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A machine learning framework for automated analysis of central bank communication and media discourse. The case of Narodowy Bank Polski

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  • Krzysztof Rybinski

    (Vistula University and Synerise SA)

Abstract

This paper presents a supervised machine learning framework based on the dictionary and Wordscores models that allows to analyse interactions between the official central bank communication (policy statements) and media discourse (newspaper articles). It was tested on the case of Narodowy Bank Polski in the period 1998−2018, with 70 policy statements accompanying interest rate changes and 21,181 Rzeczpospolita daily articles mentioning the central bank. Using a new concept – correlation of policy lexical sentiment – we found that NBP can successfully affect media discourse when the press discusses topics that fall into the NBP mandate. We also documented that the focus of both RPP statements and press articles changed over time. In the validation procedure we showed that the biggest challenge in applying machine learning to detecting monetary policy inclination in press articles is the existence of many competing policy transmission channels.

Suggested Citation

  • Krzysztof Rybinski, 2019. "A machine learning framework for automated analysis of central bank communication and media discourse. The case of Narodowy Bank Polski," Bank i Kredyt, Narodowy Bank Polski, vol. 50(1), pages 1-20.
    • Handle: RePEc:nbp:nbpbik:v:50:y:2019:i:1:p:1-20
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    More about this item

    Keywords

    monetary policy; central bank communication; text mining; Wordscores; Narodowy Bank Polski;
    All these keywords.

    JEL classification:

    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • E5 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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