DO INVESTORS CARE ABOUT PRESIDENTIAL COMPANY‐SPECIFIC TWEETS?

When the president of the United States tweets, do investors respond? We analyze the impact of tweets from President Donald J. Trump's official Twitter accounts from November 9, 2016 to December 31, 2017 that include names of publicly traded companies. We find that these tweets move company stock prices and increase trading volume, volatility, and institutional investor attention, with a stronger impact before the presidential inauguration. There is some evidence that the initial impact of the presidential tweets on stock prices is reversed in the next few trading days.     

Tweets and Trades: the Information Content of Stock Microblogs

Microblogging forums (e.g., Twitter) have become a vibrant online platform for exchanging stock‐related information. Using methods from computational linguistics, we analyse roughly 250,000 stock‐related messages (so‐called tweets) on a daily basis. We find an association between tweet sentiment and stock returns, message volume and trading volume, as well as disagreement and volatility. In contrast to previous related research, we also analyse the mechanism leading to an efficient aggregation of information in microblogging forums. Our results demonstrate that users providing above average investment advice are retweeted (i.e., quoted) more often and have more followers, which amplifies their share of voice.     

Social media and price discovery: The case of cross-listed firms

In this article, we examine whether social media information affects the price-discovery process for cross-listed companies. Using over 29 million overnight tweets mentioning cross-listed companies, we examine the role of social media for a link between the last periods of trading in the US markets and the first periods in the UK market. Our estimates suggest that the size and content of information flows on social networks support the price-discovery process. The interactions between lagged US stock features and overnight tweets significantly affect stock returns and volatility of cross-listed stocks when the UK market opens. These effects weaken and disappear 1 to 3 hr after the opening of the UK market. We also develop a profitable trading strategy based on overnight social media, and the profits remain economically significant after considering transaction costs.     

Asymmetric information,volatility components and the volume–volatility relationship for the CAC40 stocks

This paper investigates the relationship between trading volume components and various realized volatility measures for the CAC40 index constituents. A mixture-of-distribution model is used to decompose trading volume into informed and liquidity components. Realized volatility is broken down into continuous volatility and jumps. Our findings confirm the strong positive contemporaneous relationship between total trading volume and volatility when realized volatility and its continuous component are considered. A limited evidence of the effect of total trading volume on discontinuous volatility is found. The positive volume–volatility relationship is mainly driven by the informed component of trading volume. Conversely, liquidity volume is negatively related to realized volatility lending some support to the view that liquidity trading dampens the volatility of stock returns. A stronger negative relationship between liquidity volume and volatility jump is uncovered.     

Index Futures Trading and Stock Return Volatility: Evidence from the Introduction of Mid Cap 400 Index Futures

The MidCap 400 stock index is used to provide new evidence on the relation between stock index futures trading and stock return volatility. The study documents a significant decrease in return volatility and systematic risk, and a significant increase in trading volume for the MidCap 400 stocks after the introduction of the MidCap index. A control sample of medium-capitalization stocks, however, exhibits similar contemporaneous changes in these measures. The MidCap stocks and the control stocks also experience a significant decrease in volatility and an increase in volume after the introduction of MidCap 400 index futures. Thus, the study finds no difference in the behavior of the MidCap 400 stocks and the control stocks and no evidence of a relation between index futures trading and volatility in the stock market.     

THE EMPIRICAL RELATIONSHIP BETWEEN TRADING VOLUME,RETURNS AND VOLATILITY

This paper presents an empirical analysis of the relationship between trading volume, returns and volatility in the Australian stock market. The initial analysis centres upon the volume-price change relationship. The relationship between trading volume and returns, irrespective of the direction of the price change, is significant across three alternative measures of daily trading volume for the aggregate market. This finding also provides basic support for a positive relationship between trading volume and volatility. Furthermore, evidence is found supporting the hypothesis that the volume-price change slope for negative returns is smaller than the slope for non-negative returns, thereby supporting an asymmetric relationship which is hypothesised to exist because of differential costs of taking long and short positions. Analysis at the individual stock level shows weaker support for the relationship. A second related hypothesis is tested in which the formation of returns is conditional upon information arrival which similarly affects trading volume. The hypothesis is tested by using the US overnight return to proxy for expected “news” and trading volume to proxy for news arrival during the day. The results show a reduction in the significance and magnitude of persistence in volatility and hence are consistent with explaining non-normality in returns (and ARCH effects) through the rate of arrival of information. The findings in this paper help explain how returns are generated and have implications for inferring return behaviour from trading volume data.     

