Employing Google Trends and Deep Learning in Forecasting Financial Market Turbulence

Abstract

In this paper we apply text mining methodologies on a set of 10,000 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.

Keywords:

• Forecasting
• Google Trends
• XGBOOST
• Deep Neural Networks
• Text mining

Acknowledgement

The views expressed in this paper are those of the authors and not necessarily those of Bank of Greece.

Conflict of interest

Authors declare that they have no conflict of interest.

Notes

1 The algorithm is implemented using the package “topicmodels” in R.

2 We employ the tidytext package in R in order to apply tf-idf analysis.

3 We employ the Boruta Package, provided by the R programming language.