Exploiting News Analytics for Volatility Forecasting
研究将宏观经济和公司特定新闻的情绪加入传统波动率模型，发现宏观经济新闻情绪显著提升个股和标普500指数的波动率预测，尤其是长期预测；隔夜公司新闻数量则改善短期预测。
ABSTRACT 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.