Machine news and volatility: The Dow Jones Industrial Average and the TRNA sentiment series
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 Paci...
| Autores: | , , |
|---|---|
| Tipo de documento: | relatório científico |
| Data de publicação: | 2014 |
| País: | España |
| Recursos: | Universidad Complutense de Madrid (UCM) |
| Repositório: | Docta Complutense |
| Idioma: | inglês |
| OAI Identifier: | oai:docta.ucm.es:20.500.14352/41545 |
| Acesso em linha: | https://hdl.handle.net/20.500.14352/41545 |
| Access Level: | Acceso aberto |
| Palavra-chave: | DJIA Sentiment Scores TRNA Conditional Volatility Models. Econometría (Economía) 5302 Econometría |
| Resumo: | 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. |
|---|