Building News Measures from Textual Data and an Application to Volatility Forecasting
Massimiliano Caporin () and
Francesco Poli ()
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Francesco Poli: Department of Economics and Management, University of Padova/via del Santo, 33, 35123 Padova PD, Italy
Econometrics, 2017, vol. 5, issue 3, 1-46
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.
Keywords: volatility; news; Google Trends; sentiment analysis; big data; lasso; regularization (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jecnmx:v:5:y:2017:i:3:p:35-:d:108901
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