Tobias Preis
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Tobias Preis
Tobias Preis
Tobias Preis.jpg
Born 1981
Residence Germany, Switzerland, United Kingdom, United States
Nationality German
Alma mater Johannes Gutenberg University of Mainz
Known for Identification of links between online behavior and real world economic events
Scientific career

Tobias Preis is Professor of Behavioral Science and Finance at Warwick Business School and a fellow of the Alan Turing Institute. He is a computational social scientist focussing on measuring and predicting human behavior with online data. At Warwick Business School he directs the Data Science Lab together with his colleague Suzy Moat. Preis holds visiting positions at Boston University and University College London. In 2011, he worked as a senior research fellow with H. Eugene Stanley at Boston University and with Dirk Helbing at ETH Zurich. In 2009, he was named a member of the Gutenberg Academy. In 2007, he founded Artemis Capital Asset Management GmbH, a proprietary trading firm which is based in Germany. He was awarded a Ph.D. in physics from the Johannes Gutenberg University of Mainz in Germany.

Preis has quantified and modelled financial market fluctuations.[1][2] In addition, he has made contributions to general-purpose computing on graphics processing units (GPGPU) in statistical physics[3][4] and computational finance.[5]

In 2010, Preis headed a research team which provided evidence that search engine query data and stock market fluctuations are correlated.[6][7][8][9][10] The team discovered a link between the number of Internet searches for company names and transaction volumes of the corresponding stocks on a weekly time scale.[11] In a TEDx talk,[12] Preis highlights the opportunities offered by studies of citizens' online behaviour to gain insights into socio and economic decision making.

In 2012, Preis used Google Trends data to demonstrate together with his colleagues Suzy Moat, H. Eugene Stanley and Steven R. Bishop that Internet users from countries with a higher per capita gross domestic product (GDP) are more likely to search for information about the future than information about the past. The findings, published in the journal Scientific Reports, suggest there may be a link between online behaviour and real-world economic indicators.[13][14][15][16] Preis and colleagues examined Google search queries made by Internet users in 45 different countries in 2010 and calculated the ratio of the volume of searches for the coming year (2011) to the volume of searches for the previous year (2009), which they call the Future Orientation Index. A comparison of the Future Orientation Index to the per capita GDP of each country revealed a strong tendency for countries in which Google users enquire more about the future to exhibit a higher GDP. Preis and colleagues conclude from this study that a relationship potentially exists between the economic success of a country and the information-seeking behaviour of its citizens online.[13][17][18][19][20][21]

In 2013, Preis and his colleagues Suzy Moat and H. Eugene Stanley introduced a method to identify online precursors for stock market moves, using trading strategies based on search volume data provided by Google Trends.[22] Their analysis of Google search volume for 98 terms of varying financial relevance, published in Scientific Reports,[23] suggests that increases in search volume for financially relevant search terms tend to precede large losses in financial markets.[24][25][26][27][28][29][30][31] Similarly, in a study also published in Scientific Reports in 2013,[32]Suzy Moat, Preis and colleagues demonstrated a link between changes in the number of views of Wikipedia articles relating to financial topics and subsequent large stock market moves.[33]

In 2015, Preis and his colleague Suzy Moat designed and delivered a massive open online course (MOOC) on big data. The course focuses on measuring and predicting human behavior.[34]

Preis is an academic editor of PLoS ONE.[35]

