  We live in a computerized and networked society where many of our actions
leave a digital trace and affect other people's actions. This has lead to the
emergence of a new data-driven research field: mathematical methods of computer
science, statistical physics and sociometry provide insights on a wide range of
disciplines ranging from social science to human mobility. A recent important
discovery is that query volumes (i.e., the number of requests submitted by
users to search engines on the www) can be used to track and, in some cases, to
anticipate the dynamics of social phenomena. Successful exemples include
unemployment levels, car and home sales, and epidemics spreading. Few recent
works applied this approach to stock prices and market sentiment. However, it
remains unclear if trends in financial markets can be anticipated by the
collective wisdom of on-line users on the web. Here we show that trading
volumes of stocks traded in NASDAQ-100 are correlated with the volumes of
queries related to the same stocks. In particular, query volumes anticipate in
many cases peaks of trading by one day or more. Our analysis is carried out on
a unique dataset of queries, submitted to an important web search engine, which
enable us to investigate also the user behavior. We show that the query volume
dynamics emerges from the collective but seemingly uncoordinated activity of
many users. These findings contribute to the debate on the identification of
early warnings of financial systemic risk, based on the activity of users of
the www.
