Abstract
We retrieve news stories and earnings announcements of the S&P 100 constituents from two professional news providers, along with ten macroeconomic indicators. We also gather data from Google Trends about these firms’ assets as an index of retail investors’ attention. Thus, we create an extensive and innovative database that contains precise information with which to analyze the link between news and asset price dynamics. We detect the sentiment of news stories using a dictionary of sentiment-related words and negations and propose a set of more than five thousand information-based variables that provide natural proxies for the information used by heterogeneous market players. We first shed light on the impact of information measures on daily realized volatility and select them by penalized regression. Then, we perform a forecasting exercise and show that the model augmented with news-related variables provides superior forecasts.
Highlights
Traditional “efficient markets” thinking suggests that asset prices should completely and instantaneously reflect movements in underlying fundamentals, while an opposite view indicates that asset prices and fundamentals are continuously disconnected
A stream of the literature addresses the volatility reaction to news released on announcement days, focusing on the dynamics of conditional volatility based on the ARCH/GARCH framework introduced by Engle (1982) and Bollerslev (1986)
Vrugt (2009) analyzes the impact of US and Japanese macroeconomic news on stock market volatility in Japan, Hong Kong, South Korea, and Australia and employ a GARCH model that allows for multiplicative announcement effects and asymmetries to find that overnight conditional variances are higher on announcement days than on days before and after announcements, especially for US news, while the impact of announcements on implied volatilities is weak
Summary
Traditional “efficient markets” thinking suggests that asset prices should completely and instantaneously reflect movements in underlying fundamentals, while an opposite view indicates that asset prices and fundamentals are continuously disconnected. We shed light on the link between news information and volatility, focusing on three questions: What is the relative importance of types of news? We gather Google Trends information about the assets and use them as a proxy for retail investors’ attention. Our third contribution is to propose a set of news measures that provide natural proxies for retail investors’ attention and for the information heterogeneous market players use. We shed light on the impact of news on volatility and address the three questions posed above using the information-related variables we develop. We employ news and Google Trends to forecast volatility in an out-of-sample analysis. Empirical analyses favor the MDH and show that earnings announcements and news stories are the most important drivers of daily realized volatility, followed by macroeconomic news and Google. By including news-based information, we can improve volatility forecasting substantially
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