University of Warwick
Wednesday 11th June 2014, 2:35pm
TWITTER | @chestercurme
TITLE | Quantifying the semantics of search behavior before stock market moves
ABSTRACT | The Internet has become a central source of information for many people when making day-to-day decisions. Internet search data, such as from Google Trends, offer the intriguing possibility of measuring the information-gathering processes that precede real-world events. We present a method to mine the vast data Internet users create when searching for information online, in order to identify topics of interest before stock market moves. Crucially, we highlight the utility of categorizing keywords into semantic groups when analyzing these search data. In an analysis of historic data from 2004 until 2012, we find evidence of links between Internet searches relating to politics or finance 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.
BIOGRAPHY | Chester Curme is a Ph.D. candidate in the physics department at Boston University, and is currently a research fellow in data science at Warwick Business School. He received his B.A. in physics and mathematics from Middlebury College in 2012, where he graduated with Highest Honors. His research interests lie in interdisciplinary applications of statistical physics to problems in economics, computational social science, and network science. His work has been featured in both TV appearances and articles throughout the worldwide press, including the Financial Times, Washington Post, Wired, Daily Mail, Welt Am Sonntag, El País, Neue Zürcher Zeitung and Forbes. He has reviewed for the journals PLOS ONE and Physica A.