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An entropy based analysis of the relationship between the DOW JONES Index and the TRNA Sentiment series

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Allen, David E. and McAleer, Michael and Singh, Abhay K. (2017) An entropy based analysis of the relationship between the DOW JONES Index and the TRNA Sentiment series. [ Documentos de Trabajo del Instituto Complutense de Análisis Económico (ICAE); nº 01, 2017, ISSN: 2341-2356 ] (Unpublished)

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Abstract

This paper features an analysis of the relationship between the DOW JONES Industrial Average Index (DJIA) and a sentiment news series using daily data obtained from the Thomson Reuters News Analytics (TRNA)1 provided by SIRCA (The Securities Industry Research Centre of the Asia Pacic). The recent growth in the availability of on-line financial news sources such as internet news and social media sources provides instantaneous access to financial news. Various commercial agencies have started developing their own filtered financial news feeds which are used by investors and traders to support their algorithmic trading strategies. Thomson Reuters News Analytics (TRNA)2 is one such data set. In this study we use the TRNA data set to construct a series of daily sentiment scores for Dow Jones Industrial Average (DJIA) stock index component companies. We use these daily DJIA market sentiment scores to study the relationship between financial news sentiment scores and the stock prices of these companies using entropy measures. The entropy and Mutual Information (MI) statistics permit an analysis of the amount of information within the sentiment series, its relationship to the DJIA and an indication of how the relationship changes over time.


Item Type:Working Paper or Technical Report
Uncontrolled Keywords:DJIA, Sentiment, Entropy, TRNA, Information
Subjects:Social sciences > Economics > Econometrics
Social sciences > Economics > Stock exchanges
JEL:C29, G12, G14
Series Name:Documentos de Trabajo del Instituto Complutense de Análisis Económico (ICAE)
Volume:2017
Number:01
ID Code:40902
Deposited On:20 Jan 2017 11:25
Last Modified:07 Mar 2017 09:25

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