Quantifying Wikipedia Usage Patterns Before Stock Market Moves

Financial crises result from a catastrophic combination of actions. Vast stock market datasets offer us a window into some of the actions that have led to these crises. Here, we investigate whether data generated through Internet usage contain traces of attempts to gather information before trading decisions were taken. We present evidence in line with the intriguing suggestion that data on changes in how often financially related Wikipedia pages were viewed may have contained early signs of stock market moves. Our results suggest that online data may allow us to gain new insight into early information gathering stages of decision making.

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

[2]  B. Silverman,et al.  Density estimation in action , 1986 .

[3]  A. Tversky,et al.  Loss Aversion in Riskless Choice: A Reference-Dependent Model , 1991 .

[4]  R. Mantegna,et al.  Scaling behaviour in the dynamics of an economic index , 1995, Nature.

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

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

[7]  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.

[8]  N. Johnson,et al.  Financial market complexity , 2003 .

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

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

[11]  S. Sheather Density Estimation , 2004 .

[12]  J. Farmer,et al.  The Predictive Power of Zero Intelligence in Financial Markets , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[13]  Yoram Louzoun,et al.  Self-emergence of knowledge trees: extraction of the Wikipedia hierarchies. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[14]  G. Caldarelli,et al.  Preferential attachment in the growth of social networks, the Internet encyclopedia wikipedia , 2007 .

[15]  Hadley Wickham,et al.  ggplot2 - Elegant Graphics for Data Analysis (2nd Edition) , 2017 .

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

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

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

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

[20]  N. Askitas,et al.  Google Econometrics and Unemployment Forecasting , 2009, SSRN Electronic Journal.

[21]  Pedro M. Valero-Mora,et al.  ggplot2: Elegant Graphics for Data Analysis , 2010 .

[22]  David M. Pennock,et al.  Predicting consumer behavior with Web search , 2010, Proceedings of the National Academy of Sciences.

[23]  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.

[24]  E. Ben-Jacob,et al.  Index Cohesive Force Analysis Reveals That the US Market Became Prone to Systemic Collapses Since 2002 , 2011, PloS one.

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

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

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

[28]  Johan Bollen,et al.  Twitter mood predicts the stock market , 2010, J. Comput. Sci..

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

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

[31]  András Kornai,et al.  A Practical Approach to Language Complexity: A Wikipedia Case Study , 2012, PloS one.

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

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

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

[35]  Taha Yasseri,et al.  Circadian Patterns of Wikipedia Editorial Activity: A Demographic Analysis , 2011, PloS one.

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

[37]  H. Stanley,et al.  Linking agent-based models and stochastic models of financial markets , 2012, Proceedings of the National Academy of Sciences.

[38]  H. Stanley,et al.  Quantifying Trading Behavior in Financial Markets Using Google Trends , 2013, Scientific Reports.

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