The impact of sentiment and attention measures on stock market volatility

Francesco Audrino, Fabio Sigrist and Daniele Ballinari

International Journal of Forecasting, 2020, vol. 36, issue 2, 334-357

Abstract: We analyze the impact of sentiment and attention variables on the stock market volatility by using a novel and extensive dataset that combines social media, news articles, information consumption, and search engine data. We apply a state-of-the-art sentiment classification technique in order to 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 the most relevant variables to be 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. In addition, our study shows that attention and sentiment variables are able to improve volatility forecasts significantly, although the magnitudes of the improvements are relatively small from an economic point of view.

Keywords: Investor sentiment; Investor attention; Volatility prediction; Realized volatility; High-dimensional regression (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (94)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:36:y:2020:i:2:p:334-357

DOI: 10.1016/j.ijforecast.2019.05.010

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