Quantifying Trading Behavior in Financial Markets Using Google Trends

Crises in financial markets affect humans worldwide. Detailed market data on trading decisions reflect some of the complex human behavior that has led to these crises. We suggest that massive new data sources resulting from human interaction with the Internet may offer a new perspective on the behavior of market participants in periods of large market movements. By analyzing changes in Google query volumes for search terms related to finance, we find patterns that may be interpreted as “early warning signs” of stock market moves. Our results illustrate the potential that combining extensive behavioral data sets offers for a better understanding of collective human behavior.

[1]  M. Kendall.  A NEW MEASURE OF RANK CORRELATION , 1938 .

[2]  H. Simon,et al.  A Behavioral Model of Rational Choice , 1955 .

[3]  P. Krugman.  The Self Organizing Economy , 1996 .

[4]  Marcel Ausloos,et al.  Coherent and random sequences in financial fluctuations , 1997 .

[5]  M. Marchesi,et al.  Scaling and criticality in a stochastic multi-agent model of a financial market , 1999, Nature.

[6]  A. Shleifer,et al.  Inefficient Markets: An Introduction to Behavioral Finance , 2002 .

[7]  J. Bouchaud,et al.  Leverage Effect in Financial Markets , 2001 .

[8]  J. Bouchaud,et al.  Leverage effect in financial markets: the retarded volatility model. , 2001, Physical review letters.

[9]  R. Axtell.  Zipf Distribution of U.S. Firm Sizes , 2001, Science.

[10]  Armin Bunde,et al.  The science of disasters : climate disruptions, heart attacks, and market crashes , 2002 .

[11]  E. Fehr.  Behavioural science: The economics of impatience , 2002, Nature.

[12]  C. Hommes.  Modeling the stylized facts in finance through simple nonlinear adaptive systems , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[13]  Guido Caldarelli,et al.  Universal scaling relations in food webs , 2003, Nature.

[14]  V. Plerou,et al.  A theory of power-law distributions in financial market fluctuations , 2003, Nature.

[15]  F. Lillo,et al.  Econophysics: Master curve for price-impact function , 2003, Nature.

[16]  Yi Zhang,et al.  The Determinants of International Investment and Attention Allocation: Using Internet Search Query Data , 2007 .

[17]  Misako Takayasu,et al.  A mathematical definition of the financial bubbles and crashes , 2007 .

[18]  A. Vespignani,et al.  Economic Networks: The New Challenges , 2009, Science.

[19]  Jeremy Ginsberg,et al.  Detecting influenza epidemics using search engine query data , 2009, Nature.

[20]  H. Varian,et al.  Predicting the Present with Google Trends , 2009 .

[21]  Didier Sornette,et al.  The 2006–2008 oil bubble: Evidence of speculation, and prediction , 2009 .

[22]  A. Vespignani.  Predicting the Behavior of Techno-Social Systems , 2009, Science.

[23]  H. Stanley,et al.  Cross-correlations between volume change and price change , 2009, Proceedings of the National Academy of Sciences.

[24]  I. N. A. C. I. J. H. Fowler.  Book Review: Connected: The surprising power of our social networks and how they shape our lives. , 2009 .

[25]  Harry Eugene Stanley,et al.  Catastrophic cascade of failures in interdependent networks , 2009, Nature.

[26]  H Eugene Stanley,et al.  Complex dynamics of our economic life on different scales: insights from search engine query data , 2010, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[27]  R. May,et al.  Systemic risk in banking ecosystems , 2011, Nature.

[28]  Jukka-Pekka Onnela,et al.  Geographic Constraints on Social Network Groups , 2010, PloS one.

[29]  Tobias Preis,et al.  Econophysics — complex correlations and trend switchings in financial time series , 2011 .

[30]  G. King,et al.  Ensuring the Data-Rich Future of the Social Sciences , 2011, Science.

[31]  D. Sornette,et al.  Complexity clouds finance-risk models , 2011, Nature.

[32]  H. Stanley,et al.  Switching processes in financial markets , 2011, Proceedings of the National Academy of Sciences.

[33]  Matjaz Perc,et al.  Evolution of the most common English words and phrases over the centuries , 2012, Journal of The Royal Society Interface.

[34]  H. Varian,et al.  Predicting the Present with Google Trends , 2012 .

[35]  Harry Eugene Stanley,et al.  Languages cool as they expand: Allometric scaling and the decreasing need for new words , 2012, Scientific Reports.

[36]  Guido Caldarelli,et al.  Web Search Queries Can Predict Stock Market Volumes , 2011, PloS one.

[37]  D. Helbing,et al.  Quantifying the Behavior of Stock Correlations Under Market Stress , 2012, Scientific Reports.

[38]  Ricardo Llano-González.  Fowler, J. & Christakis, N. (2009). Connected: the surprising power of our social networks and how they shape our lives. New York: Little, Brown and Company. , 2012 .

[39]  H. Eugene Stanley,et al.  Quantifying the Advantage of Looking Forward , 2012, Scientific Reports.

[40]  Lada A. Adamic,et al.  Computational Social Science , 2009, Science.

[41]  S. Ferrari,et al.  Author contributions , 2021 .