Predicting Equity Markets with Digital Online Media Sentiment: Evidence from Markov-switching Models

The authors examine the predictive capabilities of online investor sentiment for the returns and volatility of MSCI U.S. Equity Sector Indices by including exogenous variables in the mean and volatility specifications of a Markov-switching model. As predicted by the semistrong efficient market hypothesis, they find that the Thomson Reuters Marketpsych Indices (TRMI) predict volatility to a greater extent than they do returns. The TRMI derived from equity specific digital news are better predictors than similar sentiment from social media. In the two-regime setting, there is evidence supporting the hypothesis of emotions playing a more important role during stressed markets compared to calm periods. The authors also find differences in sentiment sensitivity between different industries: it is greatest for financials, whereas the energy and information technology sectors are scarcely affected by sentiment. Results are obtained with the R programming language. Code is available from the authors upon request.