In this paper we apply text mining methodologies on a set of 10000 Central Bank speeches to construct a financial dictionary based on which we use Google Trends indices to measure people’s interest in financial news. Particularly we investigate the relationship between these indices and financial market turbulence leveraging on Deep Learning techniques which are benchmarked against a variety of Machine Learning algorithms and traditional statistical techniques. Our main finding is that Google queries convey information able to predict future market turbulence in a short time period (one month) and that Deep Learning algorithms clearly outperform over benchmark techniques. Google Trends can provide useful input in the creation of crisis Early Warning Systems as social data are more responsive compared to official financial indicators which are usually available with a lag of several weeks or months. Thus such an Early Warning System (EWS) that is continuously updated with current social data can be a valuable tool for policymakers as it can immediately identify signs of whether a crisis is imminent or not.
Journal of Behavioral Finance 2022 vol. 23 issue 3 353-365