Title

Machine News and Volatility: The Dow Jones Industrial Average and the TRNA Real-Time High-Frequency Sentiment Series

Document Type

Book Chapter

Publisher

Elsevier

Place of Publication

Amsterdam

Editor(s)

Gregoriou, G.N.

Faculty

Health, Engineering and Science

School

School of Business

RAS ID

20669

Comments

This chapter was originally published as: Allan, D. E., McAleer, M. J. & Singh, A. K. (2015) Machine News and Volatility: The Dow Jones Industrial Average and the TRNA Real-Time High-Frequency Sentiment Series. In Gregoriou, G.N. (Ed.), The handbook of high frequency trading (pp.327-344) Amsterdam: Elsevier. Original chapter available here

Abstract

This chapter 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 The Securities Industry Research Center of the Asia Pacific. The expansion of online 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 chapter, we use a high-frequency sentiment series, developed by TRNA, to construct a series of daily sentiment scores for 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 (Generalized Autoregressive Conditional Heteroscedastic), EGARCH (Exponential Generalized Autoregressive Conditional Heteroscedasctic), and GJR (Glosten, Jagganathan and Rundle (1993)). 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.

DOI

10.1016/B978-0-12-802205-4.00019-1

Access Rights

Not open access

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