Decrypting Financial Markets through E-Joint Attention Efforts: On-Line Adaptive Networks of Investors in Periods of Market Uncertainty

This paper looks at 800,000 messages on the Unicredit stock, exchanged by 7,500 investors in the Finanzaonline.com forum, between 2005 and 2012 and measured collective interpretations of stock market trends. We examined the correlation patterns between market uncertainty, bad news and investors' network structure by measuring the investors' communication patterns. Our results showed that the investors' network reacted to market trends in different ways: While less turbulent market phases implied less communication, higher market volatility generated more complex communication patterns. While the information content of messages was less technical in situations of uncertainty, bad news caused more informative messages only when market volatility was lower. This meant that bad news had a different impact on network behaviour, depending on market uncertainty. By measuring the investors' expertise, we found that their behaviour could help predict changes in daily stock returns. We also found that expert investors were more influential in communication processes during high volatility market phases, whereas they had less influence on the real-time forum's reaction after bad news. Our findings confirm the crucial role of e-communication platforms. However, they also show the need to reconsider the fragility of these collective intelligence systems when under external shocks.

[1]  Jonathan D. Cryer,et al.  Time Series Analysis , 1986 .

[2]  Roberto Casarin,et al.  Being on the Field When the Game Is Still Under Way. The Financial Press and Stock Markets in Times of Crisis , 2013, PloS one.

[3]  F. Bassetti,et al.  Beta-Product Dependent Pitman-Yor Processes for Bayesian Inference , 2013 .

[4]  Dirk Helbing,et al.  Globally networked risks and how to respond , 2013, Nature.

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

[6]  E. Esposito.  The structures of uncertainty: performativity and unpredictability in economic operations , 2013 .

[7]  L. McGoey.  Strategic unknowns: towards a sociology of ignorance , 2012 .

[8]  Jordi Duch,et al.  Tracking Traders' Understanding of the Market Using e-Communication Data , 2011, PloS one.

[9]  D. Stark,et al.  From dissonance to resonance: cognitive interdependence in quantitative finance , 2011 .

[10]  Brian Uzzi,et al.  Synchronicity, instant messaging, and performance among financial traders , 2011, Proceedings of the National Academy of Sciences.

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

[12]  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.

[13]  Carol Ward.  The news on noise , 2010 .

[14]  Michael W. Brandt,et al.  Financial Valuation and Risk Management Working Paper No . 421 When It Cannot Get Better or Worse : The Asymmetric Impact of Good and Bad News on Bond Returns in Expansions and Recessions , 2007 .

[15]  M. Tomasello.  Why We Cooperate , 2009 .

[16]  Fabio Canova,et al.  Estimating Multicountry VAR Models , 2009 .

[17]  Alex Preda.  Information, Knowledge and Economic Life , 2009 .

[18]  M. Tomasello,et al.  Humans Have Evolved Specialized Skills of Social Cognition: The Cultural Intelligence Hypothesis , 2007, Science.

[19]  Mark S. Ackerman,et al.  Expertise networks in online communities: structure and algorithms , 2007, WWW '07.

[20]  Daniel F. Waggoner,et al.  Methods for Inference in Large Multiple-Equation Markov-Switching Models , 2006 .

[21]  Sylvia Frühwirth-Schnatter,et al.  Finite Mixture and Markov Switching Models , 2006 .

[22]  S. Soroka.  Good News and Bad News: Asymmetric Responses to Economic Information , 2006, The Journal of Politics.

[23]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[24]  M. Tomasello,et al.  Understanding and sharing intentions: The origins of cultural cognition , 2005, Behavioral and Brain Sciences.

[25]  John R. Nofsinger.  Social Mood and Financial Economics , 2005 .

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

[27]  Arnold Zellner,et al.  "Bayesian and Non-Bayesian Methods for Combining Models and Forecasts with Applications to Forecasting International Growth Rates" , 2004 .

[28]  Peter M. Clarkson,et al.  Market Reaction to Takeover Rumour in Internet Discussion Sites , 2006 .

[29]  Werner Antweiler,et al.  Is All that Talk Just Noise? The Information Content of Internet Stock Message Boards , 2001 .

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

[31]  Fabio Canova,et al.  Forecasting and Turning Point Predictions in a Bayesian Panel VAR Model , 2001 .

[32]  C. Sims,et al.  Bayesian methods for dynamic multivariate models , 1998 .

[33]  Robert B. Litterman.  Forecasting with Bayesian Vector Autoregressions-Five Years of Experience , 1984 .

[34]  A. Tversky,et al.  Judgment under Uncertainty , 1982 .

[35]  A. Tversky,et al.  Judgment under Uncertainty: Heuristics and Biases , 1974, Science.