The use of open source internet to analysis and predict stock market trading volume

The objective of this paper is to evaluate the impact of information demand and supply on stock market trading volume. Few studies have demonstrated the role of Google search data in analyzing trading volume activity. In this study, we employ a proxy for information demand which is derived from weekly internet search volume. The latest is from Google Trends database, for 25 of the largest stocks traded on CAC40 index, between April 2007 and March 2014. We use news headlines as a proxy for information supply. We use Garch model to analyze and predict trading volume.

[1]  Paul Ryan,et al.  Are Economically Significant Stock Returns and Trading Volumes Driven by Firm-Specific News Releases? , 2004 .

[2]  P. Phillips.  Testing for a Unit Root in Time Series Regression , 1988 .

[3]  Peter D. Wysocki.  Cheap Talk on the Web: The Determinants of Postings on Stock Message Boards , 1998 .

[4]  Raphael N. Markellos,et al.  Information Demand and Stock Market Volatility , 2012 .

[5]  Mike Y. Chen,et al.  Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web , 2001 .

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

[7]  J. Tobin.  Estimation of Relationships for Limited Dependent Variables , 1958 .

[8]  Gilad Mishne,et al.  Finding high-quality content in social media , 2008, WSDM '08.

[9]  Nicholas Barberis,et al.  A Model of Investor Sentiment , 1997 .

[10]  Faten Moussa,et al.  Stock return and volatility reactions to information demand and supply , 2017 .

[11]  Jianfeng Guo,et al.  Oil Price Volatility and Oil-Related Events: An Internet Concern Study Perspective , 2015 .

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

[13]  Brad M. Barber,et al.  All that Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors , 2006 .

[14]  W. Bagehot.  The Only Game in Town , 1971 .

[15]  Theologos Dergiades,et al.  Tweets, Google trends, and sovereign spreads in the GIIPS , 2015 .

[16]  Johan Bollen,et al.  Happiness Is Assortative in Online Social Networks , 2011, Artificial Life.

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

[18]  Bruno S. Silvestre,et al.  Social Media? Get Serious! Understanding the Functional Building Blocks of Social Media , 2011 .

[19]  Xiong Xiong,et al.  Internet information arrival and volatility of SME PRICE INDEX , 2014 .

[20]  Robert F. Whitelaw,et al.  News or Noise? Internet Postings and Stock Prices , 2001 .

[21]  Nadia Vozlyublennaia,et al.  Investor attention, index performance, and return predictability , 2014 .

[22]  Xi Jin,et al.  The Impact of Interest Rate on Information Flow Interpretation: Evidence from ChiNext , 2013, ITQM.

[23]  Tarun Chordia,et al.  An Empirical Analysis of Stock and Bond Market Liquidity , 2001 .

[24]  Michael S. Drake,et al.  Investor Information Demand: Evidence from Google Searches Around Earnings Announcements , 2012 .

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

[26]  Jonathan M. Karpoff.  The Relation between Price Changes and Trading Volume: A Survey , 1987, Journal of Financial and Quantitative Analysis.