Asset Prices and Google's Search Data

Abstract

This paper investigates the novel relationship of asset price determination via Google data and trading volume. To capture this relation, we construct a model and secondly estimate panel and time series regressions. We use weekly data from 2004 to 2010 for 30 international banks. Our study is the first which differentiate between Google’s search volume and Google’s search clicks. We find that asset prices are positively related to the growth rate of Google’s search, trading volume and the level of Google search clicks. Moreover, this finding is in line with our theoretical model. Secondly, we find that the absolute level of Google’s search volume and Google’s search clicks behave differently regarding asset price dynamics. Google’s search volume, which measures long-run searches, is negatively related to asset prices and Google’s search click is positively related. Thus, Google’s search data offer a new insight into two different aspects: On the one hand it is a natural way of measuring attention to assets, and on the other hand it provides a timely and unbiased measurement of asset price dynamics especially during turbulent times. We conclude that Google’s data contain important information for the identification of asset bubbles.