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Monetary Policy on Twitter and its Effect on Asset Prices: Evidence from Computational Text Analysis

Author

Listed:
  • Jochen Lüdering

    (University of Giessen)

  • Peter Tillmann

    (University of Giessen)

Abstract

In this paper we dissect the public debate about the future course of monetary policy and trace the effects of selected topics of this discourse on U.S. asset prices. We focus on the “taper tantrum” episode in 2013, a period with large revisions in expectations about Fed policy. Based on a novel data set of 90,000 Twitter messages (“tweets”) covering the entire debate of Fed tapering on Twitter we use Latent Dirichlet Allocation, a computational text analysis tool to quantify the content of the discussion. Several estimated topic frequencies are then included in a VAR model to estimate the effects of topic shocks on asset prices. We find that the discussion about Fed policy on social media contains price-relevant information. Shocks to shares of “tantrum”-, “QE”- and “data”-related topics are shown to lead to significant asset price changes. We also show that the effects are mostly due to changes in the term premium of yields consistent with the portfolio balance channel of unconventional monetary policy.

Suggested Citation

  • 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).
    • Handle: RePEc:mar:magkse:201612
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    References listed on IDEAS

    as
    1. Lüdering Jochen & Winker Peter, 2016. "Forward or Backward Looking? The Economic Discourse and the Observed Reality," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 236(4), pages 483-515, August.
    2. Scott Hendry, 2012. "Central Bank Communication or the Media’s Interpretation: What Moves Markets?," Staff Working Papers 12-9, Bank of Canada.
    3. Joshua Aizenman & Mahir Binici & Michael M. Hutchison, 2016. "The Transmission of Federal Reserve Tapering News to Emerging Financial Markets," International Journal of Central Banking, International Journal of Central Banking, vol. 12(2), pages 317-356, June.
    4. Scott Hendry & Alison Madeley, 2010. "Text Mining and the Information Content of Bank of Canada Communications," Staff Working Papers 10-31, Bank of Canada.
    5. Stephen Hansen & Michael McMahon & Andrea Prat, 2018. "Transparency and Deliberation Within the FOMC: A Computational Linguistics Approach," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(2), pages 801-870.
    6. Alan S. Blinder & Michael Ehrmann & Marcel Fratzscher & Jakob De Haan & David-Jan Jansen, 2008. "Central Bank Communication and Monetary Policy: A Survey of Theory and Evidence," Journal of Economic Literature, American Economic Association, vol. 46(4), pages 910-945, December.
    7. Hansen, Stephen & McMahon, Michael, 2016. "Shocking language: Understanding the macroeconomic effects of central bank communication," Journal of International Economics, Elsevier, vol. 99(S1), pages 114-133.
    8. David Bholat & Stephen Hans & Pedro Santos & Cheryl Schonhardt-Bailey, 2015. "Text mining for central banks," Handbooks, Centre for Central Banking Studies, Bank of England, number 33, April.
    9. Annette Meinusch & Peter Tillmann, 2017. "Quantitative Easing and Tapering Uncertainty: Evidence from Twitter," International Journal of Central Banking, International Journal of Central Banking, vol. 13(4), pages 227-258, December.
    10. Vegard H. Larsen & Leif Anders Thorsrud, 2015. "The Value of News," Working Papers No 6/2015, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    11. repec:pri:cepsud:161blinder is not listed on IDEAS
    12. David O. Lucca & Francesco Trebbi, 2009. "Measuring Central Bank Communication: An Automated Approach with Application to FOMC Statements," NBER Working Papers 15367, National Bureau of Economic Research, Inc.
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    Cited by:

    1. Lino Wehrheim, 2019. "Economic history goes digital: topic modeling the Journal of Economic History," Cliometrica, Springer;Cliometric Society (Association Francaise de Cliométrie), vol. 13(1), pages 83-125, January.
    2. Hohberger, Stefan & Priftis, Romanos & Vogel, Lukas, 2019. "The macroeconomic effects of quantitative easing in the euro area: Evidence from an estimated DSGE model," Journal of Economic Dynamics and Control, Elsevier, vol. 108(C).
    3. Ulrich Fritsche & Johannes Puckelwald, 2018. "Deciphering Professional Forecasters’ Stories - Analyzing a Corpus of Textual Predictions for the German Economy," Macroeconomics and Finance Series 201804, University of Hamburg, Department of Socioeconomics.

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    More about this item

    Keywords

    Monetary Policy; Fed; Latent Dirichlet Allocation; Text Analysis; VAR;
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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