Volume,volatility and information linkages in the stock and option markets

This paper examines the relationship between option trading activity and stock market volatility. Although the option market is uniquely suited for trading on volatility information, there is little analysis on how trading activity in this market is linked to stock price volatility. The bulk of the discussion tends to focus on whether trading activity in the stock market is informative about stock volatility. To analyze the information in option trading activity for stock market volatility, a sample of 15 stocks with the highest option trading volume is selected. For each stock, it is noted that the trading activities in the put and call option markets have significant explanatory power for stock market volatility. In addition, the results indicate that the call option trading activity has a stronger impact on stock volatility compared with that of the put options. Our results demonstrate that information and sentiment in the option market is useful for the estimation of stock market volatility. Also, the significance of the effects of option trading activity on stock price volatility is observed to be comparable to that of stock market trading activity. Furthermore, the persistence and asymmetric effects in the volatility of some stocks tend to disappear once option trading activity is taken into account.     

Causal and Dynamic Relationships among Stock Returns,Return Volatility and Trading Volume: Evidence from Emerging markets in South-East Asia

This paper examines the causal and dynamic relationships among stock returns, return volatility and trading volume for five emerging markets in South-East Asia—Indonesia, Malaysia, Philippines, Singapore and Thailand. We find strong evidence of asymmetry in the relationship between the stock returns and trading volume; returns are important in predicting their future dynamics as well as those of the trading volume, but trading volume has a very limited impact on the future dynamics of stock returns. However, the trading volume of some markets seems to contain information that is useful in predicting future dynamics of return volatility.     

The Impact of Information Release on Stock Price Volatility and Trading Volume: The Rights Offering Case

This paper examines whether information released via rights offering announcements induces changes in price volatility and trading volume of underlying stock. The results of this paper provide support for the release of new information via offering announcements and evidence of its effects on price volatility and volume of underlying stock. Specifically, utilization of the announced information by investors is evidenced by greater trading volume following the announcement date than during the pre-announcement period. We interpret this result to mean that informedness dominates consensus. However, stock price volatility decreased from the pre-announcement period to the post-expiration period of rights offerings.     

The volume–volatility relationship and the opening of the Korean stock market to foreign investors after the financial turmoil in 1997

This paper investigates the stock volatility–volume relation in the Korean market for the period 1995–2001. Previous research examined the impact of liberalization on the Korean stock market up to the period before the financial turmoil in 1997 although the crucial measures of the liberalization were introduced after the crisis under the International Monetary Fund program. One of the major features of the reformation was the financial opening to foreign investors. In this study the ‘total’ trading volume is separated into the domestic investors’ and the foreign investors’ volume. By doing this the information used by two different groups of traders can be separated. Further, in addition to the absolute value of the returns and their squares we use the conditional volatility from a GARCH-type model as an alternative measure of stock volatility. The following observations, among other things, are noted about the volume–volatility causal relationship. First, for the entire period there is a strong bidirectional feedback between volume and volatility. In most cases this causal relationship is robust to the measures of volume and volatility used. Second, volatility is related only to ‘domestic’ volume before the crisis whereas after the crisis a bidirectional feedback relation between ‘foreign’ volume and volatility begins to exist. In other words, ‘foreign’ volume tends to have more information about volatility in recent years, which suggests the increased importance of ‘foreign’ volume as an information variable.      

