Whatever it takes to understand a central banker: Embedding their words using neural networks - Martin Baumgärtner; Johannes Zahner (Philipps-University Marburg, School of Business and Economics, 2021)
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 gather a text corpus that is unparalleled in size and diversity in the central bank communication literature, as well as introduce a novel approach to text quantification from computational linguistics. Utilizing this novel text corpus of over 23,000 documents from over 130 central banks we are able to provide high quality text-representations - embeddings - for central banks. Finally, we demonstrate the applicability of embeddings in this paper by several examples in the fields of monetary policy surprises, financial uncertainty, and gender bias.
Keywords: C45; C53; E52; Z13; 330; Word Embedding; Neural Network; Central Bank Communication; Natural Language Processing; Transfer Learning