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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| Published in: | International journal of forecasting Vol. 36; no. 2; pp. 334 - 357 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier B.V
01.04.2020
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| Subjects: | |
| ISSN: | 0169-2070, 1872-8200 |
| Online Access: | Get full text |
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| 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. |
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| AbstractList | 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. |
| Author | Audrino, Francesco Sigrist, Fabio Ballinari, Daniele |
| Author_xml | – sequence: 1 givenname: Francesco surname: Audrino fullname: Audrino, Francesco email: francesco.audrino@unisg.ch organization: University of St. Gallen, Switzerland – sequence: 2 givenname: Fabio surname: Sigrist fullname: Sigrist, Fabio email: fabio.sigrist@hslu.ch organization: Lucerne University of Applied Sciences and Arts, Grafenauweg 10, 6304 Zug, Switzerland – sequence: 3 givenname: Daniele surname: Ballinari fullname: Ballinari, Daniele email: daniele.ballinari@unisg.ch organization: University of St. Gallen, Switzerland |
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| Keywords | Investor sentiment High-dimensional regression Volatility prediction Realized volatility Investor attention |
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