Wikipedia

Tobias Preis

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 PhD in physics from the Johannes Gutenberg University of Mainz in Germany.

Tobias Preis
Born1981
NationalityGerman
Alma materJohannes Gutenberg University of Mainz
Known forIdentification of links between online behavior and real world economic events
Scientific career
Fields
Institutions

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]

Research

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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 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]

In 2013, Preis and his colleagues Moat and Stanley introduced a method to identify online precursors for stock market moves, using trading strategies based on search volume data provided by Google Trends.[21] Their analysis of Google search volume for 98 terms of varying financial relevance, published in Scientific Reports,[22] suggests that increases in search volume for financially relevant search terms tend to precede large losses in financial markets.[23][24][25][26][27][28][29][30] Similarly, in a study also published in Scientific Reports in 2013,[31] 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.[32]

In 2015, Preis and his colleague Moat designed and delivered a massive open online course (MOOC) on big data. The course focuses on measuring and predicting human behavior.[33]

Preis is an academic editor of PLoS ONE.[34]

See also

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Footnotes

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  1. ^ Peer Teuwsen (15 August 2011). "Es braucht ein neues Finanzsystem". Die Zeit. Retrieved 26 May 2012.
  2. ^ Leonid Leiva (18 May 2011). "Wie Finanzblasen platzen". Neue Zürcher Zeitung. Retrieved 26 May 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 (9): 1549–1556. arXiv:1007.3726. Bibcode:2010CoPhC.181.1549B. doi:10.1016/j.cpc.2010.05.005. S2CID 14828005.
  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 (12): 4468–4477. Bibcode:2009JCoPh.228.4468P. doi:10.1016/j.jcp.2009.03.018.
  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 (9): 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 (1933): 5707–5719. Bibcode:2010RSPTA.368.5707P. doi:10.1098/rsta.2010.0284. PMID 21078644. S2CID 11029736.
  7. ^ Kevin Voigt (15 November 2010). "Google searches predict stock market moves". CNN. Archived from the original on 15 November 2010. Retrieved 13 February 2011.
  8. ^ John Bohannon (14 November 2010). "Can Google Predict the Stock Market?". Science. Retrieved 13 February 2011.
  9. ^ Catherine Mayer (15 November 2010). "Study: Are Google Searches Affecting the Stock Market?". Time Magazine. Retrieved 13 February 2011.
  10. ^ Eva-Maria Magel (15 November 2010). "Ökonophysiker untersucht Google". Frankfurter Allgemeine Zeitung. Retrieved 26 May 2012.
  11. ^ Yvonne Esterházy (29 August 2012). "Handelsstrategie: Wer sucht, hat Angst". Wirtschaftswoche. Retrieved 29 August 2012.
  12. ^ "TEDxZurich". 4 October 2011. Retrieved 26 May 2012.
  13. ^ a b Preis, Tobias; Moat, Helen Susannah; Stanley, H. Eugene; Bishop, Steven R. (2012). "Quantifying the Advantage of Looking Forward". Scientific Reports. 2: 350. Bibcode:2012NatSR...2..350P. doi:10.1038/srep00350. PMC 3320057. PMID 22482034.
  14. ^ Paul Marks (5 April 2012). "Online searches for future linked to economic success". New Scientist. Retrieved 26 May 2012.
  15. ^ Casey Johnston (6 April 2012). "Google Trends reveals clues about the mentality of richer nations". Ars Technica. Retrieved 26 May 2012.
  16. ^ Andrew Webster (6 April 2012). "Wealthier countries are more interested in the future". The Verge. Retrieved 26 May 2012.
  17. ^ Ami Sedghi (8 February 2013). "Which countries are the most forward thinking? See it visualised". The Guardian. Retrieved 24 February 2013.
  18. ^ Bernhard Warner (31 January 2013). "What Google Searches About the Future Tell Us About the Present". Bloomberg Businessweek. Archived from the original on 3 February 2013. Retrieved 24 February 2013.
  19. ^ Caitlin Dewey (8 February 2013). "Map: The world's most and least 'forward-looking' countries, based on Google searches". The Washington Post. Retrieved 24 February 2013.
  20. ^ Alessandro Alviani (1 February 2013). "Germania Felix: più ottimisti di noi". La Stampa. Retrieved 24 February 2013.
  21. ^ Philip Ball (26 April 2013). "Counting Google searches predicts market movements". Nature. doi:10.1038/nature.2013.12879. S2CID 167357427. Retrieved 9 August 2013.
  22. ^ 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...3.1684P. doi:10.1038/srep01684. PMC 3635219. PMID 23619126.
  23. ^ Nick Bilton (26 April 2013). "Google Search Terms Can Predict Stock Market, Study Finds". New York Times. Retrieved 9 August 2013.
  24. ^ Christopher Matthews (26 April 2013). "Trouble With Your Investment Portfolio? Google It!". TIME Magazine. Retrieved 9 August 2013.
  25. ^ Philip Ball (26 April 2013). "Counting Google searches predicts market movements". Nature. doi:10.1038/nature.2013.12879. S2CID 167357427. Retrieved 9 August 2013.
  26. ^ Bernhard Warner (25 April 2013). "'Big Data' Researchers Turn to Google to Beat the Markets". Bloomberg Businessweek. Archived from the original on 26 April 2013. Retrieved 9 August 2013.
  27. ^ Hamish McRae (28 April 2013). "Hamish McRae: Need a valuable handle on investor sentiment? Google it". The Independent. Archived from the original on 21 June 2022. Retrieved 9 August 2013.
  28. ^ Richard Waters (25 April 2013). "Google search proves to be new word in stock market prediction". Financial Times. Retrieved 9 August 2013.
  29. ^ David Leinweber (26 April 2013). "Big Data Gets Bigger: Now Google Trends Can Predict The Market". Forbes. Retrieved 9 August 2013.
  30. ^ Jason Palmer (25 April 2013). "Google searches predict market moves". BBC. Retrieved 9 August 2013.
  31. ^ Moat, Helen Susannah; Curme, Chester; Avakian, Adam; Kenett, Dror Y.; Stanley, H. Eugene; Preis, Tobias (2013). "Quantifying Wikipedia Usage Patterns Before Stock Market Moves". Scientific Reports. 3: 1801. Bibcode:2013NatSR...3.1801M. doi:10.1038/srep01801. PMC 3647164.
  32. ^ "Wikipedia's crystal ball". Financial Times. 10 May 2013. Retrieved 9 August 2013.
  33. ^ Moat and Preis (2015). "Big Data: Measuring and Predicting Human Behaviour". FutureLearn. Retrieved 13 April 2015.
  34. ^ "PLOS ONE Editorial Board". Retrieved 24 February 2013.
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