Abstract
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 search engine traffic (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 examples 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 daily trading volumes of stocks traded in NASDAQ-100 are correlated with daily 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.
Highlights
Nowadays many of our activities leave a digital trace: credit card transactions, web activities, e-commerce, mobile-phones, GPS navigators, etc. This networked reality has favored the emergence of a new data-driven research field where mathematical methods of computer science [1], statistical physics [2] and sociometry provide effective insights on a wide range of disciplines like [3] social sciences [4], human mobility [5], etc
It has been shown that the activity of people querying search engines for keywords related to influenza and its treatment allows to anticipate the actual spreading as measured by official data on contagion collected by Health Care Agencies [10]
In our analysis we consider a set of companies (‘‘NASDAQ-100 set’’ hereafter) that consists of the companies included in the NASDAQ-100 stock market index
Summary
Nowadays many of our activities leave a digital trace: credit card transactions, web activities, e-commerce, mobile-phones, GPS navigators, etc. This networked reality has favored the emergence of a new data-driven research field where mathematical methods of computer science [1], statistical physics [2] and sociometry provide effective insights on a wide range of disciplines like [3] social sciences [4], human mobility [5], etc. It has been shown that the activity of people querying search engines for keywords related to influenza and its treatment allows to anticipate the actual spreading as measured by official data on contagion collected by Health Care Agencies [10]. For instance the authors of [18] predict economical outcomes starting from social data, these predictions are not in the context of financial markets
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