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

Macro news and commodity returns

This paper adopts a VAR-GARCH approach to model the dynamic linkages between both the mean and the variance of macro news and commodity returns (Gold, Corn, Wheat, Soybeans, Silver, Platinum, Palladium, Copper, Aluminium and Crude Oil) over the period 01/01/2001-26/09/2014. The chosen specification also controls for the effect of the exchange rate. The results can be summarised as follows. Mean spillovers running from news to commodity returns are positive with the exception of Gold and Silver. Volatility spillovers are bigger in size and affect most commodity returns. Both firstand second moment linkages are stronger in the post-September 2008 period. Overall, our findings confirm that commodities, despite not being financial assets, are sensitive to macro news (especially their volatility), and also suggest that the global financial crisis has strengthened such linkages

Does the earnings quality matter? Evidence from a quasi-experimental setting

Investor preference for local stocks provides a quasi-experimental setting to investigate whether the market rewards firms that comply with generally accepted accounting principles. We show firms with low earnings quality trade at a premium compared to firms in compliance with accounting principles; the difference in values is greater when the role of local investor over-trading is stronger in stock price-formation, in other words for the more isolated firms. The value of the information not conveyed to the market through accounting disclosure accounts for 30% of the market-to-book. Results are robust to earnings quality definition, and show while non-local investors are sensitive to the quality of accounting information, local and better-informed investors are not. Overall, accounting quality matters. (C) 2016 Published by Elsevier Inc

Local IPOs, local delistings, and the firm location premium

Borrowing a measure from ecology, we introduce a spatial dispersion index to quantify the firm traits related to firm geographic location and investigate firm exposure to local home bias and local investor risk tolerance as determinants of corporate market value. Consistent with the investor preference for local stocks, we find listed firms benefit from a location premium that increases with firm isolation and local investor wealth. IPOs and delistings are found to affect the market value of neighboring listed firms: isolated firms decrease in value when they cluster due to local IPOs while clustered firms increase in value as they become more isolated due to local delistings. Local firm clustering and risk tolerance also affect IPO underpricing. Empirical findings depict a framework where IPOs and delistings are locally jointly determined

Coupling News Sentiment with Web Browsing Data Improves Prediction of Intra-Day Price Dynamics

The new digital revolution of big data is deeply changing our capability of understanding society and forecasting the outcome of many social and economic systems. Unfortunately, information can be very heterogeneous in the importance, relevance, and surprise it conveys, affecting severely the predictive power of semantic and statistical methods. Here we show that the aggregation of web users’ behavior can be elicited to overcome this problem in a hard to predict complex system, namely the financial market. Specifically, our in-sample analysis shows that the combined use of sentiment analysis of news and browsing activity of users of Yahoo! Finance greatly helps forecasting intra-day and daily price changes of a set of 100 highly capitalized US stocks traded in the period 2012–2013. Sentiment analysis or browsing activity when taken alone have very small or no predictive power. Conversely, when considering a news signal where in a given time interval we compute the average sentiment of the clicked news, weighted by the number of clicks, we show that for nearly 50% of the companies such signal Granger-causes hourly price returns. Our result indicates a “wisdom-of-the-crowd” effect that allows to exploit users’ activity to identify and weigh properly the relevant and surprising news, enhancing considerably the forecasting power of the news sentiment