See also


  1. ^ Peer Teuwsen (August 15, 2011). "Es braucht ein neues Finanzsystem". Die Zeit. Retrieved 2012. 
  2. ^ Leonid Leiva (May 18, 2011). "Wie Finanzblasen platzen". Neue Zürcher Zeitung. Retrieved 2012. 
  3. ^ Block, Benjamin; Virnau, Peter; Preis, Tobias (2010). "Multi-GPU accelerated multi-spin Monte Carlo simulations of the 2D Ising model". Computer Physics Communications. 181: 1549-1556. arXiv:1007.3726Freely accessible. Bibcode:2010CoPhC.181.1549B. doi:10.1016/j.cpc.2010.05.005. 
  4. ^ Preis, Tobias; Virnau, Peter; Paul, Wolfgang; Schneider, Johannes J. (2009). "GPU accelerated Monte Carlo simulation of the 2D and 3D Ising model". Journal of Computational Physics. 228: 4468-4477. Bibcode:2009JCoPh.228.4468P. doi:10.1016/ 
  5. ^ Preis, Tobias; Virnau, Peter; Paul, Wolfgang; Schneider, Johannes J. (2009). "Accelerated fluctuation analysis by graphic cards and complex pattern formation in financial markets". New Journal of Physics. 11: 093024. Bibcode:2009NJPh...11i3024P. doi:10.1088/1367-2630/11/9/093024. 
  6. ^ Preis, Tobias; Reith, Daniel; Stanley, H. Eugene (2010). "Complex dynamics of our economic life on different scales: insights from search engine query data". Philosophical Transactions of the Royal Society A. 368: 5707-5719. Bibcode:2010RSPTA.368.5707P. doi:10.1098/rsta.2010.0284. PMID 21078644. 
  7. ^ Kevin Voigt (November 15, 2010). "Google searches predict stock market moves". CNN. Retrieved 2011. 
  8. ^ John Bohannon (November 14, 2010). "Can Google Predict the Stock Market?". Science. Retrieved 2011. 
  9. ^ Catherine Mayer (November 15, 2010). "Study: Are Google Searches Affecting the Stock Market?". Time Magazine. Retrieved 2011. 
  10. ^ Eva-Maria Magel (November 15, 2010). "Ökonophysiker untersucht Google". Frankfurter Allgemeine Zeitung. Retrieved 2012. 
  11. ^ Yvonne Esterházy (August 29, 2012). "Handelsstrategie: Wer sucht, hat Angst". Wirtschaftswoche. Retrieved 2012. 
  12. ^ "TEDxZurich". October 4, 2011. Retrieved 2012. 
  13. ^ a b Tobias Preis, Helen Susannah Moat, H. Eugene Stanley and Steven R. Bishop (2012). "Quantifying the Advantage of Looking Forward". Scientific Reports. 2: 350. Bibcode:2012NatSR...2E.350P. doi:10.1038/srep00350. PMC 3320057Freely accessible. PMID 22482034. 
  14. ^ Paul Marks (April 5, 2012). "Online searches for future linked to economic success". New Scientist. Retrieved 2012. 
  15. ^ Casey Johnston (April 6, 2012). "Google Trends reveals clues about the mentality of richer nations". Ars Technica. Retrieved 2012. 
  16. ^ Andrew Webster (April 6, 2012). "Wealthier countries are more interested in the future". The Verge. Retrieved 2012. 
  17. ^ Ami Sedghi (February 8, 2013). "Which countries are the most forward thinking? See it visualised". The Guardian. Retrieved 2013. 
  18. ^ Kate Bevan (February 7, 2013). "Germany knocks Britain off top spot to become world's most forward-thinking country in Google searches". Daily Mail. Retrieved 2013. 
  19. ^ Bernhard Warner (January 31, 2013). "What Google Searches About the Future Tell Us About the Present". Bloomberg Businessweek. Retrieved 2013. 
  20. ^ Caitlin Dewey (February 8, 2013). "Map: The world's most and least 'forward-looking' countries, based on Google searches". The Washington Post. Retrieved 2013. 
  21. ^ Alessandro Alviani (February 1, 2013). "Germania Felix: più ottimisti di noi". La Stampa. Retrieved 2013. 
  22. ^ Philip Ball (April 26, 2013). "Counting Google searches predicts market movements". Nature. Retrieved 2013. 
  23. ^ Tobias Preis, Helen Susannah Moat and H. Eugene Stanley (2013). "Quantifying Trading Behavior in Financial Markets Using Google Trends". Scientific Reports. 3: 1684. Bibcode:2013NatSR...3E1684P. doi:10.1038/srep01684. PMC 3635219Freely accessible. PMID 23619126. 
  24. ^ Nick Bilton (April 26, 2013). "Google Search Terms Can Predict Stock Market, Study Finds". New York Times. Retrieved 2013. 
  25. ^ Christopher Matthews (April 26, 2013). "Trouble With Your Investment Portfolio? Google It!". TIME Magazine. Retrieved 2013. 
  26. ^ Philip Ball (April 26, 2013). "Counting Google searches predicts market movements". Nature. Retrieved 2013. 
  27. ^ Bernhard Warner (April 25, 2013). "'Big Data' Researchers Turn to Google to Beat the Markets". Bloomberg Businessweek. Retrieved 2013. 
  28. ^ Hamish McRae (April 28, 2013). "Hamish McRae: Need a valuable handle on investor sentiment? Google it". The Independent. Retrieved 2013. 
  29. ^ Richard Waters (April 25, 2013). "Google search proves to be new word in stock market prediction". Financial Times. Retrieved 2013. 
  30. ^ David Leinweber (April 26, 2013). "Big Data Gets Bigger: Now Google Trends Can Predict The Market". Forbes. Retrieved 2013. 
  31. ^ Jason Palmer (April 25, 2013). "Google searches predict market moves". BBC. Retrieved 2013. 
  32. ^ Helen Susannah Moat, Chester Curme, Adam Avakian, Dror Y. Kenett, H. Eugene Stanley and Tobias Preis (2013). "Quantifying resource Usage Patterns Before Stock Market Moves". Scientific Reports. 3: 1801. Bibcode:2013NatSR...3E1801M. doi:10.1038/srep01801. PMC 3647164Freely accessible. 
  33. ^ "Wikipedia's crystal ball". Financial Times. May 10, 2013. Retrieved 2013. 
  34. ^ Moat and Preis (2015). "Big Data: Measuring and Predicting Human Behaviour". FutureLearn. Retrieved 2015. 
  35. ^ "PLOS ONE Editorial Board". Retrieved 2013. 

External links

  This article uses material from the Wikipedia page available here. It is released under the Creative Commons Attribution-Share-Alike License 3.0.



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