The impact of sentiment and attention measures on stock market volatility

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 inve...

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Vydáno v:International journal of forecasting Ročník 36; číslo 2; s. 334 - 357
Hlavní autoři: Audrino, Francesco, Sigrist, Fabio, Ballinari, Daniele
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.04.2020
Témata:
High-dimensional regression
Investor attention
Investor sentiment
Realized volatility
Volatility prediction
Investor sentiment
High-dimensional regression
Volatility prediction
Realized volatility
Investor attention
ISSN:0169-2070, 1872-8200
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Shrnutí: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.
ISSN:0169-2070
1872-8200
DOI:10.1016/j.ijforecast.2019.05.010