Google and Financial Markets: Can Google Trends describe and predict the dynamics of Norwegian stock market?
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- Master's theses (SV-HH) 
We investigate whether Google search volume index (SVI) can explain and predict trading activity at the Norwegian stock market (OSE). Our sample focuses on the companies listed on OSE’s tradeable index OBX. We use abnormal returns, trading volume, and volatility as measures of market activity. SVI were classified as a) search term, which Google uses to keep track of word-specific search queries; and b) business term, which Google uses to keep track of all search queries done in any language and classifies these queries together under one topic. The regression models we developed were two-fold: (1) a descriptive model that tests whether a relationship exists between each of the three indicators and SVI; and (2) a predictive model that tests the predictive power of SVI towards the three indicators. Our results show that both SVIs neither exhibit a significant relationship nor a predictive power on abnormal returns. However, both SVIs show a significant positive relationship and a predictive power on trading volume. Lastly, search activity only exhibits a predictive power with volatility. Therefore, Google searches can tell more about future trading activity than current trading activity.
Master's thesis in Finance