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An Entropy Based Analysis of the Relationship between the DOW JONES Index and the TRNA Sentiment Series

Listed author(s):
  • David E. Allen

    (Centre for Applied Financial Studies, UniSA, South Africa, and Visiting Professor University of Sydney, Australia)

  • Michael McAleer

    (National Tsing Hua University, Taiwan; Erasmus School of Economics, Erasmus University Rotterdam, and Tinbergen Institute, the Netherlands; Complutense University of Madrid, Spain)

  • Abhay K. Singh

    (Edith Cowan University, Perth, Australia)

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.

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File URL: http://papers.tinbergen.nl/16026.pdf
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Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 16-026/III.

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Date of creation: 19 Apr 2016
Handle: RePEc:tin:wpaper:20160026
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  1. Amos Golan & Esfandiar Maasoumi, 2008. "Information Theoretic and Entropy Methods: An Overview," Econometric Reviews, Taylor & Francis Journals, vol. 27(4-6), pages 317-328.
  2. Leela Mitra & Gautam Mitra & Dan Dibartolomeo, 2009. "Equity portfolio risk estimation using market information and sentiment," Quantitative Finance, Taylor & Francis Journals, vol. 9(8), pages 887-895.
  3. Smales, Lee A., 2014. "News sentiment in the gold futures market," Journal of Banking & Finance, Elsevier, vol. 49(C), pages 275-286.
  4. Allen, D.E. & McAleer, M.J. & Singh, A.K., 2015. "Daily Market News Sentiment and Stock Prices," Econometric Institute Research Papers EI2015-23, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  5. Hayfield, Tristen & Racine, Jeffrey S., 2008. "Nonparametric Econometrics: The np Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i05).
  6. 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.
  7. Brad M. Barber & Terrance Odean, 2008. "All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors," Review of Financial Studies, Society for Financial Studies, vol. 21(2), pages 785-818, April.
  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. 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.
  10. Paul C. Tetlock, 2010. "Does Public Financial News Resolve Asymmetric Information?," Review of Financial Studies, Society for Financial Studies, vol. 23(9), pages 3520-3557.
  11. Golan, Amos, 2002. "Information and Entropy Econometrics--Editor's View," Journal of Econometrics, Elsevier, vol. 107(1-2), pages 1-15, March.
  12. Paul C. Tetlock & Maytal Saar-Tsechansky & Sofus Macskassy, 2008. "More Than Words: Quantifying Language to Measure Firms' Fundamentals," Journal of Finance, American Finance Association, vol. 63(3), pages 1437-1467, 06.
  13. C. W. Granger & E. Maasoumi & J. Racine, 2004. "A Dependence Metric for Possibly Nonlinear Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(5), pages 649-669, 09.
  14. Anil Bera & Sung Park, 2008. "Optimal Portfolio Diversification Using the Maximum Entropy Principle," Econometric Reviews, Taylor & Francis Journals, vol. 27(4-6), pages 484-512.
  15. 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, 06.
  16. Steve Pincus, 2008. "Approximate Entropy as an Irregularity Measure for Financial Data," Econometric Reviews, Taylor & Francis Journals, vol. 27(4-6), pages 329-362.
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