The Dynamic Relation Between Stock Returns, Trading Volume, and Volatility

We examine the dynamic relation between returns, volume, and volatility of stock indexes. The data come from nine national markets and cover the period from 1973 to 2000. The results show a positive correlation between trading volume and the absolute value of the stock price change. Granger causality tests demonstrate that for some countries, returns cause volume and volume causes returns. Our results indicate that trading volume contributes some information to the returns process. The results also show persistence in volatility even after we incorporate contemporaneous and lagged volume effects. The results are robust across the nine national markets.     

VOLATILITY FORECASTS,TRADING VOLUME,AND THE ARCH VERSUS OPTION‐IMPLIED VOLATILITY TRADE‐OFF

We investigate empirically the role of trading volume (1) in predicting the relative informativeness of volatility forecasts produced by autoregressive conditional heteroskedasticity (ARCH) models versus the volatility forecasts derived from option prices, and (2) in improving volatility forecasts produced by ARCH and option models and combinations of models. Daily and monthly data are explored. We find that if trading volume was low during period t?1 relative to the recent past, ARCH is at least as important as options for forecasting future stock market volatility. Conversely, if volume was high during period t?1 relative to the recent past, option‐implied volatility is much more important than ARCH for forecasting future volatility. Considering relative trading volume as a proxy for changes in the set of information available to investors, our findings reveal an important switching role for trading volume between a volatility forecast that reflects relatively stale information (the historical ARCH estimate) and the option‐implied forward‐looking estimate.     

The bivariate GARCH approach to investigating the relation between stock returns,trading volume,and return volatility

We use a bivariate GJR-GARCH model to investigate simultaneously the contemporaneous and causal relations between trading volume and stock returns and the causal relation between trading volume and return volatility in a one-step estimation procedure, which leads to the more efficient estimates and is more consistent with finance theory. We apply our approach to ten Asian stock markets: Hong Kong, Japan, Korea, Singapore, Taiwan, China, Indonesia, Malaysia, the Philippines, and Thailand. Our major findings are as follows. First, the contemporaneous relation between stock returns and trading volume and the causal relation from stock returns and trading volume are significant and robust across all sample stock markets. Second, there is a positive bi-directional causality between stock returns and trading volume in Taiwan and China and that between trading volume and return volatility in Japan, Korea, Singapore, and Taiwan. Third, there exists a positive contemporaneous relation between trading volume and return volatility in Hong Kong, Korea, Singapore, China, Indonesia, and Thailand, but a negative one in Japan and Taiwan. Fourth, we find a significant asymmetric effect on return and volume volatilities in all sample countries and in Korea and Thailand, respectively.     

Does Trading Volume Contain Information to Predict Stock Returns? Evidence from China's Stock Markets

This paper examines empirical contemporaneous and causal relationships between trading volume, stock returns and return volatility in China's four stock exchanges and across these markets. We find that trading volume does not Granger-cause stock market returns on each of the markets. As for the cross-market causal relationship in China's stock markets, there is evidence of a feedback relationship in returns between Shanghai A and Shenzhen B stocks, and between Shanghai B and Shenzhen B stocks. Shanghai B return helps predict the return of Shenzhen A stocks. Shanghai A volume Granger-causes return of Shenzhen B. Shenzhen B volume helps predict the return of Shanghai B stocks. This paper also investigates the causal relationship among these three variables between China's stock markets and the US stock market and between China and Hong Kong. We find that US return helps predict returns of Shanghai A and Shanghai B stocks. US and Hong Kong volumes do not Granger-cause either return or volatility in China's stock markets. In short, information contained in returns, volatility, and volume from financial markets in the US and Hong Kong has very weak predictive power for Chinese financial market variables.     

