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Sending firm messages: text mining letters from PRA supervisors to banks and building societies they regulate

Author

Listed:
  • Bholat, David

    (Bank of England)

  • Brookes, James

    (Bank of England)

  • Cai, Chris

    (Bank of England)

  • Grundy, Katy

    (Bank of England)

  • Lund, Jakob

    (Bank of England)

Abstract

Our paper analyses confidential letters sent from the Bank of England’s Prudential Regulation Authority (PRA) to banks and building societies it supervises. These letters are a ‘report card’ written to firms annually, and are arguably the most important, regularly recurring written communication sent from the PRA to firms it supervises. Using a mix of methods, including a machine learning algorithm called random forests, we explore whether the letters vary depending on the riskiness of the firm to whom the PRA is writing. We find that they do. We also look across the letters as a whole to draw out key topical trends and confirm that topics important on the post-crisis regulatory agenda such as liquidity and resolution appear frequently. And we look at how PRA letters differ from the letters written by the PRA’s predecessor, the Financial Services Authority. We find evidence that PRA letters are different, with a greater abundance of forward-looking language and directiveness, reflecting the shift in supervisory approach that has occurred in the United Kingdom following the financial crisis of 2007–09.

Suggested Citation

  • Bholat, David & Brookes, James & Cai, Chris & Grundy, Katy & Lund, Jakob, 2017. "Sending firm messages: text mining letters from PRA supervisors to banks and building societies they regulate," Bank of England working papers 688, Bank of England.
  • Handle: RePEc:boe:boeewp:0688
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    References listed on IDEAS

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    Cited by:

    1. Youngjoon Lee & Soohyon Kim & Ki Young Park, 2018. "Deciphering Monetary Policy Committee Minutes with Text Mining Approach: A Case of South Korea," Working papers 2018rwp-132, Yonsei University, Yonsei Economics Research Institute.
    2. Hurley, James & Karmakar, Sudipto & Markoska, Elena & Walczak, Eryk & Walker, Danny, 2021. "Impacts of the Covid-19 crisis: evidence from 2 million UK SMEs," Bank of England working papers 924, Bank of England.
    3. Joan Huang & John Simon, 2021. "Central Bank Communication: One Size Does Not Fit All," RBA Research Discussion Papers rdp2021-05, Reserve Bank of Australia.
    4. Arnould, Guillaume & Guin, Benjamin & Ongena, Steven & Siciliani, Paolo, 2020. "(When) do banks react to anticipated capital reliefs?," Bank of England working papers 889, Bank of England.
    5. Young Joon Lee & Soohyon Kim & Ki Young Park, 2019. "Deciphering Monetary Policy Board Minutes with Text Mining: The Case of South Korea," Korean Economic Review, Korean Economic Association, vol. 35, pages 471-511.
    6. Bholat, David & Broughton, Nida & Parker, Alice & Ter Meer, Janna & Walczak, Eryk, 2018. "Enhancing central bank communications with behavioural insights," Bank of England working papers 750, Bank of England.

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    More about this item

    Keywords

    Bank of England Prudential Regulation Authority; banking supervision; text mining; machine learning; random forests; Financial Services Authority; central bank communications;
    All these keywords.

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

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