The impact of sentiment and attention measures on stock market volatility
Type
working paper
Date Issued
2018-06-01
Author(s)
Abstract
We analyze the impact of sentiment and attention variables on volatility 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 the question of 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 "stock market"), 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.
Language
English
HSG Classification
contribution to scientific community
HSG Profile Area
SEPS - Quantitative Economic Methods
Contact Email Address
francesco.audrino@unisg.ch
Eprints ID
254343