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Machine News and Volatility: The Dow Jones Industrial Average and the TRNA Sentiment Series

Author

Listed:
  • David E. Allen

    (University of South Africa)

  • Michael McAleer

    (National Tsing Hua University, Taiwan, Econometric Institute, Erasmus University Rotterdam, the Netherlands, Complutense University of Madrid, Spain)

  • Abhay K. Singh

    (Edith Cowan University, Perth, Australia)

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 Pacific). The expansion of on-line 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 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 first differences 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 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.

Suggested Citation

  • David E. Allen & Michael McAleer & Abhay K. Singh, 2014. "Machine News and Volatility: The Dow Jones Industrial Average and the TRNA Sentiment Series," Tinbergen Institute Discussion Papers 14-014/III, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20140014
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    References listed on IDEAS

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    7. David E Allen & Michael McAleer & Abhay K Singh, 2017. "An entropy-based analysis of the relationship between the DOW JONES Index and the TRNA Sentiment series," Applied Economics, Taylor & Francis Journals, vol. 49(7), pages 677-692, February.
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    Cited by:

    1. David E. Allen & Michael McAleer & David McHardy Reid, 2018. "Fake News And Indifference To Truth: Dissecting Tweets And State Of The Union Addresses By Presidents Obama And Trump," Advances in Decision Sciences, Asia University, Taiwan, vol. 22(1), pages 180-203, December.
    2. Allen, D.E. & McAleer, M.J. & McHardy Reid, D., 2018. "Fake News and Indifference to Truth," Econometric Institute Research Papers EI2018-10, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    3. Wei, Yu-Chen & Lu, Yang-Cheng & Chen, Jen-Nan & Hsu, Yen-Ju, 2017. "Informativeness of the market news sentiment in the Taiwan stock market," The North American Journal of Economics and Finance, Elsevier, vol. 39(C), pages 158-181.
    4. David E Allen & Michael McAleer & Abhay K Singh, 2017. "An entropy-based analysis of the relationship between the DOW JONES Index and the TRNA Sentiment series," Applied Economics, Taylor & Francis Journals, vol. 49(7), pages 677-692, February.
    5. David E. Allen & Michael McAleer, 2019. "Fake News and Propaganda: Trump’s Democratic America and Hitler’s National Socialist (Nazi) Germany," Sustainability, MDPI, vol. 11(19), pages 1-19, September.
    6. Massimiliano Caporin & Francesco Poli, 2017. "Building News Measures from Textual Data and an Application to Volatility Forecasting," Econometrics, MDPI, vol. 5(3), pages 1-46, August.

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    More about this item

    Keywords

    DJIA; Sentiment Scores; TRNA; Conditional Volatility Models;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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