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Investor's sentiment in predicting the Effective Federal Funds Rate

Author

Listed:
  • Artem Meshcheryakov

    (San Jose State University)

  • Stoyu I Ivanov

    (San Jose State University)

Abstract

In this article we study if investor's sentiment measured by an intensity of Google searches may be used to predict future changes of the Effective Federal Funds rate. We find that online searches for “fed funds rate†, “fed interest rate†, “fed reserve†, “fed reserve rate†and “federal interest rate†are associated with next week decrease of the Effective Federal Funds Rate. Google searches for “fed rate hike†and “fed raise rates†are associated with next week increase of the Effective Federal Funds Rate even after we control for a number of macroeconomic indicators. We also find that intensity of Google searches is associated with the future decrease of volatility of the Effective Federal Funds rate. This finding can be explained by the reduction of information asymmetry about future changes that leads to a reduced volatility.

Suggested Citation

  • Artem Meshcheryakov & Stoyu I Ivanov, 2017. "Investor's sentiment in predicting the Effective Federal Funds Rate," Economics Bulletin, AccessEcon, vol. 37(4), pages 2767-2796.
  • Handle: RePEc:ebl:ecbull:eb-16-00751
    as

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    File URL: http://www.accessecon.com/Pubs/EB/2017/Volume37/EB-17-V37-I4-P248.pdf
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    References listed on IDEAS

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    1. Nikolaos Askitas & Klaus F. Zimmermann, 2009. "Google Econometrics and Unemployment Forecasting," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, vol. 55(2), pages 107-120.
    2. Sarno, Lucio & Thornton, Daniel L & Valente, Giorgio, 2005. "Federal Funds Rate Prediction," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 449-471, June.
    3. D’Amuri, Francesco & Marcucci, Juri, 2017. "The predictive power of Google searches in forecasting US unemployment," International Journal of Forecasting, Elsevier, vol. 33(4), pages 801-816.
    4. Joel T. Krueger & Kenneth N. Kuttner, 1996. "The Fed funds futures rate as a predictor of federal reserve policy," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 16(8), pages 865-879, December.
    5. Clarida, Richard & Gali, Jordi & Gertler, Mark, 1998. "Monetary policy rules in practice Some international evidence," European Economic Review, Elsevier, vol. 42(6), pages 1033-1067, June.
    6. 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.).
    7. Richard H. Clarida & Jordi Gali & Mark Gertler, 1998. "Monetary policy rules in practice," Proceedings, Federal Reserve Bank of San Francisco, issue Mar.
    8. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    9. Vlastakis, Nikolaos & Markellos, Raphael N., 2012. "Information demand and stock market volatility," Journal of Banking & Finance, Elsevier, vol. 36(6), pages 1808-1821.
    10. Michael S. Drake & Darren T. Roulstone & Jacob R. Thornock, 2012. "Investor Information Demand: Evidence from Google Searches Around Earnings Announcements," Journal of Accounting Research, Wiley Blackwell, vol. 50(4), pages 1001-1040, September.
    11. Hailiang Chen & Prabuddha De & Yu (Jeffrey) Hu & Byoung-Hyoun Hwang, 2014. "Wisdom of Crowds: The Value of Stock Opinions Transmitted Through Social Media," Review of Financial Studies, Society for Financial Studies, vol. 27(5), pages 1367-1403.
    12. Joseph, Kissan & Babajide Wintoki, M. & Zhang, Zelin, 2011. "Forecasting abnormal stock returns and trading volume using investor sentiment: Evidence from online search," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1116-1127, October.
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    Cited by:

    1. Lee, Chien-Chiang & Chen, Mei-Ping, 2021. "The effects of investor attention and policy uncertainties on cross-border country exchange-traded fund returns," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 830-852.

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    More about this item

    Keywords

    effective federal funds rate; google search; internet search; investor attention; online search; federal reserve; federal rate; federal funds rate; investor; sentiment; anticipation; forecast; prediction;
    All these keywords.

    JEL classification:

    • E4 - Macroeconomics and Monetary Economics - - Money and Interest Rates
    • E5 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit

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