Quantifying the Semantics of Search Behavior Before Stock Market Moves

Proceedings of the National Academy of Sciences 111, 11600-11605; DOI:10.1073/pnas.1324054111 (2014)

6 Pages Posted: 14 Aug 2014

See all articles by Chester Curme

Chester Curme

Boston University

Tobias Preis

Data Science Lab, Behavioural Science, Warwick Business School; The Alan Turing Institute

H. Eugene Stanley

Boston University - Center for Polymer Studies

Helen Susannah Moat

University College London - Department of Civil, Environmental and Geomatic Engineering; Boston University - Center for Polymer Studies

Date Written: August 12, 2014

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.

Keywords: Big Data, Financial Markets, Predictive Analytics, Search Volume, Data Mining, Google, Wikipedia, Computational Social Science, Digital Traces

JEL Classification: A10, B40, C10, C20, C22, C53, C90, D70, D79, D83, J10, J11, O40, O47

Suggested Citation

Curme, Chester and Preis, Tobias and Stanley, H. Eugene and Moat, Helen Susannah, Quantifying the Semantics of Search Behavior Before Stock Market Moves (August 12, 2014). Proceedings of the National Academy of Sciences 111, 11600-11605; DOI:10.1073/pnas.1324054111 (2014) . Available at SSRN: https://ssrn.com/abstract=2480274

Chester Curme

Boston University ( email )

595 Commonwealth Avenue
Boston, MA 02215
United States

Tobias Preis (Contact Author)

Data Science Lab, Behavioural Science, Warwick Business School ( email )

University of Warwick
Coventry, CV4 7AL
United Kingdom

HOME PAGE: http://www.tobiaspreis.com

The Alan Turing Institute ( email )

British Library, 96 Euston Road
London, NW12DB
United Kingdom

HOME PAGE: http://https://www.turing.ac.uk

H. Eugene Stanley

Boston University - Center for Polymer Studies ( email )

Boston, MA 02215
United States

Helen Susannah Moat

University College London - Department of Civil, Environmental and Geomatic Engineering ( email )

Gower Street
London, WC1E 6BT
United Kingdom

HOME PAGE: http://www.suzymoat.co.uk

Boston University - Center for Polymer Studies ( email )

590 Commonwealth Avenue
Boston, MA 02215
United States

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