| By: |
David E. Allen (School of Accounting, Finance and Economics Edith Cowan University, Australia.);
Michael McAleer (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.);
Abhay K. Singh (School of Accounting, Finance and Economics, Edith Cowan University) |
| 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. |
| Keywords: |
DJIA; Sentiment Scores; TRNA; Conditional Volatility Models. |
| JEL: |
C58 G14 |
| Date: |
2014–01–14 |
| URL: |
http://d.repec.org/n?u=RePEc:ucm:doicae:1402&r=tur |