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Do Google Trend data contain more predictability than price returns?

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  • Damien Challet

    (MAS - Mathématiques Appliquées aux Systèmes - EA 4037 - Ecole Centrale Paris)

  • Ahmed Bel Hadj Ayed

Abstract

Using non-linear machine learning methods and a proper backtest procedure, we critically examine the claim that Google Trends can predict future price returns. We first review the many potential biases that may influence backtests with this kind of data positively, the choice of keywords being by far the greatest culprit. We then argue that the real question is whether such data contain more predictability than price returns themselves: our backtest yields a performance of about 17bps per week which only weakly depends on the kind of data on which predictors are based, i.e. either past price returns or Google Trends data, or both.

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  • Damien Challet & Ahmed Bel Hadj Ayed, 2015. "Do Google Trend data contain more predictability than price returns?," Post-Print hal-00960875, HAL.
    • Handle: RePEc:hal:journl:hal-00960875
    DOI: 10.21314/JOIS.2015.064
    Note: View the original document on HAL open archive server: https://hal.science/hal-00960875
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    References listed on IDEAS

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    1. Ryan Sullivan & Allan Timmermann & Halbert White, 1999. "Data‐Snooping, Technical Trading Rule Performance, and the Bootstrap," Journal of Finance, American Finance Association, vol. 54(5), pages 1647-1691, October.
    2. Huina Mao & Scott Counts & Johan Bollen, 2011. "Predicting Financial Markets: Comparing Survey, News, Twitter and Search Engine Data," Papers 1112.1051, arXiv.org.
    3. Ilaria Bordino & Stefano Battiston & Guido Caldarelli & Matthieu Cristelli & Antti Ukkonen & Ingmar Weber, 2012. "Web Search Queries Can Predict Stock Market Volumes," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-17, July.
    4. Jennifer L. Castle & Nicholas W.P. Fawcett & David F. Hendry, 2009. "Nowcasting Is Not Just Contemporaneous Forecasting," National Institute Economic Review, National Institute of Economic and Social Research, vol. 210(1), pages 71-89, October.
    5. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
    6. Ladislav Kristoufek, 2013. "Can Google Trends search queries contribute to risk diversification?," Papers 1310.1444, arXiv.org.
    7. Rechenthin, Michael & Street, W. Nick & Srinivasan, Padmini, 2013. "Stock chatter: Using stock sentiment to predict price direction," Algorithmic Finance, IOS Press, vol. 2(3-4), pages 169-196.
    8. repec:bla:jfinan:v:59:y:2004:i:3:p:1259-1294 is not listed on IDEAS
    9. Takeda, Fumiko & Wakao, Takumi, 2014. "Google search intensity and its relationship with returns and trading volume of Japanese stocks," Pacific-Basin Finance Journal, Elsevier, vol. 27(C), pages 1-18.
    10. Dietmar Janetzko, 2014. "Using Twitter to Model the EUR/USD Exchange Rate," Papers 1402.1624, arXiv.org.
    11. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
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    Cited by:

    1. Duarte Queirós, Sílvio M., 2016. "Trading volume in financial markets: An introductory review," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 24-37.
    2. Jacques Bughin, 2015. "Google searches and twitter mood: nowcasting telecom sales performance," Netnomics, Springer, vol. 16(1), pages 87-105, August.
    3. Chong, Terence Tai Leung & Li, Chen, 2020. "Search of Attention in Financial Market," MPRA Paper 99003, University Library of Munich, Germany.
    4. Dimitrios Vezeris & Themistoklis Kyrgos & Christos Schinas, 2018. "Take Profit and Stop Loss Trading Strategies Comparison in Combination with an MACD Trading System," JRFM, MDPI, vol. 11(3), pages 1-23, September.
    5. 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.

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