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
Date Written: August 12, 2014
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
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