To quantify changes in information gathering behavior, we use the relative change in search volume: Δn(t, Δt) = n(t) − N(t − 1, Δt) with N(t − 1, Δt) = (n(t − 1 ) + n(t − 2 ) + … + n(t − Δt))/Δt, where t is measured in units of weeks. The variability of Google Trends data across different dates of access is irrelevant for our results and it can be shown that the data are consistent with reported real world events (see Fig. For each search term, we therefore average over three realizations of its search volume time series, based on three independent data requests in consecutive weeks. We find that search volume data change slightly over time due to Google's extraction procedure. We use Google Trends to determine how many searches n(t – 1) have been carried out for a specific search term such as debt in week t – 1, where Google defines weeks as ending on a Sunday, relative to the total number of searches carried out on Google during that time. To uncover the relationship between the volume of search queries for a specific term and the overall direction of trader decisions, we analyze closing prices p(t) of the Dow Jones Industrial Average (DJIA) on the first trading day of week t.
We explain our strategy based on changes in search volume with reference to the term debt, a keyword with an obvious semantic connection to the most recent financial crisis and overall the term which performed best in our analyses. The set of terms used was therefore not arbitrarily chosen, as we intentionally introduced some financial bias. We included terms related to the concept of stock markets, with some terms suggested by the Google Sets service, a tool which identifies semantically related keywords. We analyze the performance of a set of 98 search terms. Our results suggest that, following this logic, during the period 2004 to 2011 Google Trends search query volumes for certain terms could have been used in the construction of profitable trading strategies. In such periods, investors may search for more information about the market, before eventually deciding to buy or sell. Our findings are consistent with the intriguing proposal that notable drops in the financial market are preceded by periods of investor concern. Here, we suggest that within the time period we investigate, Google Trends data did not only reflect the current state of the stock markets 33 but may have also been able to anticipate certain future trends. A very recent study has shown that Internet users from countries with a higher per capita GDP are more likely to search for information about years in the future than years in the past 36. Choi and Varian 35 have shown that data from Google Trends can be linked to current values of various economic indicators, including automobile sales, unemployment claims, travel destination planning and consumer confidence. This demonstration of a link between stock market transaction volume and search volume has also been replicated using Yahoo! data 34. Further studies exploiting the temporal dimension of Google Trends data have demonstrated that changes in query volumes for selected search terms mirror changes in current numbers of influenza cases 32 and current volumes of stock market transactions 33. In the present study, we investigate the intriguing possibility of analyzing search query data from Google Trends to provide new insights into the information gathering process that precedes the trading decisions recorded in the stock market data.Ī recent investigation has shown that the number of clicks on search results stemming from a given country correlates with the amount of investment in that country 31. Recently, the search engine Google has begun to provide access to aggregated information on the volume of queries for different search terms and how these volumes change over time, via the publicly available service Google Trends. In today's world, information gathering often consists of searching online sources. According to Herbert Simon, actors begin their decision making processes by attempting to gather information 30. For example, a range of recent studies have focused on modeling financial markets 20, 21, 22, 23, 24, 25 and on performing network analyses 26, 27, 28, 29.Īt their core, financial trading data sets reflect the myriad of decisions taken by market participants. Movements in the markets exert immense impacts on personal fortunes and geopolitical events, generating considerable scientific attention to this subject 10, 11, 12, 13, 14, 15, 16, 17, 18, 19. Financial markets are a prime target for such quantitative investigations 8, 9. The increasing volumes of ‘big data’ reflecting various aspects of our everyday activities represent a vital new opportunity for scientists to address fundamental questions about the complex world we inhabit 1, 2, 3, 4, 5, 6, 7.
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