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Abstract
This study investigates the effect of news sentiment on stock market volatility using the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and measures the asymmetric effect with the GJR-GARCH model. We adopt patented linguistic analysis that considers the semantic orientation process to quantify financial news that may attract investor attention. This study distinguishes between unclassified market news sentiment and macroeconomic-related news effects. The evidence suggests that both contemporaneous and lagged news are determinants of market volatility. The effect is especially strong with the market aggregate news sentiment index (ANSI) and the negative ANSI, particularly during the 2008–2009 financial crisis period. This analysis of news sentiment improves the accuracy of in-sample and out-of-sample volatility forecasting.
Details
; Yang-Cheng, Lu 2 ; Jimmy, Yang J 3 1 Fu Jen Catholic University, Department of Finance and International Business, Xinzhuang Dist., New Taipei City, Taiwan (GRID:grid.256105.5) (ISNI:0000 0004 1937 1063); National Taiwan University, Center for Research in Econometric Theory and Applications, Taipei, Taiwan (GRID:grid.19188.39) (ISNI:0000 0004 0546 0241)
2 Ming Chuan University, Department of Finance, Taipei, Taiwan (GRID:grid.411804.8) (ISNI:0000 0004 0532 2834)
3 Oregon State University, School of Accounting, Finance, and Information Systems, College of Business, Corvallis, USA (GRID:grid.4391.f) (ISNI:0000 0001 2112 1969)