• Media type: E-Book
  • Title: Predicting Financial Markets with Google Trends and Not so Random Keywords
  • Contributor: Challet, Damien [Author]; Bel Hadj Ayed, Ahmed [Other]
  • Published: [S.l.]: SSRN, [2013]
  • Extent: 1 Online-Ressource (9 p)
  • Language: English
  • DOI: 10.2139/ssrn.2310621
  • Identifier:
  • Origination:
  • Footnote: Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments August 14, 2013 erstellt
  • Description: 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)
  • Access State: Open Access