Title: Using large and heterogeneous sources of sentiment and attention data for predicting stock market volatility
Authors: Francesco Audrino - University of St Gallen (Switzerland)
Daniele Ballinari - University of St Gallen (Switzerland)
Fabio Sigrist - Lucerne University of Applied Sciences (Switzerland) [presenting]
Abstract: The impact of sentiment and attention variables on volatility is analyzed by using a novel and extensive dataset that combines social media, news articles, information consumption, and search engine data. Applying a state-of-the-art sentiment classification technique, we investigate whether sentiment and attention measures contain additional predictive power for realized volatility when controlling for a wide range of economic and financial predictors. Using a penalized regression framework, we identify investors' attention, as measured by the number of Google searches on financial keywords (e.g. ``financial market'' and ``stockmarket''), and the daily volume of company-specific short messages posted on StockTwits to be the most relevant variables. In addition, our study shows that attention and sentiment variables are able to significantly improve volatility forecasts, although the improvements are of relatively small magnitude from an economic point of view.