IDEAS home Printed from https://ideas.repec.org/a/gam/jijfss/v9y2021i4p56-d649470.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7072/9/4/56/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7072/9/4/56/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    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. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. Weale, Martin & Wieladek, Tomasz, 2016. "What are the macroeconomic effects of asset purchases?," Journal of Monetary Economics, Elsevier, vol. 79(C), pages 81-93.
    12. 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.
    13. 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.
    14. Vlastakis, Nikolaos & Markellos, Raphael N., 2012. "Information demand and stock market volatility," Journal of Banking & Finance, Elsevier, vol. 36(6), pages 1808-1821.
    15. Ioannidis, Christos & Kontonikas, Alexandros, 2008. "The impact of monetary policy on stock prices," Journal of Policy Modeling, Elsevier, vol. 30(1), pages 33-53.
    16. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.

    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. Papadamou, Stephanos & Fassas, Athanasios & Kenourgios, Dimitris & Dimitriou, Dimitrios, 2020. "Direct and Indirect Effects of COVID-19 Pandemic on Implied Stock Market Volatility: Evidence from Panel Data Analysis," MPRA Paper 100020, University Library of Munich, Germany.
    2. Tamgac, Unay, 2021. "Emerging market exchange rates during quantitative tapering: The effect of US and domestic news," Research in International Business and Finance, Elsevier, vol. 57(C).
    3. Kim, Neri & Lučivjanská, Katarína & Molnár, Peter & Villa, Roviel, 2019. "Google searches and stock market activity: Evidence from Norway," Finance Research Letters, Elsevier, vol. 28(C), pages 208-220.
    4. Smales, L.A., 2021. "Investor attention and global market returns during the COVID-19 crisis," International Review of Financial Analysis, Elsevier, vol. 73(C).
    5. Hervé, Fabrice & Zouaoui, Mohamed & Belvaux, Bertrand, 2019. "Noise traders and smart money: Evidence from online searches," Economic Modelling, Elsevier, vol. 83(C), pages 141-149.
    6. Eli Arditi & Eldad Yechiam & Gal Zahavi, 2015. "Association between Stock Market Gains and Losses and Google Searches," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-12, October.
    7. Qadan, Mahmoud & Nama, Hazar, 2018. "Investor sentiment and the price of oil," Energy Economics, Elsevier, vol. 69(C), pages 42-58.
    8. Hamid, Alain & Heiden, Moritz, 2015. "Forecasting volatility with empirical similarity and Google Trends," Journal of Economic Behavior & Organization, Elsevier, vol. 117(C), pages 62-81.
    9. Tariq Aziz & Valeed Ahmad Ansari, 2021. "How Does Google Search Affect the Stock Market? Evidence from Indian Companies," Vision, , vol. 25(2), pages 224-232, June.
    10. Fantazzini, Dean & Shangina, Tamara, 2019. "The importance of being informed: forecasting market risk measures for the Russian RTS index future using online data and implied volatility over two decades," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 55, pages 5-31.
    11. Hyoung-Goo Kang & Kyounghun Bae & Jung Ah Shin & Seongmin Jeon, 2021. "Will data on internet queries predict the performance in the marketplace: an empirical study on online searches and IPO stock returns," Electronic Commerce Research, Springer, vol. 21(1), pages 101-124, March.
    12. Vozlyublennaia, Nadia, 2014. "Investor attention, index performance, and return predictability," Journal of Banking & Finance, Elsevier, vol. 41(C), pages 17-35.
    13. Aouadi, Amal & Arouri, Mohamed & Roubaud, David, 2018. "Information demand and stock market liquidity: International evidence," Economic Modelling, Elsevier, vol. 70(C), pages 194-202.
    14. Sergiy Saydometov & Sanjiv Sabherwal & Ramya Rajajagadeesan Aroul, 2020. "Sentiment and its asymmetric effect on housing returns," Review of Financial Economics, John Wiley & Sons, vol. 38(4), pages 580-600, October.
    15. Konstantinos N. Konstantakis & Despoina Paraskeuopoulou & Panayotis G. Michaelides & Efthymios G. Tsionas, 2021. "Bank deposits and Google searches in a crisis economy: Bayesian non‐linear evidence for Greece (2009–2015)," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5408-5424, October.
    16. Tihana Škrinjarić, 2019. "Time Varying Spillovers between the Online Search Volume and Stock Returns: Case of CESEE Markets," IJFS, MDPI, vol. 7(4), pages 1-30, October.
    17. Qian Chen & Xiang Gao & Jianming Mo & Zhouling Xu, 2022. "Market Reaction to Local Attention around Earnings Announcements in China: Evidence from Internet Search Activity," IJFS, MDPI, vol. 10(4), pages 1-26, October.
    18. Jain, Anshul & Biswal, Pratap Chandra, 2019. "Does internet search interest for gold move the gold spot, stock and exchange rate markets? A study from India," Resources Policy, Elsevier, vol. 61(C), pages 501-507.
    19. Chang, Young Bong & Kwon, YoungOk, 2018. "Ambiguities in valuing information technology firms: Do internet searches help?," Journal of Business Research, Elsevier, vol. 92(C), pages 260-269.
    20. Sifat, Imtiaz Mohammad & Thaker, Hassanudin Mohd Thas, 2020. "Predictive power of web search behavior in five ASEAN stock markets," Research in International Business and Finance, Elsevier, vol. 52(C).

    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:gam:jijfss:v:9:y:2021:i:4:p:56-:d:649470. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.