The Effects of Twitter Sentiment on Stock Price Returns

Social media are increasingly reflecting and influencing behavior of other complex systems. In this paper we investigate the relations between a well-know micro-blogging platform Twitter and financial markets. In particular, we consider, in a period of 15 months, the Twitter volume and sentiment about the 30 stock companies that form the Dow Jones Industrial Average (DJIA) index. We find a relatively low Pearson correlation and Granger causality between the corresponding time series over the entire time period. However, we find a significant dependence between the Twitter sentiment and abnormal returns during the peaks of Twitter volume. This is valid not only for the expected Twitter volume peaks (e.g., quarterly announcements), but also for peaks corresponding to less obvious events. We formalize the procedure by adapting the well-known "event study" from economics and finance to the analysis of Twitter data. The procedure allows to automatically identify events as Twitter volume peaks, to compute the prevailing sentiment (positive or negative) expressed in tweets at these peaks, and finally to apply the "event study" methodology to relate them to stock returns. We show that sentiment polarity of Twitter peaks implies the direction of cumulative abnormal returns. The amount of cumulative abnormal returns is relatively low (about 1-2%), but the dependence is statistically significant for several days after the events

Collaborative Speculation and Overvaluation: Evidence from Social Media

I use data from StockTwits and Twitter to provide evidence that investor attention on social media in the period before earnings is related to short-term overvaluation, consistent with bullish investors herding around common information. In the 2 to 60 days after earnings, returns for companies in the highest quintile of pre-earnings announcement investor attention are 4.2 percent lower than those of companies in the lowest quintile. I find evidence that the negative post-earnings drift result found in this study is related to investors waiting until after earnings are announced to enact costly arbitrage strategies. I further examine intra- and inter-network herding and find evidence that social media influences investors beyond the population of active users. This study contributes to prior literature on herding, social media, and speculation and arbitrage

The Effects of Twitter Sentiment on Stock Price Returns

Social media are increasingly reflecting and influencing behavior of other complex systems. In this paper we investigate the relations between a well-known micro-blogging platform Twitter and financial markets. In particular, we consider, in a period of 15 months, the Twitter volume and sentiment about the 30 stock companies that form the Dow Jones Industrial Average (DJIA) index.We find a relatively low Pearson correlation and Granger causality between the corresponding time series over the entire time period. However, we find a significant dependence between the Twitter sentiment and abnormal returns during the peaks of Twitter volume. This is valid not only for the expected Twitter volume peaks (e.g., quarterly announcements), but also for peaks corresponding to less obvious events. We formalize the procedure by adapting the well-known "event study" from economics and finance to the analysis of Twitter data. The procedure allows to automatically identify events as Twitter volume peaks, to compute the prevailing sentiment (positive or negative) expressed in tweets at these peaks, and finally to apply the "event study" methodology to relate them to stock returns. We show that sentiment polarity of Twitter peaks implies the direction of cumulative abnormal returns. The amount of cumulative abnormal returns is relatively low (about 1-2%), but the dependence is statistically significant for several days after the events

Information or noise: How Twitter facilitates stock market information aggregation

We assess the relevance of Twitter for stock-relevant information dissemination in financial markets on the single stock level. We use a unique dataset including more than 12 million Twitter feeds linked to specific firms. Using intraday data for the computation of advanced trading metrics, such as effective spreads, intraday volatility, and a daily version of the microstructure variable probability of informed trading (PIN), we measure the impact of Twitter activity on trading and information dissemination. The PIN model indicates that more uninformed than informed traders rush to the market along with rising Twitter activity. These results indicate that Twitter serves as an excellent indicator of news that is relevant for the stock market. However, we show that Twitter does not lead traditional news channels. In contrast, Twitter activity follows the market and has no predictive power with regard to future stock trading volume or volatility on the single stock level

Forecasting power of social media sentiment time series

Social media are not only the new form of communication, but also give the ability to big industry players, like Facebook, to analyze overwhelming amounts of data about customers’ behavior. The focus of this study was to analyze if social media time series have the power to predict the evolution of financial markets. Despite the short time frame being analyzed, the study delivered promising results that social media time series may be a leading indicator for market behavior after special events. Building on my findings, an applied sentiment trading strategy delivered positive abnormal returns and statistically significant positive alpha in and out-of-sample

Essays on Information in Finance

In the first paper I investigate how firms' disclosure strategies shape the relationship between information disseminated on social media and stock returns. After constructing a novel and comprehensive dataset of over 7 million tweets posted by S&P 1500 firms, I adopt text analysis methods and find that firms with negative earnings surprises have higher announcement returns when they tweet about financial news, despite being ex-ante less likely to tweet about it. Further, I nd that rms which tweet about financial news have less short run autocorrelation in returns and higher demand for information from investors. The second paper is a joint work with M. J. Arteaga-Garavito, M. M. Croce, and P. Farroni. We quantify the exposure of major financial markets to news shocks about global contagion risk accounting for local epidemic conditions. For a wide cross section of countries, we construct a novel data set comprising (i) announcements related to COVID19, and (ii) high-frequency data on epidemic news diffused through Twitter. Across several classes of financial assets, we provide novel empirical evidence about {financial dynamics (i) around epidemic announcements, (ii) at a daily frequency, and (iii) at an intra-daily frequency.} Formal estimations based on both contagion data and social media activity about COVID19 confirm that the market price of contagion risk is very significant. We conclude that prudential policies aimed at mitigating either global contagion or local diffusion may be extremely valuable. The third paper is a joint work with Lucia Alessi, Brunella Bruno, Elena Carletti and Katja Neugebauer. We analyze the determinants of coverage ratios and their components (NPLs and loss loan reserves) in a large sample of European banks. We find that bank-specific factors, and in particular credit risk variables including forward-looking indicators, matter the most. We also uncover that coverage ratios do not adjust sufficiently when asset quality deteriorates but that high-NPL banks tend to be relatively better covered. At the country level, specific macroprudential levers as well as developing NPL secondary markets enhance bank coverage policy. Our findings emphasize the importance of micro prudential oversight and call for more stringent macro policies in high-NPL countries