Finding temporal associative patterns of Web search with stock price movements for trading strategy design

Behavioral attention to stock markets in open source web search has attracted remarkable effort for financial analytics and applications in recent years. Differently from existing explorations at the overall market level, this paper investigates associative patterns that link search volume changes and stock price movements at individual stock level, so as to be able to support the design of flexible trading strategies. In doing so, a method (namely EATAPSP) is introduced as an extension to temporal associative pattern mining for time-series data, which can effectively discover the association between search volumes and stock prices in a temporal logic manner with the “after” predicate. Furthermore, the real world data on all China's A-share stocks from Shanghai Stock Exchange is used to test the method, revealing the effectiveness of the method. Moreover, with the discovered patterns, a new stock trading strategy (i.e., EATAPSP-Trading) is designed, which obtains superior cumulative returns and significantly outperforms other strategies.

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

[2]  Lei Gao,et al.  Investor attention and abnormal performance of timberland investments in the United States , 2013 .

[3]  Qiang Wei,et al.  A STUDY ON AFTER-TEMPORAL ASSOCIATION BETWEEN ONLINE SEARCH VOLUME AND STOCK PRICE WITH AN INTELLIGENT ATARII METHOD , 2014 .

[4]  R. Roll.  R-S1-2 , 1988 .

[5]  Jaideep Srivastava,et al.  Selecting the right interestingness measure for association patterns , 2002, KDD.

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

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

[8]  James F. Allen.  Maintaining knowledge about temporal intervals , 1983, CACM.

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

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

[11]  Kent D. Daniel,et al.  Investor Psychology in Capital Markets: Evidence and Policy Implications , 2001 .

[12]  Li Zhang,et al.  Discovering the Factors Affecting the Location Selection of FDI in China , 2005, Intelligent Data Mining.

[13]  Torsten Schmidt,et al.  A monthly consumption indicator for Germany based on Internet search query data , 2010 .

[14]  Giselle C. Guzman,et al.  Internet Search Behavior as an Economic Forecasting Tool: The Case of Inflation Expectations , 2011 .

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

[16]  Michał Dzieliński,et al.  National Centre of Competence in Research Financial Valuation and Risk Management Working Paper No . 638 Measuring Economic Uncertainty and its Impact on the Stock Market Michal Dzielinski , 2010 .

[17]  Torsten Schmidt,et al.  Forecasting Private Consumption: Survey-Based Indicators vs. Google Trends , 2009 .

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

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

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