Staff Directory

Dr Tobias Preis

Associate Professor of Behavioural Science & Finance

Research Interests

Technology is becoming deeply interwoven into the fabric of society. Preis's current research activities are driven by a deep interest in understanding the complex behaviour of financial systems by exploiting big data resources, with the hope that such understanding could contribute to the development of reliable and stable financial systems.

His expertise covers a wide range of subjects including computational social science, big data analytics, predictive analytics, data mining, complex systems, network science, and management science.

Together with Suzy Moat and Mark Carrigan, he chaired Europe's first Computational Social Science Conference at Warwick Business School.

See also his Google Scholar Profile for a list of citations and statistics.

Teaching in 2014-2015

Research Students

  • IB9ES0: Advanced Communication Skills for Data Science Research

Executive MBA

  • IB9AP0: Behavioural Sciences for the Manager


  • IB9CS0: Big Data Analytics

MSc Business

  • IB9CSB: Big Data Analytics

  • IM9030: Complexity in the Social Sciences

Research Students

  • IB9ER0: Designing and Managing Data Science Research

Behavioural Science Group

Email:  /  Tel: 024 765 28422


Tobias Preis is an Associate Professor of Behavioural Science and Finance at Warwick Business School. His recent research has aimed to carry out large scale experiments on complex social and economic systems by exploiting the volumes of data being generated by our interactions with technology.

In 2010, Preis headed a research team which provided evidence that search engine query data and stock market fluctuations are correlated. In 2012, Preis and his colleagues Helen Susannah Moat, H. Eugene Stanley and Steven R. Bishop used Google Trends data to demonstrate that Internet users from countries with a higher per capita GDP are more likely to search for information about the future than information about the past.

Preis received his Ph.D. in Theoretical Physics from the Johannes Gutenberg University of Mainz in 2010 and draws on an interdisciplinary background in physics, economics, and computer science. He has authored more than 30 scientific publications, published a book about the physics of financial markets and acts as a reviewer for more than 15 leading international journals. Preis serves as an Academic Editor of the multidisciplinary journal PLoS ONE.

Preis advises government agencies as well as private companies on potential exploitation of online digital traces. More information can be found on his personal website

Recent media coverage of his research includes:

  • Financial Times, UK (2013)
        "Google search proves to be new word in stock market prediction"
  • BBC, UK (2013)
        "Google searches predict market moves"
  • Nature, UK (2013)
        "Counting Google searches predicts market movements"
  • New York Times, USA (2013)
        "Google Search Terms Can Predict Stock Market, Study Finds"
  • Fox Business, USA (2013)
        "Can Google Predict the Stock Market?"
  • Guardian, UK (2013)
        "Which countries are the most forward thinking? See it visualised"
  • Bloomberg Businessweek, USA (2013)
        "What Google Searches About the Future Tell Us About the Present"
  • New Scientist, UK (2012)
        "Online searches for future linked to economic success"
  • Time Magazine, USA (2010)
        "Study: Are Google Searches Affecting the Stock Market?"
  • Science, USA (2010)
        "Can Google Predict the Stock Market?"

  • Recent Publications [all...]

    Journal Articles

    Book Chapters

    • Preis, T. and Moat, H. S.. "Early Signs of Financial Market Moves Reflected by Google Searches"
      Social Phenomena: From Data Analysis to Models (2015): 89-102.
    • Moat, H.S., Curme, C., Stanley, H.E., Preis, T.. "Anticipating Stock Market Movements with Google and Wikipedia"
      Nonlinear Phenomena in Complex Systems: From Nano to Macro Scale (2014): 47-59.
    • Preis, T.. "Price-Time Priority and Pro Rata Matching in an Order Book Model of Financial Markets"
      Econophysics of Order-driven Markets (2011): 65-72.

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    What's the big deal about big data?: The potential of Big Data to help identify links between data from the real and online world is immense according to Suzy Moat and Tobias Preis of Warwick Business School.

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