Quantifying the Semantics of Search Behavior Before Stock Market Moves

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: 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