Short-term prediction of bank deposit flows: do textual features matter?

Motivated by the successful usage of machine learning around computer science and its wide acceptance from the finance literature, we utilize monthly data spanning the period 2008–2018 for the Euro area peripheral countries, in order to embark on a two-fold mission. First, to construct short-term prediction models for bank deposit flows in the Euro area peripheral countries, employing machine learning techniques. Second, to examine whether textual features enhance the predictive ability of our models. From the variety of models tested, we find that Random Forest models including both textual features and macroeconomic variables outperform models including only macro factors or textual features. Monetary policy authorities or macroprudential regulators could adopt our approach to timely predict potential excessive bank deposit outflows and assess the resilience of the whole banking sector in the Euro area peripheral countries.