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

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

Predictive power of web Big Data in Financial Economics

Due to the availability of big datasets, the digital revolution is profoundly changing our capability of understanding society and forecasting the outcome of many social and economic systems. Increasingly sophisticated semantic techniques are adopted to automatically interpret information published in articles, blogs, newspapers etc. Unfortunately, irrelevant or already commonly known information can increase the noise of these signals and make their predictive power severely affected or vanished. In this thesis we present a novel methodology which combines the information coming from the sentiment conveyed by public news with the browsing activity of the users of a finance specialized portal to forecast price returns at daily and intra-day time scale. To this aim we leverage a unique dataset consisting of a fragment of the log of Yahoo! Finance, containing the news articles displayed on the web site and the respective number of ”clicks”, i.e. the visualizations made by the users. Our analysis considers 100 highly capitalized US stocks in a one-year period between 2012 and 2013. Noticeably the sentiment signal and the browsing activity individually taken have very small or no predictive power. Conversely, constructing a signal which in a given time interval gives the average sentiment of the clicked news, weighted by the number of clicks, we show that for more than 50% of the investigated companies it Granger causes price returns. Our result indicates a wisdom of the crowd effect which allows to exploit users’ activity to identify and weight properly the relevant and surprising news, enhancing considerably the forecasting power of the news sentiment. In addition we study the presence of predictive power between Twitter messages and price return both n terms of volumes and aggregate sentiment and we present an ”event study” methodology to measure the impact of days of high attention on Twitter on the stock price

Coupling news sentiment with web browsing data predicts intra-day stock prices

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, we show that the combined use of sentiment analysis of news and browsing activity of users of Yahoo! Finance allows to forecast 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 more the 50% of the companies such signal Granger-causes 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

<i>CAR</i> for all detected events, including EA.

<p>The <i>x</i> axis is the lag between the event and <i>CAR</i>, and the red markers indicate days with statistically significant abnormal return.</p

The Twitter data for the 15 months period.

<p>For each company, there is the DJIA ticker symbol and the number of collected tweets.</p

Daily time series of Twitter volume for the Nike company.

<p>Detected Twitter peaks and actual EA events are indicated.</p

Results of the Pearson correlation and Granger causality tests.

<p>Companies are ordered as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0138441#pone.0138441.t001" target="_blank">Table 1</a>. The arrows indicate a statistically significant Granger causality relation for a company, at the 5% significance level. A right arrow indicates that the Twitter variable (sentiment polarity <i>P</i>_<i>d</i> or volume <i>TW</i>_<i>d</i>) Granger-causes the market variable (return <i>R</i>_<i>d</i>), while a left arrow indicates that the market variable Granger-causes the Twitter variable. The counts at the bottom show the total number of companies passing the Granger test.</p

A comparison of the inter-annotator agreement and the classifier performance.

<p>The inter-annotator agreement is computed from the examples labeled twice. The classifier performance is estimated from the 10-fold cross-validation.</p