusive. Our findings should be of considerable interest to investors and practitioners, as well as to behavioral finance researchers interested in how sentiment shapes the ‘uncertainty of uncertainty.

The relationship between global wealth and happiness: An analytical study of returns and volatility spillovers

This paper explores the relationship between global wealth and happiness. We employ a bivariate generalized autoregressive conditional heteroskedasticity framework for global wealth and happiness represented, respectively, by FTSE All-World and Twitter's Daily Happiness Sentiment indexes from October 14, 2013 to December 31, 2019. We find that daily changes in happiness significantly mitigate wealth volatility, and daily wealth returns positively affect the changes in happiness sentiment. These findings reveal a spiral transmission in daily changes in happiness sentiment and global wealth volatility and returns. Metaphysically, our findings imply that the relationship between the mind and matter of finance is either materialist or monistically neutral. From alternative perspective, we construct a pseudo portfolio of global wealth and happiness indexes in which the factor of happiness plays a more important role. Thus, a concentration on happiness is preferable for the pursuit of multiple objectives, which essentially include mind and matter.

Predictive role of online investor sentiment for cryptocurrency market: Evidence from happiness and fears

We examine the predictive ability of online investor sentiment for six major cryptocurrency returns. For this, we use two proxies, the FEARS index of Da et al. (2015) and Twitter Happiness sentiment, applying the bivariate cross-quantilogram of Han et al. (2016). Happiness sentiment index significantly predicts Bitcoin return as well as other major cryptocurrencies at the two extreme states of the market and for extreme levels of sentiment. Hence, investors should readjust their portfolios according to the market sentiment and limit their decision on the safe-haven property of Bitcoin. As to FEARS, predictability also exists but is rather pronounced for a low level of sentiment. Overall, Happiness sentiment reveals to be a persistent and robust predictor for most cryptocurrency returns. FEARS index also shows significant predictability of returns, but the predictability is weaker and mainly in the short-term. In summary, our findings provide evidence that online investor sentiment is a significant nonlinear predictor for most major cryptocurrencies returns, suggesting though the superiority of Twitter to Google-based online investor sentiment proxy. Moreover, cryptocurrency returns seem to be driven more by sentiment transmitted through social media than with macroeconomic news, which is in line with the nature of cryptocurrency participants, mainly young individuals computer enthusiasts.

Does Twitter Happiness Sentiment predict cryptocurrency?

AbstractWe examine the predictive ability of Twitter Happiness Sentiment for six major cryptocurrencies using daily data from August 7, 2015 to December 31, 2019. At first instance, our results conclude a significant nonlinear relationship between Twitter Happiness Sentiment and cryptocurrencies. The nonlinear dependence structure is further enhanced when using the quantile‐on‐quantile (QQ) analysis, which indicates that high and low sentiment predicts returns of five cryptocurrencies. These findings are statistically and economically significant.

Happiness sentiments and the prediction of cross-border country exchange-traded fund returns

This research examines whether social media (Twitter) happiness sentiment and country-level happiness sentiment indices predict cross-border ETF returns. To account for complicated associations between happiness sentiment and ETF returns, we use a quantile regression approach and find that Twitter and trading market (U.S.) happiness sentiments are strong predictors of future ETF returns, for which both have far greater predictive power than those of their home countries. Home country happiness indices exhibit asymmetric impacts across quantiles, suggesting the importance of trading country (U.S.) and Twitter happiness sentiments. Higher U.S. and home countries’ freedom to make life choices, absence of corruption perception, and confidence in national government precede higher ETF returns, while U.S. GDP, social support, health life expectancy, positive affect, and negative affect precede lower (abnormal) returns. We find that higher return quantile country ETFs provide a safe haven for U.S. investors during a U.S. bear market.

Quantifying the cross sectional relation of daily happiness sentiment and return skewness: Evidence from US industries

In this paper, we initiate cross-sectional return skewness correlation study between investor daily happiness sentiment (DHS) and twenty-two US industry indices. The Twitter happiness index, originated from the world’s largest microblog platform, Twitter.com, is employed as the representative of DHS. Also, with quantile setting of DHS, we break our full sample into five subsamples and detect apparent and reliable skewness distinctions among DHS subgroups. We further implement the robustness check with altered subgroups for the credibility enhancement. In summary, the robustness results follow the steps of original findings.

Quantifying the cross sectional relation of daily happiness sentiment and stock return: Evidence from US

In this paper, we utilize the natural log value of Twitter happiness index as the measurement for investor daily happiness sentiment (DHS) and investigate its cross-sectional correlations with twelve US indices. With correlation coefficients and Granger causality tests, we find contemporaneous and long-term interactions between DHS and indices returns. In addition, we discover noticeable return differences across quantile subgroups and the differences are further qualified with significant statistics. We also carry out another robustness check with altered quantile setting. In general, the robustness results show no divergence with prior empirical findings.

