Machine news and volatility: The Dow Jones Industrial Average and the TRNA sentiment series
David Allen (),
Michael McAleer and
Abhay K. Singh
Working Papers in Economics from University of Canterbury, Department of Economics and Finance
This paper features an analysis of the relationship between the volatility of the Dow Jones Industrial Average (DJIA) Index and a sentiment news series using daily data obtained from the Thomson Reuters News Analytics (TRNA) provided by SIRCA (The Securities Industry Research Centre of the Asia Pacific). The expansion of on-line financial news sources, such as internet news and social media sources, provides instantaneous access to financial news. Commercial agencies have started developing their own filtered financial news feeds, which are used by investors and traders to support their algorithmic trading strategies. In this paper we use a sentiment series, developed by TRNA, to construct a series of daily sentiment scores for Dow Jones Industrial Average (DJIA) stock index component companies. A variety of forms of this measure, namely basic scores, absolute values of the series, squared values of the series, and the first differences of the series, are used to estimate three standard volatility models, namely GARCH, EGARCH and GJR. We use these alternative daily DJIA market sentiment scores to examine the relationship between financial news sentiment scores and the volatility of the DJIA return series. We demonstrate how this calibration of machine filtered news can improve volatility measures.
Keywords: DJIA; Sentiment Scores; TRNA; Conditional Volatility Models (search for similar items in EconPapers)
JEL-codes: C58 G14 (search for similar items in EconPapers)
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Working Paper: Machine News and Volatility: The Dow Jones Industrial Average and the TRNA Sentiment Series (2014)
Working Paper: Machine news and volatility: The Dow Jones Industrial Average and the TRNA sentiment series (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:cbt:econwp:14/04
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