Dictionary approaches are at the forefront of current techniques for quantifying central bank communication. This paper proposes embeddings - a language model trained using machine learning techniques - to locate words and documents in a multidimensional vector space. To accomplish this, we...

This paper proposes an econometric framework for nowcasting the monetary policy stance and decisions of the European Central Bank (ECB) exploiting the ow of conventional and textual data that become available between two consecutive press conferences. Decompositions of the updated nowcasts into...

The purpose of this research is to examine the impact of sentiment derived from news headlines on the direction of stock price changes. The study examines stocks listed on the WIG-banking sub-sector index on the Warsaw Stock Exchange. Two types of data were used: textual and market data. The...

In recent months, the world has changed faster than ever. It's not easy for communications managers to keep track of all these changes, not to mention advise their top management and adjust corporate communications accordingly. Therefore, a research team from Leipzig University and University of...

This work assesses whether certain indicators constructed from unstructured information published in newspapers contain useful information regarding dynamics of Argentina's country risk volatility, estimated from a GARCH(1,1) model. The analysis covers the period 1998-2019. One standard...

This paper proposes an econometric framework for nowcasting the monetary policy stance and decisions of the European Central Bank (ECB) exploiting the ow of conventional and textual data that become available between two consecutive press conferences. Decompositions of the updated nowcasts into...