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Quantifying Economic Behaviour Using Big Data

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Quantifying Economic Behaviour Using Big Data Quantifying Economic Behaviour Using Big Data Tobias Preis Warwick Business School [email protected] www.tobiaspreis.de A map of the world built only from GPS locations of Flickr photos The big data explosion 1 The advantage of looking forward Suzy Moat & Tobias Preis Future Orientation Index 2010 Based on Preis, Moat, Stanley and Bishop (2012) more Google searches for “2009” more Google searches for “2011” Suzy Moat & Tobias Preis Future Orientation Index 2012 Based on Preis, Moat, Stanley and Bishop (2012) 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 Ratio of Google searches for “2011” to searches for “2009” during 2010 for 45 countries 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 FutureRatio Orientation of Google searches for “2013” to searches Index for “2011” during 20122010 for 45 countries Richer countries look forward A B Preis, Moat, Stanley & 5 “2007” “2008” “2009” 4 Bishop (2012) USD] 0 4 10 3 2 5 “2009” “2010” “2011” Featured by: 1 Search Volume 0 2008 2009 2010 GDP / Capita [10 0.0 0.5 1.0 1.5 2.0 Time with Weekly Granularity Future-Orientation Index Photo: Perpetual Tourist 2 Predicting stock markets Financial markets: big data Photo: Perpetual Tourist The Internet: big data Moat et al. (2013); Hypothetical strategy Preis et al. (2013) number of Google searches for keyword week t Moat et al. (2013); Hypothetical strategy Preis et al. (2013) number of Google searches for keyword week t-3 t-2 t-1 t Moat et al. (2013); Hypothetical strategy Preis et al. (2013) number of Google searches for keyword Search volume decreased: BUY stock in week t+1 week t-3 t-2 t-1 t Moat et al. (2013); Hypothetical strategy Preis et al. (2013) number of Google searches for keyword Search volume decreased: BUY stock in week t+1 Search volume increased: SELL stock week in week t+1 t-3 t-2 t-1 t Example: “culture” Featured by: % proft 40 16 0 5 −40 2005 2007 2009 2011 Preis, Moat “culture” trading strategy & Stanley buy and hold strategy (2013) mean ± 1 sd of random strategies Example: “debt” Featured by: % proft 300 326 200 100 0 16 2005 2007 2009 2011 Preis, Moat “debt” trading strategy & Stanley buy and hold strategy (2013) mean ± 1 sd of random strategies How different keywords perform return (random strategy sds) 2 Random strategy mean + 2 sds 1 Random strategy mean + 1 sd 0 -1 “debt” “culture” Preis, Moat & Stanley (2013) How different keywords perform return (random strategy sds) 2 Random strategy mean + 2 sds 1 Random strategy mean + 1 sd 0 -1 “debt” “credit” “culture” “stocks” “train” “garden” Preis, Moat & Stanley (2013) How different keywords perform return (random strategy sds) 2 Random strategy mean + 2 sds 1 Random strategy mean + 1 sd 0 -1 Financial relevance Returns signifcantly correlated with indicator # occurrences in FT of fnancial relevance # hits on Google Preis, Moat & Stanley (2013) Wikipedia: Dow Jones companies Moat, Curme, 0.6 Wikipedia Views DJIA Companies Avakian, Kenett, Wikipedia Edits Stanley & Preis DJIA Companies (2013) 0.4 Featured by: Density Random 0.2 Strategy 0.0 −2 0 2 Return [Std. Dev. of Random Strategies] Views strategies proftable Wikipedia: Financial topics Moat, Curme, 1.00 Avakian, Kenett, Wikipedia Edits Stanley & Preis 0.75 Financial Topics Wikipedia Views (2013) Financial Topics 0.50 Featured by: Density Random 0.25 Strategy 0.00 −2 0 2 Return [Std. Dev. of Random Strategies] Views strategies proftable Wikipedia: Actors and flmmakers? Moat, Curme, 0.4 Avakian, Kenett, Random Wikipedia Views Stanley & Preis Strategy Actors & Filmmakers 0.3 (2013) 0.2 Featured by: Density 0.1 0.0 −2 0 2 Return [Std. Dev. of Random Strategies] Strategies NOT proftable What is searched for before falls? debt apple housing crisis housing orange tree Curme, Preis, Stanley & Moat (2014) debt What is searched for before falls? Cumulative Returns (%) -100 0 100 200 Random Strategy Politics I Business 55 groups of search terms Business and politics most related Curme, Preis, Stanley & Moat (2014) 3 Sensing disasters Flickr and Hurricane Sandy 0.10 A 0.08 0.06 Flickr: photos taken Landfall of Hurricane Sandy 0.04 Flickr Photos with Hurricane Related Tags 0.02 0.00 20 Oct 25 Oct 30 Oct 05 Nov 10 Nov 15 Nov 20 Nov Normalised Number of Photos PreisTime and Moat! (under review); B Landfall of Hurricane Sandy 1020 1000 Averaged Pressure in 980 US State New Jersey Air pressure 960 20 Oct 25 Oct 30 Oct 05 Nov 10 Nov 15 Nov 20 Nov Atmospheric Pressure [mbar] Time Preis, Moat, Bishop, Treleaven and Stanley (2013) How can open data help you? [email protected] Data from the Internet may help us measure and even predict human behaviour .
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