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Deciphering Federal Reserve Communication via Text Analysis of Alternative FOMC Statements

Author

Listed:
  • Taeyoung Doh
  • Dongho Song
  • Shu-Kuei X. Yang

Abstract

We apply a natural language processing algorithm to FOMC statements to construct a new measure of monetary policy stance, including the tone and novelty of a policy statement. We exploit cross-sectional variations across alternative FOMC statements to identify the tone (for example, dovish or hawkish), and contrast the current and previous FOMC statements released after Committee meetings to identify the novelty of the announcement. We then use high-frequency bond prices to compute the surprise component of the monetary policy stance. Our text-based estimates of monetary policy surprises are not sensitive to the choice of bond maturities used in estimation, are highly correlated with forward guidance shocks in the literature, and are associated with lower stock returns after unexpected policy tightening. The key advantage of our approach is that we are able to conduct a counterfactual policy evaluation by replacing the released statement with an alternative statement, allowing us to perform a more detailed investigation at the sentence and paragraph level.

Suggested Citation

  • Taeyoung Doh & Dongho Song & Shu-Kuei X. Yang, 2020. "Deciphering Federal Reserve Communication via Text Analysis of Alternative FOMC Statements," Research Working Paper RWP 20-14, Federal Reserve Bank of Kansas City.
  • Handle: RePEc:fip:fedkrw:88946
    DOI: 10.18651/RWP2020-14
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    References listed on IDEAS

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    1. Ben S. Bernanke & Kenneth N. Kuttner, 2005. "What Explains the Stock Market's Reaction to Federal Reserve Policy?," Journal of Finance, American Finance Association, vol. 60(3), pages 1221-1257, June.
    2. Lukas Hoesch & Barbara Rossi & Tatevik Sekhposyan, 2020. "Has the Information Channel of Monetary Policy Disappeared? Revisiting the Empirical Evidence," Working Paper Series 2020-08, Federal Reserve Bank of San Francisco.
    3. Swanson, Eric T., 2021. "Measuring the effects of federal reserve forward guidance and asset purchases on financial markets," Journal of Monetary Economics, Elsevier, vol. 118(C), pages 32-53.
    4. Miguel Acosta & Ellen E. Meade, 2015. "Hanging on Every Word : Semantic Analysis of the FOMC's Postmeeting Statement," FEDS Notes 2015-09-30, Board of Governors of the Federal Reserve System (U.S.).
    5. Giavazzi, Francesco & Iglhaut, Felix & Lemoli, Giacomo & Rubera, Gaia, 2020. "Terrorist Attacks, Cultural Incidents and the Vote for Radical Parties: Analyzing Text from Twitter," CEPR Discussion Papers 14455, C.E.P.R. Discussion Papers.
    6. Francesco Giavazzi & Felix Iglhaut & Giacomo Lemoli & Gaia Rubera, 2020. "Terrorist Attacks, Cultural Incidents and the Vote for Radical Parties: Analyzing Text from Twitter," NBER Working Papers 26825, National Bureau of Economic Research, Inc.
    7. Chunya Bu & John Rogers & Wenbin Wu, 2020. "Forward-Looking Monetary Policy and the Transmission of Conventional Monetary Policy Shocks," Finance and Economics Discussion Series 2020-014, Board of Governors of the Federal Reserve System (U.S.).
    8. Francesco Giavazzi & Felix Iglhaut & Giacomo Lemoli & Gaia Rubera, 2020. "Terrorist Attacks, Cultural Incidents and the Vote for Radical Parties: Analyzing Text from Twitter," Working Papers 659, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    9. Kurt G. Lunsford, 2020. "Policy Language and Information Effects in the Early Days of Federal Reserve Forward Guidance," American Economic Review, American Economic Association, vol. 110(9), pages 2899-2934, September.
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    Cited by:

    1. Donato Masciandaro & Davide Romelli & Gaia Rubera, 2021. "Monetary policy, Twitter and financial markets: evidence from social media traffic," BAFFI CAREFIN Working Papers 21160, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.

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    More about this item

    Keywords

    FOMC; Alternative FOMC statements; Counterfactual policy evaluation; Monetary policy stance; Text analysis; Natural language processing;
    All these keywords.

    JEL classification:

    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)
    • E40 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - General
    • E50 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - General
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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