This study investigates tail dependence among five major cryptocurrencies, namely Bitcoin, Ethereum, Litecoin, Ripple, and Bitcoin Cash, and uncertainties in the gold, oil, and equity markets. Using the cross-quantilogram method and quantile connectedness approach, we identify cross-quantile...

The rapid rise of Bitcoin and its increasing global adoption has raised concerns about its impact on traditional markets, particularly in periods of economic turmoil and uncertainty such as the COVID-19 pandemic. This study examines the extent of the volatility contagion from the Bitcoin market...

This paper investigates the impact of Twitter attention, measured by abnormal number of tweets on stock trading activities. We find that Twitter attention has predictive power for future stock volatility and trading volume. A heightened number of tweets is followed by high volatility and trading...

Using a novel and direct measure of investor sentiment, I find that Facebook's Gross National Happiness (GNH) has the ability to predict changes in both daily returns and trading volume in the US stock market. For instance, an increase of one standard deviation in GNH is associated with an...

With the rise of social media, investors have a new tool to measure sentiment in real time. However, the nature of these sources of data raises serious questions about its quality. Since anyone on social media can participate in a conversation about markets -- whether they are informed or not --...

We study the effect on stock volatility and turnover of coverage by traditional news media and social media. We find that coverage by traditional news media predicts decreases in subsequent volatility and turnover, but coverage by social media predicts increases in volatility and turnover. These...

Using a novel and direct measure of investor sentiment, I find that Facebook's Gross National Happiness (GNH) has the ability to predict changes in both daily returns and trading volume in the US stock market. For instance, an increase of one standard deviation in GNH is associated with an...

This research analyses the link between fundamental information, social media sentiment, and stock returns from 2010 to 2018. We are interested in whether social media sentiment provides additional information to already published fundamental information, such as financial information and...

This paper proposes a new model to reveal the underlying mechanism of excess stock returns comovement by modeling two types of information diffusion behaviors of investors on social media, i.e., common attention on different forums and information interaction among investors on the same forum....

This paper applies a recently developed social media-based sentiment proxy for the construction of a new risk factor for sentiment-augmented asset pricing models on U.S. equities. Accounting for endogeneity, autocorrelation and heteroskedasticity in a GMM framework, we find that the inclusion...