A1744
Title: News measures, volatility and jumps
Authors: Francesco Poli - University of Padova (Italy) [presenting]
Massimiliano Caporin - University of Padova (Italy)
Abstract: From two professional news providers we retrieve news stories and earnings announcements of the S\&P 100 constituents and a set of 10 macroeconomic fundamentals. We thus create an extensive and innovative dataset which contains information with minute precision, useful to analyze the link between news and asset price dynamics. We develop a novel text-analysis technique to detect the sentiment of a financial text of any type, size and audience, and propose a set of more than 4K news-based variables that provide natural proxies of the information used by heterogeneous market players. We first shed light on the impact of news on daily realized volatility and select news measures by penalized regression. Then, we distinguish the relative importance between news measures and use them to forecast volatility. Finally, we investigate the relation between news and intraday jumps, within a penalized logit approach.
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