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

Web search queries can predict stock market volumes