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Predicting financial markets with Google Trends and not so random keywords

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  • Damien Challet
  • Ahmed Bel Hadj Ayed

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," Papers 1307.4643, arXiv.org, revised Mar 2014.
  • Handle: RePEc:arx:papers:1307.4643
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    Cited by:

    1. Bijl, Laurens & Kringhaug, Glenn & Molnár, Peter & Sandvik, Eirik, 2016. "Google searches and stock returns," International Review of Financial Analysis, Elsevier, vol. 45(C), pages 150-156.
    2. David Garcia & Frank Schweitzer, 2015. "Social signals and algorithmic trading of Bitcoin," Papers 1506.01513, arXiv.org, revised Sep 2015.

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