Market sentiment dispersion and its effects on stock return and volatility
- 697 Downloads
Behavioral economics has revealed that investor sentiment can profoundly affect individual behavior and decision-making. Recently, the question is no longer whether investor sentiment affects stock market valuation, but how to directly measure investor sentiment and quantify its effects. Before the era of big data, research uses proxies as a mediator to indirectly measure investor sentiment, which has proved elusive due to insufficient data points. In addition, most of extant sentiment analysis studies focus on institutional investors instead of individual investors. This is despite the fact that United States individual investors have been holding around 50% of the stock market in direct stock investments. In order to overcome difficulties in measuring sentiment and endorse the importance of individual investors, we examine the role of individual sentiment dispersion in stock market. In particular, we investigate whether sentiment dispersion contains information about future stock returns and realized volatility. Leveraging on development of big data and recent advances in data and text mining techniques, we capture 1,170,414 data points from Twitter and used a text mining method to extract sentiment and applied both linear regression and Support Vector Regression; found that individual sentiment dispersion contains information about stock realized volatility, and can be used to increase the prediction accuracy. We expect our results contribute to extant theories of electronic market financial behavior by directly measuring the individual sentiment dispersion; raising a new perspective to assess the impact of investor opinion on stock market; and recommending a supplementary investing approach using user-generated content.
KeywordsInvestor sentiment Text mining Return and volatility predictability
JEL ClassificationC55 C53 C52
- Almgren, R. (2009). High frequency volatility. New York University.Google Scholar
- Bing, L., Chan, K. C., & Ou, C. (2014). Public sentiment analysis in twitter data for prediction of a company's stock price movements. In e-business engineering (ICEBE), 2014 I.E. 11th International Conference on (pp. 232-239). IEEE.Google Scholar
- Corsi, F. (2005). Measuring and modelling realized volatility: From tick-by-tick to long memory (Doctoral dissertation, University of Lugano).Google Scholar
- De Long, J. B., Shleifer, A., Summers, L. H., & Waldmann, R. J. (1990). Noise trader risk in financial markets. Journal of Political Economy, 703–738.Google Scholar
- Drucker, H., Burges, C. J., Kaufman, L., Smola, A., & Vapnik, V. (1997). Support vector regression machines. Advances in neural information processing systems, 9, 155–161.Google Scholar
- Hornik, K., & Grün, B. (2011). Topicmodels: An R package for fitting topic models. Journal of Statistical Software, 40(13), 1–30.Google Scholar
- Hsiao, C. (2014). Analysis of panel data, 3rd edn. Econometric Society monographs 54. Cambridge University Press.Google Scholar
- Keynes, J. M. (1936). The general theory of employment, interest and money. London: Macmillan.Google Scholar
- Kotsiantis, S., Kanellopoulos, D., & Pintelas, P. (2006). Handling imbalanced datasets: A review. GESTS International Transactions on Computer Science and Engineering, 30(1), 25–36.Google Scholar
- Mao, H., Counts, S., & Bollen, J. (2011). Predicting Financial Markets: Comparing Survey, News, Twitter and Search Engine Data. ArXiv E-prints, p. 10. Available from: http://arxiv.org/abs/1112.1051.
- McAfee, A., & Brynjolfsson, E. (2012). Big Data: The management Revolution: Exploiting vast new flows of information can radically improve your company’s performance. But first you’ll have to change your decision making culture’ Harvard Business Review.Google Scholar
- McGraw Hill Financial (n.d.). Dow Jones Averages | About the Averages | Overview. Retrieved August 12, 2015, from https://www.djaverages.com/?go=about-overview
- Oliveira, N., Cortez, P., & Areal, N. (2013b). Some experiments on modeling stock market behavior using investor sentiment analysis and posting volume from twitter. In Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics (p. 31). ACM.Google Scholar
- Oliveira, N., Cortez, P., & Areal, N. (2014, July). Automatic creation of stock market lexicons for sentiment analysis using StockTwits data. In Proceedings of the 18th International Database Engineering & Applications Symposium (pp. 115-123). ACM.Google Scholar
- Pedersen T, Banerjee S (2011) WordNet::Stem, Retrieved August 05, 2015, from http://search.cpan.org/~tpederse/WordNet-Similarity-2.05/lib/WordNet/stem.pm.
- Rao, T., & Srivastava, S. (2012, August). Analyzing stock market movements using twitter sentiment analysis. In Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012) (pp. 119-123). IEEE computer society.Google Scholar
- Schwert, G. W. (1998). Stock market volatility: Ten years after the crash (no. w6381). National Bureau of economic research.Google Scholar
- Stoffman, N. S. (2008). Individual and institutional investor behavior. ProQuest.Google Scholar