On the Predictability of Stock Market Behavior Using StockTwits Sentiment and Posting Volume
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Abstract
In this study, we explored data from StockTwits, a microblogging platform exclusively dedicated to the stock market. We produced several indicators and analyzed their value when predicting three market variables: returns, volatility and trading volume. For six major stocks, we measured posting volume and sentiment indicators. We advance on the previous studies on this subject by considering a large time period, using a robust forecasting exercise and performing a statistical test of forecasting ability. In contrast with previous studies, we find no evidence of return predictability using sentiment indicators, and of information content of posting volume for forecasting volatility. However, there is evidence that posting volume can improve the forecasts of trading volume, which is useful for measuring stock liquidity (e.g. assets easily sold).
Keywords
Microblogging Data Returns Trading Volume Volatility RegressionPreview
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References
- 1.Amihud, Y., Mendelson, H., Pedersen, L.H.: Liquidity and Asset Prices. Foundations and Trends in Finance 1(4), 269–364 (2007)CrossRefGoogle Scholar
- 2.Antweiler, W., Frank, M.Z.: Is all that talk just noise? the information content of internet stock message boards. The Journal of Finance 59(3), 1259–1294 (2004)CrossRefGoogle Scholar
- 3.Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. Journal of Computational Science 2(1), 1–8 (2011)CrossRefGoogle Scholar
- 4.Harvey, D., Leybourne, S., Newbold, P.: Testing the equality of prediction mean squared errors. International Journal of Forecasting 13(2), 281–291 (1997)CrossRefGoogle Scholar
- 5.Hyndman, R.J., Koehler, A.B.: Another look at measures of forecast accuracy. International Journal of Forecasting 22(4), 679–688 (2006)CrossRefGoogle Scholar
- 6.Jiang, G.J.: The Model-Free Implied Volatility and Its Information Content. Review of Financial Studies 18(4), 1305–1342 (2005)CrossRefGoogle Scholar
- 7.Mao, H., Counts, S., Bollen, J.: Predicting financial markets: Comparing survey, news, twitter and search engine data. arXiv preprint arXiv:1112.1051 (2011)Google Scholar
- 8.Nofsinger, J.R.: Social mood and financial economics. The Journal of Behavioral Finance 6(3), 144–160 (2005)CrossRefGoogle Scholar
- 9.Oh, C., Sheng, O.R.L.: Investigating predictive power of stock micro blog sentiment in forecasting future stock price directional movement. In: ICIS 2011 Proceedings (2011)Google Scholar
- 10.Peterson, R.L.: Affect and financial decision-making: How neuroscience can inform market participants. The Journal of Behavioral Finance 8(2), 70–78 (2007)CrossRefGoogle Scholar
- 11.R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2012) ISBN 3-900051-07-0Google Scholar
- 12.Schumaker, R.P., Chen, H.: Textual analysis of stock market prediction using breaking financial news: The azfin text system. ACM Transactions on Information Systems (TOIS) 27(2), 12 (2009)CrossRefGoogle Scholar
- 13.Smola, A., Schölkopf, B.: A tutorial on support vector regression. Statistics and Computing 14, 199–222 (2004)CrossRefMathSciNetGoogle Scholar
- 14.Sprenger, T., Welpe, I.: Tweets and trades: The information content of stock microblogs. Social Science Research Network Working Paper Series, pp. 1–89 (2010)Google Scholar
- 15.Tashman, L.J.: Out-of-sample tests of forecasting accuracy: an analysis and review. International Journal of Forecasting 16(4), 437–450 (2000)CrossRefGoogle Scholar
- 16.Timmermann, A.: Elusive return predictability. International Journal of Forecasting 24(1), 1–18 (2008)CrossRefGoogle Scholar