The predictor impact of Web search media on Bitcoin trading volumes

In the last decade, Web 2.0 services have been widely used as communication media. Due to the huge amount of available information, searching has become dominant in the use of Internet. Millions of users daily interact with search engines, producing valuable sources of interesting data regarding several aspects of the world. Search queries prove to be a useful source of information in financial applications, where the frequency of searches of terms related to the digital currency can be a good measure of interest in it. Bitcoin, a decentralized electronic currency, represents a radical change in financial systems, attracting a large number of users and a lot of media attention. In this work we studied the existing relationship between Bitcoin's trading volumes and the queries volumes of Google search engine. We achieved significant cross correlation values, demonstrating search volumes power to anticipate trading volumes of Bitcoin currency.

[1]  Efthymios Constantinides,et al.  Social Media: A New Frontier for Retailers? , 2008 .

[2]  Ben Shneiderman,et al.  Analyzing Social Media Networks with NodeXL: Insights from a Connected World , 2010 .

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

[4]  A. Kaplan,et al.  Users of the world, unite! The challenges and opportunities of Social Media , 2010 .

[5]  Michele Marchesi,et al.  Bitcoin Spread Prediction Using Social and Web Search Media , 2015, UMAP Workshops.

[6]  Anshul Mittal,et al.  Stock Prediction Using Twitter Sentiment Analysis , 2011 .

[7]  Daniel E. Rose,et al.  Understanding user goals in web search , 2004, WWW '04.

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

[9]  Peter A. Gloor,et al.  Nowcasting the Bitcoin Market with Twitter Signals , 2014, ArXiv.

[10]  Reuben Grinberg.  Bitcoin: An Innovative Alternative Digital Currency , 2011 .

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

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

[13]  T. Rao,et al.  Analyzing Stock Market Movements Using Twitter Sentiment Analysis , 2012, ASONAM 2012.

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

[15]  Adi Shamir,et al.  Quantitative Analysis of the Full Bitcoin Transaction Graph , 2013, Financial Cryptography.

[16]  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.

[17]  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.

[18]  Qing Bai,et al.  The Impacts of Social Media on Bitcoin Performance , 2016, ICIS.

[19]  C. Granger.  Investigating causal relations by econometric models and cross-spectral methods , 1969 .

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