Abstract
The broad aim of this paper is to answer the following query: is the relationship between social media sentiments and stock returns time-varying? To provide a satisfactory response, a novel methodology-a symbiosis of Bayesian Dynamic Linear Models and Seemingly Unrelated Regressions -is introduced. Two sets of Dow Jones Industrial Average stock data and corresponding social media data from Yahoo! Finance stock message boards are used in a comprehensive empirical study. Some key findings are: (a) Affirmative response to the above question; (b) Models with only social media sentiments and market returns perform at least as well as models that include Fama-French and Momentum factors; (c) There are significant correlations between stocks, ranging from -0.8 to 0.6 in both data sets.
Original language | English (US) |
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Journal | Decision Support Systems |
DOIs | |
State | Accepted/In press - Jan 13 2017 |
Keywords
- Bayesian inference
- Dynamic Linear Models
- Markov chain Monte Carlo
- Seemingly Unrelated Regressions
- Social media sentiments
ASJC Scopus subject areas
- Management Information Systems
- Information Systems
- Developmental and Educational Psychology
- Arts and Humanities (miscellaneous)
- Information Systems and Management