Is Bitcoin's Market Predictable? Analysis of Web Search and Social Media

In recent years, Internet has completely changed the way real life works. In particular, it has been possible to witness the online emergence of web 2.0 services that have been widely used as communication media. On one hand, services such as blogs, tweets, forums, chats, email have gained wide popularity. On the other hand, 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. 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 whether Bitcoin’s trading volume is related to the web search and social volumes about Bitcoin. We investigated whether public sentiment, expressed in large-scale collections of daily Twitter posts, can be used to predict the Bitcoin market too. We achieved significant cross correlation outcomes, demonstrating the search and social volumes power to anticipate trading volumes of Bitcoin currency.

[1]  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.

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

[3]  Ben Kirman,et al.  Exploring Twitter as a Game Platform; Strategies and Opportunities for Microblogging-based Games , 2015, CHI PLAY.

[4]  Lillian Lee,et al.  Opinion Mining and Sentiment Analysis , 2008, Found. Trends Inf. Retr..

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

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

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

[8]  Timothy W. Finin,et al.  Why we twitter: understanding microblogging usage and communities , 2007, WebKDD/SNA-KDD '07.

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

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

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

[12]  David Cornforth,et al.  Identifying the High-Value Social Audience from Twitter through Text-Mining Methods , 2015 .

[13]  Mike Thelwall,et al.  Sentiment in short strength detection informal text , 2010 .

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

[15]  Thomas Dimpfl,et al.  Can Internet Search Queries Help to Predict Stock Market Volatility? , 2016 .

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

[17]  Young Bin Kim,et al.  Virtual World Currency Value Fluctuation Prediction System Based on User Sentiment Analysis , 2015, PloS one.

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

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

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

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

[22]  Alessandro Vespignani,et al.  The Twitter of Babel: Mapping World Languages through Microblogging Platforms , 2012, PloS one.

[23]  C. Granger.  Investigating Causal Relations by Econometric Models and Cross-Spectral Methods , 1969 .

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

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

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

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

[28]  Ladislav Kristoufek,et al.  Power-law correlations in finance-related Google searches, and their cross-correlations with volatility and traded volume: Evidence from the Dow Jones Industrial components , 2015, 1502.00225.

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

[30]  Mike Thelwall,et al.  Sentiment in Twitter events , 2011, J. Assoc. Inf. Sci. Technol..

[31]  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.

[32]  Alessandro Vespignani,et al.  Beating the news using social media: the case study of American Idol , 2012, EPJ Data Science.

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

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