The Good, The Bad, and The Trending: Using Social Media Data To Test Theories of Momentum

40 Pages Posted: 15 Mar 2019 Last revised: 6 Jun 2019

See all articles by Austin Hill-Kleespie

Austin Hill-Kleespie

G. Brint Ryan College of Business, University of North Texas

Date Written: June 3, 2019

Abstract

I use a unique data set from investor centric social media site StockTwits to test the Hong and Stein (1999) two trader theory of momentum against Daniel, Hirshleifer, and Subrahmanyam (1998) self attribution theory. Tests of individual securities and portfolios produce results which are largely consistent with Hong and Stein (1999). For individual securities I find that trailing measures of sentiment have predictive power over future stock returns. Portfolios generated using StockTwits data are found to have strong explanatory power over the daily momentum factor from Carhart (1997).

Keywords: Microblogging, Sentiment Analysis, StockTwits, Stock Returns, Momentum

JEL Classification: G11, G12

Suggested Citation

Hill-Kleespie, Austin, The Good, The Bad, and The Trending: Using Social Media Data To Test Theories of Momentum (June 3, 2019). 9th Miami Behavioral Finance Conference 2018, Available at SSRN: https://ssrn.com/abstract=2906380 or http://dx.doi.org/10.2139/ssrn.2906380

Austin Hill-Kleespie (Contact Author)

G. Brint Ryan College of Business, University of North Texas

Denton, TX 76203
United States

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