Whatever it takes to understand a central banker - Embedding their words using neural networks

Martin Baumgaertner () and Johannes Zahner ()
Additional contact information
Martin Baumgaertner: THM Business School
Johannes Zahner: Goethe University Frankfurt
Authors registered in the RePEc Author Service: Martin Baumgärtner

MAGKS Papers on Economics from Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung)

Abstract: 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 utilize 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. This allows us to provide high-quality central bank-specific textual representations and demonstrate their applicability by developing an index that tracks deviations in the Fed's communication towards inflationtargeting. Our findings indicate that these deviations in communication significantly impact monetary policy actions, substantiallyreducing the reaction towards inflation deviation in the US.

Keywords: Word Embedding; Neural Network; Central Bank Communication; Natural Language Processing; Transfer Learning (search for similar items in EconPapers)
JEL-codes: C45 C53 E52 Z13 (search for similar items in EconPapers)
Pages: 43 pages
Date: 2021
New Economics Papers: this item is included in nep-ban, nep-big, nep-cba, nep-cmp, nep-isf, nep-mac and nep-mon
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Citations: View citations in EconPapers (2)

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https://www.uni-marburg.de/en/fb02/research-groups ... 2021_baumgartner.pdf First 202130 (application/pdf)

Related works:
Working Paper: Whatever it takes to understand a central banker: Embedding their words using neural networks (2023)
Working Paper: Whatever it Takes to Understand a Central Banker – Embedding their Words Using Neural Networks (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:mar:magkse:202130

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