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The Effect of Quantitative Easing through Google Metrics on US Stock Indices

Author

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  • Nikoletta Poutachidou

    (Department of Economics, University of Thessaly, 28th October Str. 78, 38333 Volos, Greece)

  • Stephanos Papadamou

    (Department of Economics, University of Thessaly, 28th October Str. 78, 38333 Volos, Greece
    Department of Social Science, Hellenic Open University, Parodos Aristotelous Str. 18, 26335 Patras, Greece)

Abstract

The purpose of this study is to investigate the fluctuations that occur in stock returns of US stock indices when there is an increase in the volume of Google internet searches for the phrase “quantitative easing” in the US. The exponential generalized autoregressive conditional heteroscedasticity model (EGARCH) was applied based on weekly data of stock indices using the three-factor model of Fama and French for the period of 1 January 2006 to 30 October 2020. The existence of a statistically significant relationship between searches and financial variables, especially in the stock market, is evident. The result is strong in three of the four stock indices studied. Specifically, the SVI index was statistically significant, with a positive trend for the S&P 500 and Dow Jones indices and a negative trend for the VIX index. Investor focus on quantitative easing (QE), as determined by Google metrics, seems to calm stock market volatility and increase stock returns. Although there is a large body of research using Google Trends as a crowdsourcing method of forecasting stock returns, this paper is the first to examine the relationship between the increase in internet searches of “quantitative easing” and stock market returns.

Suggested Citation

  • Nikoletta Poutachidou & Stephanos Papadamou, 2021. "The Effect of Quantitative Easing through Google Metrics on US Stock Indices," IJFS, MDPI, vol. 9(4), pages 1-19, October.
    • Handle: RePEc:gam:jijfss:v:9:y:2021:i:4:p:56-:d:649470
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    References listed on IDEAS

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    1. Nicholas Apergis, 2019. "The role of the expectations channel in the quantitative easing in the Eurozone," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 46(2), pages 372-382, March.
    2. Michael D. Bauer & Glenn D. Rudebusch, 2014. "The Signaling Channel for Federal Reserve Bond Purchases," International Journal of Central Banking, International Journal of Central Banking, vol. 10(3), pages 233-289, September.
    3. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
    4. Papadamou, Stephanos & Fassas, Athanasios P. & Kenourgios, Dimitris & Dimitriou, Dimitrios, 2021. "Flight-to-quality between global stock and bond markets in the COVID era," Finance Research Letters, Elsevier, vol. 38(C).
    5. 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.
    6. A. Gregoriou & A. Kontonikas & R. MacDonald & A. Montagnoli, 2009. "Monetary policy shocks and stock returns: evidence from the British market," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 23(4), pages 401-410, December.
    7. Richard Ajayi & Seyed Mehdian & Mark Perry, 2006. "A test of US equity market reaction to surprises in an era of high trading volume," Applied Financial Economics, Taylor & Francis Journals, vol. 16(6), pages 461-469.
    8. Athanasios P. Fassas & Stephanos Papadamou, 2018. "Unconventional monetary policy announcements and risk aversion: evidence from the U.S. and European equity markets," The European Journal of Finance, Taylor & Francis Journals, vol. 24(18), pages 1885-1901, December.
    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. Ioannidis, Christos & Kontonikas, Alexandros, 2008. "The impact of monetary policy on stock prices," Journal of Policy Modeling, Elsevier, vol. 30(1), pages 33-53.
    11. Papadamou, Stephanos & Siriopoulos, Costas, 2014. "Interest rate risk and the creation of the Monetary Policy Committee: Evidence from banks’ and life insurance companies’ stocks in the UK," Journal of Economics and Business, Elsevier, vol. 71(C), pages 45-67.
    12. Bijl, Laurens & Kringhaug, Glenn & Molnár, Peter & Sandvik, Eirik, 2016. "Google searches and stock returns," International Review of Financial Analysis, Elsevier, vol. 45(C), pages 150-156.
    13. Weale, Martin & Wieladek, Tomasz, 2016. "What are the macroeconomic effects of asset purchases?," Journal of Monetary Economics, Elsevier, vol. 79(C), pages 81-93.
    14. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    15. Stephanos Papadamou & Costas Siriopoulos & Nikolaos A. Kyriazis, 2020. "A survey of empirical findings on unconventional central bank policies," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 47(7), pages 1533-1577, April.
    16. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
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    2. Kittipob Saetia & Jiraphat Yokrattanasak, 2022. "Stock Movement Prediction Using Machine Learning Based on Technical Indicators and Google Trend Searches in Thailand," IJFS, MDPI, vol. 11(1), pages 1-21, December.

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