IDEAS home Printed from https://ideas.repec.org/p/hal/wpaper/hal-00851607.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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).
    3. 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.
    4. 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.
    5. 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).
    6. 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.
    7. David Garcia & Frank Schweitzer, 2015. "Social signals and algorithmic trading of Bitcoin," Papers 1506.01513, arXiv.org, revised Sep 2015.
    8. 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.
    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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:wpaper:hal-00851607. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.