The time-varying nature of social media sentiments in modeling stock returns
作者：
Highlights：
• Is the relationship between social media sentiments and stock returns time-varying? How does one capture the inherent cross-correlation between stocks to better model the time-varying relationship?
• Answers to the above queries are tackled via a novel methodology: Bayesian Dynamic Linear Models and Seemingly Unrelated Regressions.
• The impact of social media sentiments on future stock returns vary over time.
• The posterior distributions of correlations range from -0.8 to +0.6.
• The time-varying social media sentiments coefficients are more stable in 2011 when compared to 2009; the latter was a period of high turbulence due to recession.
摘要
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.
论文关键词：Bayesian inference,Seemingly Unrelated Regressions,Social media sentiments,Dynamic Linear Models,Markov chain Monte Carlo
论文评审过程：Received 13 January 2017, Revised 3 June 2017, Accepted 16 June 2017, Available online 22 June 2017, Version of Record 19 August 2017.
论文官网地址：https://doi.org/10.1016/j.dss.2017.06.001
