Google Searches and Stock Returns

We investigate whether data from Google Trends can be used to forecast stock returns. Previous studies have found that high Google search volumes predict high returns for the first one to two weeks, with subsequent price reversal. By using a more recent dataset that covers the period from 2008 to 2013 we find that high Google search volumes lead to negative returns. We also examine a trading strategy based on selling stocks with high Google search volumes and buying stocks with infrequent Google searches. This strategy is profitable when the transaction cost is not taken into account but is not profitable if we take into account transaction costs.

[1]  Michael J. Cooper.  Filter Rules Based on Price and Volume in Individual Security Overreaction , 1999 .

[2]  Fulvio Corsi,et al.  A Simple Approximate Long-Memory Model of Realized Volatility , 2008 .

[3]  Eleftherios Mylonakis,et al.  Google trends: a web-based tool for real-time surveillance of disease outbreaks. , 2009, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[4]  Damien Challet,et al.  Predicting Financial Markets with Google Trends and Not so Random Keywords , 2013, 1307.4643.

[5]  Zhi Da,et al.  In Search of Attention , 2009 .

[6]  J. Hausman.  Specification tests in econometrics , 1978 .

[7]  S. Satchell,et al.  Forecasting Volatility in Financial Markets : A Review , 2004 .

[8]  Sidney B. Wachtel.  Certain Observations on Seasonal Movements in Stock Prices , 1942 .

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

[10]  M. B. Wintoki,et al.  Forecasting Abnormal Stock Returns and Trading Volume Using Investor Sentiment: Evidence from Online Search ? , 2011 .

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

[12]  Paul C. Tetlock.  Giving Content to Investor Sentiment: The Role of Media in the Stock Market , 2005, The Journal of Finance.

[13]  Allaudeen Hameed,et al.  Volume and Autocovariances in Short-Horizon Individual Security Returns , 1994 .

[14]  Andrew Ang,et al.  Stock Return Predictability: Is it There? , 2001 .

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

[16]  L. Glosten,et al.  On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks , 1993 .

[17]  A. Flahault,et al.  More Diseases Tracked by Using Google Trends , 2009, Emerging infectious diseases.

[18]  A. Lo,et al.  Stock Market Prices Do Not Follow Random Walks: Evidence from a Simple Specification Test , 1987 .

[19]  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.

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

[21]  B. Malkiel.  The Ef cient Market Hypothesis and Its Critics , 2003 .

[22]  E. Fama.  The Behavior of Stock-Market Prices , 1965 .

[23]  A. Craig MacKinlay,et al.  Stock Market Prices Do Not Follow Random Walks: Evidence from a Simple Specification Test , 1988 .

[24]  Jiang Wang,et al.  Trading Volume and Serial Correlation in Stock Returns , 1992 .

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

[26]  Malcolm P. Baker,et al.  Investor Sentiment and the Cross-Section of Stock Returns , 2003 .