title: Quantifying the semantics of search behavior before stock market moves.
abstract: Technology is becoming deeply interwoven into the fabric of society. The Internet has become a central source of information for many people when making day-to-day decisions. Here, we present a method to mine the vast data Internet users create when searching for information online, to identify topics of interest before stock market moves. In an analysis of historic data from 2004 until 2012, we draw on records from the search engine Google and online encyclopedia Wikipedia as well as judgments from the service Amazon Mechanical Turk. We find evidence of links between Internet searches relating to politics or business and subsequent stock market moves. In particular, we find that an increase in search volume for these topics tends to precede stock market falls. We suggest that extensions of these analyses could offer insight into large-scale information flow before a range of real-world events.
authors: Center for Polymer Studies and Department of Physics, Boston University, Boston, MA 02215; andWarwick Business School, University of Warwick, Coventry CV4 7AL, United Kingdom ccurme@bu.edu hes@bu.edu.
authors: Chester
authors: C
authors: Curme
authors: Warwick Business School, University of Warwick, Coventry CV4 7AL, United Kingdom.
authors: Tobias
authors: T
authors: Preis
authors: Center for Polymer Studies and Department of Physics, Boston University, Boston, MA 02215; and ccurme@bu.edu hes@bu.edu.
authors: H Eugene
authors: HE
authors: Stanley
authors: Warwick Business School, University of Warwick, Coventry CV4 7AL, United Kingdom.
authors: Helen Susannah
authors: HS
authors: Moat
keywords: complex systems; computational social science; data science; financial markets; online data