Text mining letters from financial regulators to firms they supervise.
Our article uses text mining techniques to examine confidential letters sent from the Bank of England's Prudential Regulation Authority (PRA) to financial institutions it supervises. These letters are a 'report card' written to firms annually, and are the most important, regularly recurring written...
| Published in: | Digital Scholarship in the Humanities Vol. 35; no. 4; pp. 776 - 797 |
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| Main Authors: | , |
| Format: | Article |
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Oxford University Press / USA
Dec2020
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=146804842&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 146804842 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 2055768X JEO9 jtl: Digital Scholarship in the Humanities issn: 2055768X maglogo: N pubinfo: dt: Dec2020 vid: 35 iid: 4 pid: 622 pub: Oxford University Press / USA artinfo: ui: 146804842 10.1093/llc/fqz063 ppf: 776 ppct: 21 formats: fmt: – @attributes: type: T – @attributes: type: P size: 626KB tig: atl: Text mining letters from financial regulators to firms they supervise. aug: au: Bholat, David Brookes, James affil: Advanced Analytics , Bank of England, UK su: Bank of England. Prudential Regulation Authority Financial Services Authority (Great Britain) Random forest algorithms Written communication Report cards Letters Machine learning Governors (Machinery) sug: subj: Bank of England. Prudential Regulation Authority Financial Services Authority (Great Britain) Random forest algorithms Written communication Report cards Letters Machine learning Governors (Machinery) ab: Our article uses text mining techniques to examine confidential letters sent from the Bank of England's Prudential Regulation Authority (PRA) to financial institutions it supervises. These letters are a 'report card' written to firms annually, and are the most important, regularly recurring written communication sent from the PRA to firms it supervises. Using two complementary machine learning techniques—random forests and logistic ridge regression—we explore whether the letters vary in substance and style depending on the size and importance of the firm to whom the PRA is writing. We find that letters to high impact firms use more evaluative, judgment-based language, and adopt a more forward-looking perspective. We also examine how PRA letters differ from similarly purposed letters written by its predecessor, the Financial Services Authority. We find evidence that PRA letters are different, with a greater degree of forward-looking language and directiveness, reflecting the shift in supervisory approach that has occurred in the UK following the financial crisis of 2007–09. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: © 2019 EADH: The European Association for Digital Humanities. item: Digital Scholarship in the Humanities holder: Oxford University Press / USA dt: @attributes: year: 2020 holdings: @attributes: islocal: N |
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