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Can internet search queries help to predict stock market volatility?


  • Dimpfl, Thomas
  • Jank, Stephan


This paper studies the dynamics of stock market volatility and retail investor attention measured by internet search queries. We find a strong co-movement of stock market indices' realized volatility and the search queries for their names. Furthermore, Granger causality is bi-directional: high searches follow high volatility, and high volatility follows high searches. Using the latter feedback effect to predict volatility we find that search queries contain additional information about market volatility. They help to improve volatility forecasts in-sample and out-of-sample as well as for different forecasting horizons. Search queries are particularly useful to predict volatility in high-volatility phases.

Suggested Citation

  • Dimpfl, Thomas & Jank, Stephan, 2011. "Can internet search queries help to predict stock market volatility?," CFR Working Papers 11-15, University of Cologne, Centre for Financial Research (CFR).
  • Handle: RePEc:zbw:cfrwps:1115

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    1. repec:spr:elmark:v:27:y:2017:i:4:d:10.1007_s12525-016-0244-z is not listed on IDEAS
    2. Semen Son-Turan, 2016. "The Impact of Investor Sentiment on the "Leverage Effect"," International Econometric Review (IER), Econometric Research Association, vol. 8(1), pages 4-18, April.
    3. Byström, Hans, 2016. "Language, news and volatility," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 42(C), pages 139-154.
    4. Coble, David & Pincheira, Pablo, 2017. "Nowcasting Building Permits with Google Trends," MPRA Paper 76514, University Library of Munich, Germany.
    5. Amal Aouadi & Mohamed Arouri & Frédéric Teulon, 2014. "Investor Following and Volatility: A GARCH Approach," Working Papers 2014-286, Department of Research, Ipag Business School.
    6. Moussa, Faten & Delhoumi, Ezzeddine & Ouda, Olfa Ben, 2017. "Stock return and volatility reactions to information demand and supply," Research in International Business and Finance, Elsevier, vol. 39(PA), pages 54-67.
    7. Ana Brochado, 2016. "Investor attention and Portuguese stock market volatility: We’ll google it for you!," EcoMod2016 9345, EcoMod.
    8. Jukka Ruohonen & Sami Hyrynsalmi, 0. "Evaluating the use of internet search volumes for time series modeling of sales in the video game industry," Electronic Markets, Springer;IIM University of St. Gallen, vol. 0, pages 1-20.
    9. Bianconi, Marcelo & Hua, Xiaxin & Tan, Chih Ming, 2015. "Determinants of systemic risk and information dissemination," International Review of Economics & Finance, Elsevier, vol. 38(C), pages 352-368.
    10. repec:trp:01jefa:jefa0003 is not listed on IDEAS
    11. 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.
    12. Sun, Hang, 2016. "Crisis-Contingent Dynamics of Connectedness: An SVAR-Spatial-Network “Tripod” Model with Thresholds," Research Memorandum 032, Maastricht University, Graduate School of Business and Economics (GSBE).
    13. Levent Bulut, 2017. "Does Statistical Significance Help to Evaluate Predictive Performance of Competing Models?," Journal of Economics and Financial Analysis, Tripal Publishing House, vol. 1(1), pages 1-13.
    14. Matija Piv{s}korec & Nino Antulov-Fantulin & Petra Kralj Novak & Igor Mozetiv{c} & Miha Grv{c}ar & Irena Vodenska & Tomislav v{S}muc, 2014. "News Cohesiveness: an Indicator of Systemic Risk in Financial Markets," Papers 1402.3483,
    15. repec:gam:jecnmx:v:5:y:2017:i:3:p:35-:d:108901 is not listed on IDEAS
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    17. Latoeiro, Pedro & Ramos, Sofía B. & Veiga, Helena, 2013. "Predictability of stock market activity using Google search queries," DES - Working Papers. Statistics and Econometrics. WS ws130605, Universidad Carlos III de Madrid. Departamento de Estadística.
    18. repec:kap:apfinm:v:24:y:2017:i:3:d:10.1007_s10690-017-9228-z is not listed on IDEAS
    19. repec:ipg:wpaper:2014-405 is not listed on IDEAS
    20. Aouadi, Amal & Arouri, Mohamed & Teulon, Frédéric, 2013. "Investor attention and stock market activity: Evidence from France," Economic Modelling, Elsevier, vol. 35(C), pages 674-681.

    More about this item


    realized volatility; forecasting; investor behavior; noise trader; search engine data;

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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