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Text mining for central banks

Author

Listed:
  • David Bholat
  • Stephen Hans
  • Pedro Santos
  • Cheryl Schonhardt-Bailey

Abstract

Although often applied in other social sciences, text mining has been less frequently used in economics and in policy circles, particularly inside central banks. This Handbook is a brief introduction to the field, discussing how text mining is useful for addressing research topics of interest to central banks, and providing a step-by-step primer on how to mine text, including an overview of unsupervised and supervised techniques.

Suggested Citation

  • David Bholat & Stephen Hans & Pedro Santos & Cheryl Schonhardt-Bailey, 2015. "Text mining for central banks," Handbooks, Centre for Central Banking Studies, Bank of England, edition 1, number 33.
  • Handle: RePEc:ccb:hbooks:33
    as

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    File URL: http://www.bankofengland.co.uk/education/ccbs/handbooks/pdf/ccbshb33.pdf
    File Function: English version
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    References listed on IDEAS

    as
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    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Chakraborty, Chiranjit & Joseph, Andreas, 2017. "Machine learning at central banks," Bank of England working papers 674, Bank of England.
    2. Paola Cerchiello & Giancarlo Nicola, 2017. "Assessing News Contagion in Finance," DEM Working Papers Series 139, University of Pavia, Department of Economics and Management.
    3. Paola Cerchiello & Giancarlo Nicola & Samuel Rönnqvist & Peter Sarlin, 2017. "Deep Learning Bank Distress from News and Numerical Financial Data," DEM Working Papers Series 140, University of Pavia, Department of Economics and Management.
    4. Oleksiy Kryvtsov & Luba Petersen, 2019. "Central Bank Communication That Works: Lessons from Lab Experiments," Staff Working Papers 19-21, Bank of Canada.
    5. Vegard Høghaug Larsen & Leif Anders Thorsrud, 2018. "Business cycle narratives," Working Papers No 6/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    6. Jochen Lüdering & Peter Tillmann, 2016. "Monetary Policy on Twitter and its Effect on Asset Prices: Evidence from Computational Text Analysis," MAGKS Papers on Economics 201612, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    7. Flood, M. D. & Jagadish, H. V. & Raschid, L., 2016. "Big data challenges and opportunities in financial stability monitoring," Financial Stability Review, Banque de France, issue 20, pages 129-142, April.
    8. Schmeling, Maik & Wagner, Christian, 2019. "Does Central Bank Tone Move Asset Prices?," CEPR Discussion Papers 13490, C.E.P.R. Discussion Papers.
    9. 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.
    10. Pongsak Luangaram & Warapong Wongwachara, 2017. "More Than Words: A Textual Analysis of Monetary Policy Communication," PIER Discussion Papers 54, Puey Ungphakorn Institute for Economic Research, revised Feb 2017.
    11. repec:eee:dyncon:v:100:y:2019:i:c:p:230-250 is not listed on IDEAS
    12. Philip ME Garboden, 2019. "Sources and Types of Big Data for Macroeconomic Forecasting," Working Papers 2019-3, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    13. Leif Anders Thorsrud, 2016. "Nowcasting using news topics Big Data versus big bank," Working Papers No 6/2016, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    14. Sturm, Silke, 2019. "Political Competition: How to Measure Party Strategy in Direct Voter Communication using Social Media Data?," Hamburg Discussion Papers in International Economics 1, University of Hamburg, Chair of International Economics.
    15. Acosta, Miguel, 2015. "FOMC Responses to Calls for Transparency," Finance and Economics Discussion Series 2015-60, Board of Governors of the Federal Reserve System (US).
    16. 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.
    17. repec:kap:iaecre:v:25:y:2019:i:1:d:10.1007_s11294-019-09721-y is not listed on IDEAS
    18. Samuel Ronnqvist & Peter Sarlin, 2016. "Bank distress in the news: Describing events through deep learning," Papers 1603.05670, arXiv.org, revised Dec 2016.
    19. Ki Young Park & Youngjoon Lee & Soohyon Kim, 2019. "Deciphering Monetary Policy Board Minutes through Text Mining Approach: The Case of Korea," Working Papers 2019-1, Economic Research Institute, Bank of Korea.
    20. Luis E. Arango & Javier Pantoja & Carlos Velásquez, 2017. "Effects of the central bank’s communications in Colombia. A content analysis," Borradores de Economia 1024, Banco de la Republica de Colombia.
    21. repec:gam:jecnmx:v:6:y:2018:i:1:p:5-:d:130110 is not listed on IDEAS
    22. Angrick, Stefan & Naoyuki, Yoshino, 2018. "From window guidance to interbank rates : Tracing the transition of monetary policy in Japan and China," BOFIT Discussion Papers 4/2018, Bank of Finland, Institute for Economies in Transition.

    More about this item

    Keywords

    Text mining for central banks;

    JEL classification:

    • A12 - General Economics and Teaching - - General Economics - - - Relation of Economics to Other Disciplines
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design

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