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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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    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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