Quantifying the effects of online bullishness on international financial markets

Computational methods to gauge investor sentiment from commonly used online data sources that rely on machine learning classifiers and lexicons have shown considerable promise, but suffer from measurement and classification errors. In our work, we develop a simple, direct and unambiguous indicator of online investor sentiment, which is based on Twitter updates and Google search queries. We examine the predictive power of this new investor bullishness indicator for international stock markets. Our results indicate several striking regularities. First, changes in Twitter bullishness predict changes in Google bullishness, indicating that Twitter information precedes Google queries. Second, Twitter and Google bullishness are positively correlated to investor sentiment and lead established investor sentiment surveys. The former, in particular, is a more powerful predictor of changes in sentiment in the stock market than the latter. Third, we observe that high Twitter bullishness predicts increases in stock returns, with these then returning to their fundamental values. We believe that our results may support the investor sentiment hypothesis in behavioural finance. JEL Classification: C1, C12

[1]  Olivia Sheng,et al.  Investigating Predictive Power of Stock Micro Blog Sentiment in Forecasting Future Stock Price Directional Movement , 2011, ICIS.

[2]  A. Tversky,et al.  Prospect theory: an analysis of decision under risk — Source link , 2007 .

[3]  The Usefulness of Consumer Confidence Indexes in the United States , 2002 .

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

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

[6]  Zhi Da,et al.  The Sum of All FEARS: Investor Sentiment and Asset Prices , 2013 .

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

[8]  R. Thaler,et al.  A Survey of Behavioral Finance , 2002 .

[9]  Eric Gilbert,et al.  Widespread Worry and the Stock Market , 2010, ICWSM.

[10]  L. Summers,et al.  Noise Trader Risk in Financial Markets , 1990, Journal of Political Economy.

[11]  Jeffrey C. Fuhrer,et al.  Does Consumer Sentiment Forecast Household Spending? If So, Why? , 1994 .

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

[13]  Tim Loughran,et al.  When is a Liability not a Liability? Textual Analysis, Dictionaries, and 10-Ks , 2010 .

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

[15]  Jeffrey Wurgler,et al.  Investor Sentiment in the Stock Market , 2011 .

[16]  Sofus A. Macskassy,et al.  More than Words: Quantifying Language to Measure Firms' Fundamentals the Authors Are Grateful for Assiduous Research Assistance from Jie Cao and Shuming Liu. We Appreciate Helpful Comments From , 2007 .

[17]  Navneet Kaur,et al.  Opinion mining and sentiment analysis , 2016, 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom).

[18]  E. Fama.  EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK* , 1970 .

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

[20]  L. Summers,et al.  Positive Feedback Investment Strategies and Destabilizing Rational Speculation , 1989 .

[21]  K. Shapiro.  The limits of attention : temporal constraints in human information processing , 2001 .

[22]  C. Osgood,et al.  The Pollyanna hypothesis. , 1969 .