[go: up one dir, main page]

IDEAS home Printed from https://ideas.repec.org/p/war/wpaper/2020-33.html
   My bibliography  Save this paper

The impact of the content of Federal Open Market Committee post-meeting statements on financial markets – text mining approach

Author

Listed:
  • Ewelina Osowska

    (Data Science Lab WNE UW, University of Warsaw)

  • Piotr Wójcik

    (Faculty of Economic Sciences, Data Science Lab WNE UW, University of Warsaw)

Abstract
This article examines the impact of FOMC statements on stock and foreign exchange markets with the use of text mining and modelling methods including linear and non-linear algorithms. Proposed methodology is based on calculating the FOMC statements’ tone called as sentiment and incorporate it as a potential predictor in the modelling process. Additionally, we incorporate the market surprise component as well as two financial indicators namely Purchasing Managers' Index and Consumer confidence index that gauge for corporate managers and retail customers assessment of the economic situation and potential fluctuations. Eight event windows around the event are considered: 60-minute and 20-minute windows before the event and also 15-minute, 20-minute, 25-minute, 30-minute, 60-minute and 120-minute windows after the event. Research has shown that given linear models the sentiment of FOMC statements does not generate a significant response in any of the analyzed event windows neither for the S&P 500 Index nor for the spot price on the EUR/USD currency pair. However, significant predictors occurred to be market shock in case of both S&P 500 Index and EUR/USD spot price, PMI in case of EUR/USD spot price and also CCI in case of EUR/USD spot price. Given non-linear models, the negative relation of statement’s sentiment score and the model prediction is observed for EUR/USD spot price.

Suggested Citation

  • Ewelina Osowska & Piotr Wójcik, 2020. "The impact of the content of Federal Open Market Committee post-meeting statements on financial markets – text mining approach," Working Papers 2020-33, Faculty of Economic Sciences, University of Warsaw.
  • Handle: RePEc:war:wpaper:2020-33
    as

