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.

Chester Curme
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