An Empirical Examination of the Return Volatility-Volume Relation in Related Markets: The Case of Stock and Options

This paper empirically examines the impact of option trading on the relation between daily stock return volatility and stock trading volume. For a sample of firms for which options were newly listed on the CBOE from 1982 to 1985, the empirical evidence indicates that there is a structural shift in the relation after option trading is introduced. Also, the findings show that daily stock return volatility is significantly and positively correlated with contemporaneous option volume, but not one-day lagged option volume. These results suggest that contemporaneous option volume may be an important variable in modelling daily stock return volatility and heteroskedasticity.     

The UK equity market around the ex-split date

Using UK stock market data this study unveils positive abnormal returns on and around the ex-split date. These excess returns are partially predictable using the publicly available information prior to the ex-split date. There is also a persistent increase in the post-split volatility of these stocks with the results being robust to the choice of the volatility proxy. Post-split volatility is found to be positively related to trading activity. Contrary to the US findings, volatility dynamics following the stock split are better captured by changes in the daily trading volume rather than by the number of trades.     

MACROECONOMIC NEWS,STOCK TURNOVER,AND VOLATILITY CLUSTERING IN DAILY STOCK RETURNS

We study volatility clustering in daily stock returns at both the index and firm levels from 1985 to 2000. We find that the relation between today's index return shock and the next period's volatility decreases when important macroeconomic news is released today and increases with the shock in today's stock market turnover. Collectively, our results suggest that volatility clustering tends to be stronger when there is more uncertainty and disperse beliefs about the market's information signal. Our findings also contribute to a better understanding of the joint dynamics of stock returns and trading volume.     

Stock returns,trading volume,and volatility: The case of African stock markets

The presence of the African Stock Markets (ASMs) in the global frontier markets indices confirms their global portfolio diversification role. This study investigates the asymmetric and intertemporal causality among the stock returns, trading volume, and volatility of eight ASMs. Results based on the linear model reveal that return generally Granger cause trading volume. However, evidence from the quantile regression shows that lagged trading volume has a negative causal effect on returns at low quantiles and positive causal effects at high quantiles. This evidence is consistent with volume-return equilibrium models, disposition and overconfidence models, and information asymmetry models. The positive causal effects of volatility on volume support the dispersion of beliefs model. In contrast, intertemporal evidence of contemporaneous and lagged causal relationships from trading volume to volatility supports the mixture of distribution hypothesis, sequential information acquisition hypothesis, and dynamic efficient market hypothesis. Volume-return and return-volume causality dynamics are quantile-specific and therefore driven by market conditions. However, the volume-volatility causality is dependent on volatility regimes. The linear model results confirm how model misspecification can distort and even reverse empirical evidence relative to nonlinear models.     

The Impact of Firm-Specific Public News on Intraday Market Dynamics: Evidence from the Turkish Stock Market

This study attempts to discover the intraday firm-specific news announcements and return volatility relation in the Turkish stock market. The GARCH framework is utilized to investigate the impact of firm-specific public news announcements on volatility persistence with and without trading volume. For the majority of the stocks in the sample, the volatility persistence diminishes with the inclusion of firm-specific news, implying that news is impounded rapidly into prices. This effect is more pronounced for larger stocks. When there is no news, the trading volume does not appear to reduce the volatility persistence for the majority of stocks, possibly due to the presence of private information possessed by informed traders.     

Stock market structure and volatility

The procedure for opening stocks on the NYSE appears to affectprice volatility. An analytical framework for assessing themagnitude of the structurally induced volatility is presented.The ratio of variance of open-to-open returns to close-to-closereturns is shown to be consistently greater than one for NYSEcommon stocks during the period 1982 through 1986. The greatervolatility at the open is not attributable to the way in whichpublic information is released since both the open-to-open returnand the close-to-close return span the same period of time.Instead, the greater volatility appears to be attributable toprivate information revealed in trading and to temporary pricedeviations induced by specialist and other traders. The impliedcost of immediacy at the open is significantly higher than atthe close. Other empirical evidence in this article documentsthe volume of trading at the open, the time delays between theexchange opening and the first transaction in a stock, the differencein daytime volatility versus overnight volatility, and the extendto which volatility is related to trading volume.