EconPapers    
Economics at your fingertips  
 

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
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed

Forthcoming in

Downloads: (external link)
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 (2022) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:mar:magkse:202130

Access Statistics for this paper

More papers in MAGKS Papers on Economics from Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung) Contact information at EDIRC.
Bibliographic data for series maintained by Bernd Hayo ().

 
Page updated 2024-02-20
Handle: RePEc:mar:magkse:202130