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

Author

Listed:
  • Allen, D.E.
  • McAleer, M.J.
  • Singh, A.K.
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.

Suggested Citation

  • Allen, D.E. & McAleer, M.J. & Singh, A.K., 2016. "An entropy based analysis of the relationship between the DOW JONES Index and the TRNA Sentiment series," Econometric Institute Research Papers EI2016-21, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:80108
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    References listed on IDEAS

    as
    1. David E. Allen & Michael McAleer & Abhay K. Singh, 2014. "Machine news and volatility: The Dow Jones Industrial Average and the TRNA sentiment series," Documentos de Trabajo del ICAE 2014-02, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    2. Golan, Amos, 2002. "Information and Entropy Econometrics--Editor's View," Journal of Econometrics, Elsevier, vol. 107(1-2), pages 1-15, March.
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    9. Smales, Lee A., 2014. "News sentiment in the gold futures market," Journal of Banking & Finance, Elsevier, vol. 49(C), pages 275-286.
    10. Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, June.
    11. Racine, Jeffrey S., 2008. "Nonparametric Econometrics: A Primer," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(1), pages 1-88, March.
    12. Steve Pincus, 2008. "Approximate Entropy as an Irregularity Measure for Financial Data," Econometric Reviews, Taylor & Francis Journals, vol. 27(4-6), pages 329-362.
    13. David E. Allen & Michael McAleer & Abhay K. Singh, 2019. "Daily market news sentiment and stock prices," Applied Economics, Taylor & Francis Journals, vol. 51(30), pages 3212-3235, June.
    14. Hayfield, Tristen & Racine, Jeffrey S., 2008. "Nonparametric Econometrics: The np Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i05).
    15. Andreas Storkenmaier & Martin Wagener & Christof Weinhardt, 2012. "Public information in fragmented markets," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 26(2), pages 179-215, June.
    16. Brad M. Barber & Terrance Odean, 2008. "All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors," The Review of Financial Studies, Society for Financial Studies, vol. 21(2), pages 785-818, April.
    17. Groß-Klußmann, Axel & Hautsch, Nikolaus, 2011. "When machines read the news: Using automated text analytics to quantify high frequency news-implied market reactions," Journal of Empirical Finance, Elsevier, vol. 18(2), pages 321-340, March.
    18. Paul C. Tetlock, 2010. "Does Public Financial News Resolve Asymmetric Information?," The Review of Financial Studies, Society for Financial Studies, vol. 23(9), pages 3520-3557.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Chinmoy Ghosh & Cristian Pinto‐Gutiérrez & Jaideep Shenoy, 2024. "Does negative news disclosure induce better decision‐making? Evidence from acquisitions," The Financial Review, Eastern Finance Association, vol. 59(2), pages 325-372, May.
    2. 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.
    3. Yang, Shanxiang & Liu, Zhechen & Wang, Xinjie, 2020. "News sentiment, credit spreads, and information asymmetry," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    4. 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.
    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.
    7. Tom Marty & Bruce Vanstone & Tobias Hahn, 2020. "News media analytics in finance: a survey," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(2), pages 1385-1434, June.
    8. 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.
    9. Zhang, Heng-Guo & CAO, Tingting & Li, Houxuan & Xu, Tiantian, 2021. "Dynamic measurement of news-driven information friction in China's carbon market: Theory and evidence," Energy Economics, Elsevier, vol. 95(C).
    10. David E. Allen & Michael McAleer, 2022. "Trump’s COVID-19 tweets and Dr. Fauci’s emails," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(3), pages 1643-1655, March.

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    More about this item

    Keywords

    DJIA; Sentiment; Entropy; TRNA; Information;
    All these keywords.

    JEL classification:

    • C29 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Other
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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