Do Tweet Sentiments Still Predict the Stock Market?

16 Pages Posted: 22 Aug 2016

See all articles by Jim Kyung-Soo Liew

Jim Kyung-Soo Liew

Johns Hopkins University - Carey Business School

Tamás Budavári

Johns Hopkins University - Department of Applied Mathematics and Statistics

Date Written: August 8, 2016

Abstract

In this paper, we document a strong predictive relationship between US equity markets as proxied by the capitalization weighted index excess returns and it’s corresponding tweet sentiments. We employ StockTwits' labels of "Bullish" and "Bearish" to train several standard machine learning classification algorithms. Armed with our sentiment engines, we document that Support Vector Machines (SVM) generates the most useful predictive sentiments vis-à-vis those generated by Single Layer Perceptron (SLP) and Naïve Bayes (NB). Tweet sentiments are strongly positively contemporaneously correlated with daily market returns, additionally we find a weaker negatively correlation with next day's market returns.

We report statistically significant evidence that the market returns Granger-cause next day's sentiment movements. Moreover, in the most recent period of 2015, our rolling analysis shows significant evidence that the tweet sentiments actually Granger-caused the market to move! While a fascinating discovery, we temper our enthusiasm, as the market appears to have quickly and efficiently digested the ability to convert tweets into sentiment and in turn predict market movements. Our results support the notion of a highly efficient market that has rapidly digested and processed such tweets sentiment data.

Keywords: Tweet, Sentiment, Market Efficiency, Stock Markets

Suggested Citation

Liew, Jim Kyung-Soo and Budavari, Tamas, Do Tweet Sentiments Still Predict the Stock Market? (August 8, 2016). Available at SSRN: https://ssrn.com/abstract=2820269 or http://dx.doi.org/10.2139/ssrn.2820269

Jim Kyung-Soo Liew (Contact Author)

Johns Hopkins University - Carey Business School ( email )

100 International Drive
Baltimore, MD 21202
United States

Tamas Budavari

Johns Hopkins University - Department of Applied Mathematics and Statistics ( email )

3400 N Charles Street
Whitehead 100
Baltimore, MD 21218
United States

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