Google Searches and Stock Volatility - Evidence from the Danish market

Abstract: In this thesis, we investigate the effect of online search activity approximated by Google searches on the volatility of the largest, most actively traded stocks listed on the Danish stock exchange. The interest in this relationship stems from an increasing number of retail investors entering the market in recent years. Driven by low-to-negative deposit rates, easily accessible online trading platforms with limited entry regulation, and most recently, the Covid-19 pandemic, we argue that online information seeking of “amateur investors” with little to no prior trading experience can be linked to the dynamics of return volatility. By conducting both statistical and regression analyses we investigate a dataset containing information on 27 Danish stocks for the years 2016-2021. Contrary to our expectations, we fail to establish a descriptive link between Google search activity and volatility at the market level, nor do we find predictive powers of Google searches on volatility patterns for our sample. Each result is somewhat controversial and lacks support in most of the established body of literature covering developed markets. We hypothesize that this can be explained by the methodology of which search data was obtained, leaving valuable knowledge for future investigators.