    Download full text from publisher

    File URL: https://www.wne.uw.edu.pl/index.php/download_file/5882/
    File Function: First version, 2020
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. Kuttner, Kenneth N., 2001. "Monetary policy surprises and interest rates: Evidence from the Fed funds futures market," Journal of Monetary Economics, Elsevier, vol. 47(3), pages 523-544, June.
    3. Mira Farka & Adrian R. Fleissig, 2013. "The impact of FOMC statements on the volatility of asset prices," Applied Economics, Taylor & Francis Journals, vol. 45(10), pages 1287-1301, April.
    4. Panagiotis Mazis & Andrianos Tsekrekos, 2017. "Latent semantic analysis of the FOMC statements," Review of Accounting and Finance, Emerald Group Publishing Limited, vol. 16(2), pages 179-217, May.
    5. Sandra A. Cannon, 2015. "Sentiment of the FOMC: Unscripted," Economic Review, Federal Reserve Bank of Kansas City, issue Q IV, pages 5-31.
    6. Tim Loughran & Bill Mcdonald, 2016. "Textual Analysis in Accounting and Finance: A Survey," Journal of Accounting Research, Wiley Blackwell, vol. 54(4), pages 1187-1230, September.
    7. Anna Cieslak & Adair Morse & Annette Vissing‐Jorgensen, 2019. "Stock Returns over the FOMC Cycle," Journal of Finance, American Finance Association, vol. 74(5), pages 2201-2248, October.
    8. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hayo, Bernd & Henseler, Kai & Steffen Rapp, Marc & Zahner, Johannes, 2022. "Complexity of ECB communication and financial market trading," Journal of International Money and Finance, Elsevier, vol. 128(C).
    2. Gómez-Cram, Roberto & Grotteria, Marco, 2022. "Real-time price discovery via verbal communication: Method and application to Fedspeak," Journal of Financial Economics, Elsevier, vol. 143(3), pages 993-1025.
    3. Dossani, Asad, 2021. "Central bank tone and currency risk premia," Journal of International Money and Finance, Elsevier, vol. 117(C).
    4. Abdi, Farshid & Kormanyos, Emily & Pelizzon, Loriana & Getmansky, Mila & Simon, Zorka, 2021. "Market impact of government communication: The case of presidential tweets," SAFE Working Paper Series 314, Leibniz Institute for Financial Research SAFE, revised 2021.
    5. Andreas Neuhierl & Michael Weber & Michael Weber, 2017. "Monetary Momentum," CESifo Working Paper Series 6648, CESifo.
    6. Aeimit Lakdawala, 2019. "Decomposing the effects of monetary policy using an external instruments SVAR," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(6), pages 934-950, September.
    7. Lakdawala, Aeimit & Schaffer, Matthew, 2019. "Federal reserve private information and the stock market," Journal of Banking & Finance, Elsevier, vol. 106(C), pages 34-49.
    8. van Binsbergen, Jules H. & Diamond, William F. & Grotteria, Marco, 2022. "Risk-free interest rates," Journal of Financial Economics, Elsevier, vol. 143(1), pages 1-29.
    9. repec:spo:wpmain:info:hdl:2441/74362fq3f99s299n07e84dlcib is not listed on IDEAS
    10. Gardner, Ben & Scotti, Chiara & Vega, Clara, 2022. "Words speak as loudly as actions: Central bank communication and the response of equity prices to macroeconomic announcements," Journal of Econometrics, Elsevier, vol. 231(2), pages 387-409.
    11. Monaco, Eleonora & Murgia, Lucia Milena, 2023. "Retail attention and the FOMC equity premium," Finance Research Letters, Elsevier, vol. 53(C).
    12. Refet S. Gürkaynak & Jonathan H. Wright, 2013. "Identification and Inference Using Event Studies," Manchester School, University of Manchester, vol. 81, pages 48-65, September.
    13. Alvin Andhika Zulen & Okiriza Wibisono, 2019. "Measuring stakeholders’ expectations for the central bank’s policy rate," IFC Bulletins chapters, in: Bank for International Settlements (ed.), The use of big data analytics and artificial intelligence in central banking, volume 50, Bank for International Settlements.
    14. Andreas Neuhierl & Michael Weber, 2016. "Monetary Policy and the Stock Market: Time-Series Evidence," NBER Working Papers 22831, National Bureau of Economic Research, Inc.
    15. Kurov, Alexander & Olson, Eric & Zaynutdinova, Gulnara R., 2022. "When does the fed care about stock prices?," Journal of Banking & Finance, Elsevier, vol. 142(C).
    16. Dick van Dijk & Robin L. Lumsdaine & Michel van der Wel, 2014. "Market Set-Up in Advance of Federal Reserve Policy Decisions," NBER Working Papers 19814, National Bureau of Economic Research, Inc.
    17. Schmeling, Maik & Wagner, Christian, 2019. "Does Central Bank Tone Move Asset Prices?," CEPR Discussion Papers 13490, C.E.P.R. Discussion Papers.
    18. 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.
    19. David O. Lucca & Emanuel Moench, 2015. "The Pre-FOMC Announcement Drift," Journal of Finance, American Finance Association, vol. 70(1), pages 329-371, February.
    20. Bennani, Hamza, 2019. "Does People's Bank of China communication matter? Evidence from stock market reaction," Emerging Markets Review, Elsevier, vol. 40(C), pages 1-1.
    21. Adlai Fisher & Charles Martineau & Jinfei Sheng, 2022. "Macroeconomic Attention and Announcement Risk Premia," The Review of Financial Studies, Society for Financial Studies, vol. 35(11), pages 5057-5093.

    More about this item

    Keywords

    FOMC Statements; event arbitrage; sentiment analysis; financial markets prediction;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:war:wpaper:2020-33. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Marcin Bąba (email available below). General contact details of provider: https://edirc.repec.org/data/fesuwpl.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.