The Impact of Sentiment and Attention Measures on Stock Market Volatility

33 Pages Posted: 16 Jun 2018

See all articles by Francesco Audrino

Francesco Audrino

University of St. Gallen

Fabio Sigrist

Institute of Financial Services Zug (IFZ)

Daniele Ballinari

University of St. Gallen

Date Written: June 1, 2018

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.

Keywords: Sentiment, Investor Attention, Realized Volatility, Volatility Forecasting, HAR

JEL Classification: C22, C53, C58, G14

Suggested Citation

Audrino, Francesco and Sigrist, Fabio and Ballinari, Daniele, The Impact of Sentiment and Attention Measures on Stock Market Volatility (June 1, 2018). Available at SSRN: https://ssrn.com/abstract=3188941 or http://dx.doi.org/10.2139/ssrn.3188941

Francesco Audrino (Contact Author)

University of St. Gallen ( email )

Bodanstrasse 6
St. Gallen, CH-9000
Switzerland

Fabio Sigrist

Institute of Financial Services Zug (IFZ) ( email )

Zug, CH-6304
Switzerland

Daniele Ballinari

University of St. Gallen ( email )

Bodanstrasse, 6
St. Gallen, St. Gallen 9000
Switzerland

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