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Predicting financial markets with Google Trends and not so random keywords

Author

Listed:
  • Damien Challet

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

  • Ahmed Bel Hadj Ayed

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

Abstract

We check the claims that data from Google Trends contain enough data to predict future financial index returns. We first discuss the many subtle (and less subtle) biases that may affect the backtest of a trading strategy, particularly when based on such data. Expectedly, the choice of keywords is crucial: by using an industry-grade backtesting system, we verify that random finance-related keywords do not to contain more exploitable predictive information than random keywords related to illnesses, classic cars and arcade games. We however show that other keywords applied on suitable assets yield robustly profitable strategies, thereby confirming the intuition of Preis et al. (2013)

Suggested Citation

  • Damien Challet & Ahmed Bel Hadj Ayed, 2013. "Predicting financial markets with Google Trends and not so random keywords," Working Papers hal-00851607, HAL.
    • Handle: RePEc:hal:wpaper:hal-00851607
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    Cited by:

    1. Cheraghali, Hamid & Høydal, Hannah & Lysebo, Caroline & Molnár, Peter, 2023. "Consumer attention and company performance: Evidence from luxury companies," Finance Research Letters, Elsevier, vol. 58(PA).
    2. Massimo Guidolin & Alexei G. Orlov & Manuela Pedio, 2018. "How good can heuristic-based forecasts be? A comparative performance of econometric and heuristic models for UK and US asset returns," Quantitative Finance, Taylor & Francis Journals, vol. 18(1), pages 139-169, January.
    3. Are Oust & Ole Martin Eidjord, 2020. "Can Google Search Data be Used as a Housing Bubble Indicator?," International Real Estate Review, Asian Real Estate Society, vol. 23(2), pages 893-934.
    4. David Garcia & Frank Schweitzer, 2015. "Social signals and algorithmic trading of Bitcoin," Papers 1506.01513, arXiv.org, revised Sep 2015.
    5. Swamy, Vighneswara & Dharani, M. & Takeda, Fumiko, 2019. "Investor attention and Google Search Volume Index: Evidence from an emerging market using quantile regression analysis," Research in International Business and Finance, Elsevier, vol. 50(C), pages 1-17.
    6. Are Oust & Ole Martin Eidjord, 2020. "Can Google Search Data be Used as a Housing Bubble Indicator?," International Real Estate Review, Global Social Science Institute, vol. 23(2), pages 267-308.
    7. Cheraghali, Hamid & Igeh, Sofia Aarstad & Lin, Kuan-Heng & Molnár, Peter & Wijerathne, Iddamalgodage, 2022. "Online attention and mutual fund performance: Evidence from Norway," Finance Research Letters, Elsevier, vol. 49(C).
    8. Ishani Chaudhuri & Parthajit Kayal, 2022. "Predicting Power of Ticker Search Volume in Indian Stock Market," Working Papers 2022-214, Madras School of Economics,Chennai,India.
    9. Muhammad Ali Nasir & Toan Luu Duc Huynh & Sang Phu Nguyen & Duy Duong, 2019. "Forecasting cryptocurrency returns and volume using search engines," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-13, December.

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