Quantifying the correlation and prediction of daily happiness sentiment and stock return: The Case of Singapore

In this paper, with the newly emerged Twitter happiness index, we employ the VAR regression, linear, and nonlinear Granger causality tests to investigate the predictive correlations between daily happiness sentiment (DHS) and Singapore Straits Times Index (STI) stock performance indicators. The empirical results reveal that DHS presents significant predictability with future STI return. While, for the realized volatility, no compelling forecasting powers are detected. We also perform another two subsample tests as robustness checks. In general, the subsample results are in line with those of the full sample.

Quantifying the Cross Sectional Relation of Daily Happiness Sentiment and Stock Return: Evidence From Us

Quantifying the Cross Sectional Relation of Daily Happiness Sentiment and Stock Return: Evidence From Us

The cross-correlations between online sentiment proxies: Evidence from Google Trends and Twitter

In this paper, we explore the cross-correlations between two commonly-employed online sentiment proxies, i.e., the Financial and Economic Attitudes Revealed by Search (FEARS) from Google Trends and Daily Happiness Sentiment (DHS) from Twitter, with the methodology of MF-DCCA. The empirical results mainly show that: firstly, there exists power-law cross-correlation between the FEARS and DHS and the cross-correlation between them perform multifractality; secondly, the degree of multifractality in short term is significantly smaller than that in long term indicating a more stable cross-correlation in short term; finally, with the rolling window analysis, we further find that the evolution of the cross-correlations between FEARS and DHS is erratic.

Twitter’s daily happiness sentiment and international stock returns: Evidence from linear and nonlinear causality tests

With newly emerged daily happiness sentiment (dhs) from Twitter, we find strong evidence that dhs can Granger cause indexes returns in linear causality test, while the opposite direction is more prominent in nonlinear causality test. Besides, dhs and indexes returns exhibit strong nonlinear relationships in American, while dhs cannot Granger cause indexes returns in Middle East and North Africa. Taken together, all these findings indicate interdependencies between online activities and stock markets.

Quantifying the cross-sectional relationship between online sentiment and the skewness of stock returns

The constantly increasing utilization of social media as the alternative information channel, e.g., Twitter, provides us a unique opportunity to investigate the dynamics of the financial market. In this paper, we employ the daily happiness sentiment extracted from Twitter as the proxy for the online sentiment dynamics and investigate its association with the skewness of stock returns of 26 international stock market index returns. The empirical results show that: (1) by dividing the daily happiness sentiment into quintiles from the least to the most happiness days, the skewness of the Most-happiness subgroup is significantly larger than that of the Least-happiness subgroup. Besides, there exist significant differences in any pair of subgroups; (2) in an event study methodology, we further show that the skewness around the highest happiness days is significantly larger than the skewness around the lowest happiness days.

Twitter's daily happiness sentiment and the predictability of stock returns

Using a novel investor sentiment proxy extracted from Twitter, this paper investigates whether investor sentiment as expressed in daily happiness has predictive power for stock returns in 10 international stock markets. To account for complex relationships between sentiment and stock returns, a Granger non-causality test in quantiles is used. Our empirical results indicate that the causal relations vary across different quantiles. We observe that the causal relationship from happiness sentiment to stock returns exist only in high quantiles interval. The causal relationship from stock returns to happiness sentiment exists only in the tail area.

Daily happiness and stock returns: The case of Chinese company listed in the United States

Existing literature exclusively focuses on the association between local investor sentiment and local stock market performance. In this paper, we investigate the contemporaneous and the lead-lag relationship between local daily happiness sentiment extracted from Twitter and stock returns of cross-listed companies, i.e., the Chinese companies listed in the United States. The empirical results show that: 1) by respectively controlling for the firm capitalization, liquidity and volatility, there exists the largest skewness on the Most-happiness subgroup. (2) There exist bi-directional relationships between daily happiness sentiment and market variables, i.e., the stock return, range-based volatility and excess trading volume. (3) There are significantly positive stock returns, higher excess trading volume and higher range-based volatility around the daily happiness sentiment spike days. These findings not only suggest that there exists significant interdependence between online activities and stock market dynamics, but also provide evidence for the existence of “home bias”.

Daily happiness and stock returns: Some international evidence

Abstract In this paper, we examine the relations between the daily happiness sentiment extracted from Twitter and the stock market performance in 11 international stock markets. By partitioning this happiness sentiment into quintiles from the least to the happiest days, we first show that the contemporary correlation coefficients between happiness sentiment and index return in the 4 and most-happiness subgroups are higher than that in least, 2 and 3-happiness subgroups. Secondly, the happiness sentiment can provide additional explanatory power for index return in the most-happiness subgroup. Thirdly, the daily happiness can granger-cause the changes in index return for the majority of stock markets. Fourthly, we find that the index return and the range-based volatility of the most-happiness subgroup are larger than those of other subgroups. These results highlight the important role of social media in stock market.