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Revealing the mood of economic agents based on search queries

Author

Listed:
  • Petrova, Diana

    (Russian Presidential Academy of National Economy and Public Administration. Moscow, Russian Federation)

  • Trunin, Pavel

    (Russian Presidential Academy of National Economy and Public Administration, Gaidar Institute for Economic Policy. Moscow, Russian Federation)

Abstract

Currently, user behavior on the Internet is becoming a key source of information about the market sentiments and the public mood. In this regard, it becomes relevant to study the usefulness of such indicators in modeling macroeconomic indicators. This article proposes an approach to assessment the mood of economic agents using Google Trends search queries. A key particularity of the article is the selection of keywords based on the analysis of RBC news from January 2010 to March 2020. The results showed that sentiments in financial and money markets based on a principal component analysis strongly correlated with the financial stress index of ACRA and Rosstat consumer confidence index, which confirms the possibility of use search queries in the development and analysis of economic policy. We use search query indices for the consumer confidence index forecasting with mixed data sampling (MIDAS). In out-of-sample forecasting, our results show that MIDAS which includes the indicator of sentiment in the money market gives the best forecast performance for the next quarter, and MIDAS with the sentiment in the financial markets has a high accuracy of forecasting the consumer confidence index for 3 quarters.

Suggested Citation

  • Petrova, Diana & Trunin, Pavel, 2020. "Revealing the mood of economic agents based on search queries," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 59, pages 71-87.
    • Handle: RePEc:ris:apltrx:0400
    as

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    References listed on IDEAS

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    1. Michał Chojnowski & Piotr Dybka, 2017. "Is Exchange Rate Moody? Forecasting Exchange Rate with Google Trends Data," Econometric Research in Finance, SGH Warsaw School of Economics, Collegium of Economic Analysis, vol. 2(1), pages 1-21, June.
    2. Nimark, Kristoffer P. & Pitschner, Stefan, 2019. "News media and delegated information choice," Journal of Economic Theory, Elsevier, vol. 181(C), pages 160-196.
    3. Levent Bulut, 2018. "Google Trends and the forecasting performance of exchange rate models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(3), pages 303-315, April.
    4. Fantazzini, Dean & Shakleina, Marina & Yuras, Natalia, 2018. "Big Data for computing social well-being indices of the Russian population," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 50, pages 43-66.
    5. Michael P. Clements & Ana Beatriz Galvao, 2009. "Forecasting US output growth using leading indicators: an appraisal using MIDAS models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(7), pages 1187-1206.
    6. Li, Xin & Ma, Jian & Wang, Shouyang & Zhang, Xun, 2015. "How does Google search affect trader positions and crude oil prices?," Economic Modelling, Elsevier, vol. 49(C), pages 162-171.
    7. Seabold,Skipper & Coppola,Andrea, 2015. "Nowcasting prices using Google trends : an application to Central America," Policy Research Working Paper Series 7398, The World Bank.
    8. Leif Anders Thorsrud, 2020. "Words are the New Numbers: A Newsy Coincident Index of the Business Cycle," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 393-409, April.
    9. Larsen, Vegard H. & Thorsrud, Leif A., 2019. "The value of news for economic developments," Journal of Econometrics, Elsevier, vol. 210(1), pages 203-218.
    10. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
    11. Smith, Geoffrey Peter, 2012. "Google Internet search activity and volatility prediction in the market for foreign currency," Finance Research Letters, Elsevier, vol. 9(2), pages 103-110.
    12. I. Goloshchapova & M. Andreev., 2017. "Measuring inflation expectations of the Russian population with the help of machine learning," VOPROSY ECONOMIKI, N.P. Redaktsiya zhurnala "Voprosy Economiki", vol. 6.
    13. Feuerriegel, Stefan & Gordon, Julius, 2019. "News-based forecasts of macroeconomic indicators: A semantic path model for interpretable predictions," European Journal of Operational Research, Elsevier, vol. 272(1), pages 162-175.
    14. Steven L. Scott & Hal R. Varian, 2015. "Bayesian Variable Selection for Nowcasting Economic Time Series," NBER Chapters, in: Economic Analysis of the Digital Economy, pages 119-135, National Bureau of Economic Research, Inc.
    15. Minchae Song & Kyung‐shik Shin, 2019. "Forecasting economic indicators using a consumer sentiment index: Survey‐based versus text‐based data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(6), pages 504-518, September.
    16. Ksenia Yakovleva, 2018. "Text Mining-based Economic Activity Estimation," Russian Journal of Money and Finance, Bank of Russia, vol. 77(4), pages 26-41, December.
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    More about this item

    Keywords

    search queries; Google Trend; topic modeling; text analysis; sentiment; forecasting;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C69 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Other
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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