Abstract

http://ssrn.com/abstract=2310621
 


 



Predicting Financial Markets with Google Trends and Not so Random Keywords


Damien Challet


Ecole Centrale Paris - Laboratory of Mathematics Applied to Systems; Encelade Capital SA

Ahmed Bel Hadj Ayed


Ecole Centrale Paris - Laboratory of Mathematics Applied to Systems

August 14, 2013


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 back-test of a trading strategy, particularly when based on such data. Expectedly, the choice of keywords is crucial: by using an industry-grade back-testing 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)

Number of Pages in PDF File: 9

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Date posted: August 15, 2013  

Suggested Citation

Challet, Damien and Bel Hadj Ayed, Ahmed, Predicting Financial Markets with Google Trends and Not so Random Keywords (August 14, 2013). Available at SSRN: http://ssrn.com/abstract=2310621 or http://dx.doi.org/10.2139/ssrn.2310621

Contact Information

Damien Challet (Contact Author)
Ecole Centrale Paris - Laboratory of Mathematics Applied to Systems ( email )
Grande Voie des Vignes
Châtenay-Malabry CEDEX, 92290
France
Encelade Capital SA ( email )
Chemin du Bochet 8
Sulpice, 1025
Switzerland
Ahmed Bel Hadj Ayed
Ecole Centrale Paris - Laboratory of Mathematics Applied to Systems ( email )
Grande Voie des Vignes
Châtenay-Malabry, 92290
France
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