  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.
