Exploiting News Analytics for Volatility Forecasting

Bibliographic Data

ID21653606
AuthorsSimon Tranberg Bodilsen (0000-0002-6735-8475, Department of Economics and Business Economics Aarhus University Aarhus Denmark, corresponding author), Asger Lunde (0000-0002-0805-0892, Copenhagen Economics Copenhagen Denmark)
Year2025
Volume40
Issue1
Pages18-36
Publication date2025-01-01
Open AccessYes
TypeARTICLE
VenueJournal of Applied Econometrics (JOURNAL)
Journal identifiersISSN: 1099-1255 • E-ISSN: 0883-7252
PublisherWiley (PUBLISHER • GB)
DOI10.1002/jae.3095
OpenAlexW4403701590
LanguageEN
References cited50

This study investigates the potential of news sentiment in predicting stock market volatility. We augment traditional time series models of realized volatility with the sentiment of macroeconomic and firm‐specific news. Our results demonstrate that incorporating the sentiment of domestic macroeconomic news significantly improves volatility predictions for individual stocks and the S&P 500 Index. Notably, we find substantial enhancements in long‐horizon volatility predictions when including the sentiment of macroeconomic news in the regression models. In contrast, firm‐specific news sentiment shows only modest predictive power in the general framework. However, expanding the set of predictors to include the news count of firm‐specific news occurring overnight between two consecutive trading periods significantly improves one‐period‐ahead volatility forecasts. JEL Classification: C53, C55, C58, G14, G17

Analytics · Data science · Econometrics · Economics · Machine learning · News analytics · Computer Science · Financial Risk and Volatility Modeling · Market Dynamics and Volatility · Stock Market Forecasting Methods

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