Collective Attention and Stock Prices: Evidence from Google Trends Data on Standard and Poor's 100

Today´s connected world allows people to gather information in shorter intervals than ever before, widely monitored by massive online data sources. As a dramatic economic event, recent financial crisis increased public interest for large companies considerably. In this paper, we exploit this change in information gathering behavior by utilizing Google query volumes as a "bad news" indicator for each corporation listed in the Standard and Poor´s 100 index. Our results provide not only an investment strategy that gains particularly in times of financial turmoil and extensive losses by other market participants, but reveal new sectoral patterns between mass online behavior and (bearish) stock market movements. Based on collective attention shifts in search queries for individual companies, hence, these findings can help to identify early warning signs of financial systemic risk. However, our disaggregated data also illustrate the need for further efforts to understand the influence of collective attention shifts on financial behavior in times of regular market activities with less tremendous changes in search volumes.

[1]  Joachim Mathiesen,et al.  Excitable human dynamics driven by extrinsic events in massive communities , 2013, Proceedings of the National Academy of Sciences.

[2]  Ladislav Kristoufek,et al.  BitCoin meets Google Trends and Wikipedia: Quantifying the relationship between phenomena of the Internet era , 2013, Scientific Reports.

[3]  Derek Ruths,et al.  Classifying Political Orientation on Twitter: It's Not Easy! , 2013, ICWSM.

[4]  H. Eugene Stanley,et al.  Quantifying Wikipedia Usage Patterns Before Stock Market Moves , 2013, Scientific Reports.

[5]  Petra Kralj Novak,et al.  Cohesiveness in Financial News and its Relation to Market Volatility , 2014, Scientific Reports.

[6]  H Eugene Stanley,et al.  Quantifying the semantics of search behavior before stock market moves , 2014, Proceedings of the National Academy of Sciences.

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

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

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

[10]  Pavlin Mavrodiev,et al.  The digital traces of bubbles: feedback cycles between socio-economic signals in the Bitcoin economy , 2014, Journal of The Royal Society Interface.

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

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

[13]  George Sugihara,et al.  Complex systems: Ecology for bankers , 2008, Nature.

[14]  Ladislav Kristoufek,et al.  Can Google Trends search queries contribute to risk diversification? , 2013, Scientific Reports.

[15]  Robert M. May,et al.  Networks and webs in ecosystems and financial systems , 2013, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[16]  Roberto Casarin,et al.  Being on the Field When the Game Is Still Under Way. The Financial Press and Stock Markets in Times of Crisis , 2013, PloS one.

[17]  Frank Schweitzer,et al.  Economic Networks: What Do We Know and What Do We Need to Know? , 2009, Adv. Complex Syst..

[18]  Zeynep Tufekci,et al.  Big Questions for Social Media Big Data: Representativeness, Validity and Other Methodological Pitfalls , 2014, ICWSM.

[19]  Matthias Bank,et al.  Google search volume and its influence on liquidity and returns of German stocks , 2010 .

[20]  Tobias Preis,et al.  Quantifying the Relationship Between Financial News and the Stock Market , 2013, Scientific Reports.

[21]  Xue-Qi Cheng,et al.  Trading Network Predicts Stock Price , 2014, Scientific Reports.

[22]  Vygintas Gontis,et al.  Consentaneous Agent-Based and Stochastic Model of the Financial Markets , 2014, PloS one.

[23]  Drona Kandhai,et al.  Information dissipation as an early-warning signal for the Lehman Brothers collapse in financial time series , 2013, Scientific Reports.

[24]  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.

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

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

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

[28]  D. Ruths,et al.  Social media for large studies of behavior , 2014, Science.

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

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

[31]  H. Eugene Stanley,et al.  Identifying States of a Financial Market , 2012, Scientific Reports.

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

[33]  Ladislav Kristoufek,et al.  What Are the Main Drivers of the Bitcoin Price? Evidence from Wavelet Coherence Analysis , 2014, PloS one.

[34]  P. Ivanov,et al.  Impact of Stock Market Structure on Intertrade Time and Price Dynamics , 2005, PloS one.

[35]  Huawei Shen,et al.  Degree-Strength Correlation Reveals Anomalous Trading Behavior , 2012, PloS one.

[36]  Scott A. Golder,et al.  Diurnal and Seasonal Mood Vary with Work, Sleep, and Daylength Across Diverse Cultures , 2011 .

[37]  Raphael H. Heiberger,et al.  Stock network stability in times of crisis , 2014 .

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

[39]  J. V. Andersen,et al.  “Price-Quakes” Shaking the World's Stock Exchanges , 2011, PloS one.

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

[41]  D. Lazer,et al.  The Parable of Google Flu: Traps in Big Data Analysis , 2014, Science.