Template-type: ReDIF-Paper 1.0
Author-Name: David E. Allen
Author-Email: d.allen@ecu.edu.au
Author-Person: pal66
Author-Workplace-Name: School of Accounting, Finance and Economics Edith Cowan University, Australia.
Author-Name: Michael McAleer
Author-Person: pmc90 
Author-Workplace-Name: Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam and Tinbergen Institute,
	The Netherlands, Department of Quantitative Economics, Complutense University of Madrid, and Institute of
	Economic Research, Kyoto University. 
Author-Name: Abhay K. Singh
Author-Workplace-Name: School of Accounting, Finance and Economics, Edith Cowan University
Title: Machine news and volatility: The Dow Jones Industrial Average and the TRNA sentiment series
Abstract: 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 Pacic). 
	The expansion of on-line nancial news sources, such as internet news and social media sources, provides 
	instantaneous access to nancial news. Commercial agencies have started developing their own ltered 
	nancial 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 rst dierences 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 nancial news sentiment scores and the volatility of the DJIA return series. We 
	demonstrate how this calibration of machine ltered news can improve volatility measures.
Classification-JEL: C58, G14.
Keywords: DJIA; Sentiment Scores; TRNA; Conditional Volatility Models.
Length: 18 pages 
Creation-Date: 2014-01-14  
Number: 2014-02 
X-File-Ref: http://america.sim.ucm.es/repec/ucm/ref/doicae1402.txt
File-URL: https://eprints.ucm.es/id/eprint/24356/1/1402.pdf
File-Format: Application/pdf
Handle: RePEc:ucm:doicae:1402