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 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.
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- Richard D. F. Harris & Evarist Stoja & Jon Tucker, 2007. "A simplified approach to modeling the co‐movement of asset returns," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 27(6), pages 575-598, 06.
- 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.
- Bollerslev, Tim, 1986.
"Generalized autoregressive conditional heteroskedasticity,"
Journal of Econometrics,
Elsevier, vol. 31(3), pages 307-327, April.
- Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
- Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-70, March.
- Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993.
" On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks,"
Journal of Finance,
American Finance Association, vol. 48(5), pages 1779-1801, December.
- Lawrence R. Glosten & Ravi Jagannathan & David E. Runkle, 1993. "On the relation between the expected value and the volatility of the nominal excess return on stocks," Staff Report 157, Federal Reserve Bank of Minneapolis.
- 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.
- 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.
- 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.
- David E. Allen & Michael McAleer & Abhay K. Singh, 2016. "An Entropy Based Analysis of the Relationship between the DOW JONES Index and the TRNA Sentiment Series," Tinbergen Institute Discussion Papers 16-026/III, Tinbergen Institute.
- 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.
- David E. Allen & Michael McAleer & Abhay K. Singh, 2015. "Daily Market News Sentiment and Stock Prices," Tinbergen Institute Discussion Papers 15-090/III, Tinbergen Institute.
- David E. Allen & Michael McAleer & Abhay K. Singh, 2015. "Daily Market News Sentiment and Stock Prices," Documentos de Trabajo del ICAE 2015-11, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
- Smales, Lee A., 2014. "News sentiment in the gold futures market," Journal of Banking & Finance, Elsevier, vol. 49(C), pages 275-286.
- Andreas Storkenmaier & Martin Wagener & Christof Weinhardt, 2012. "Public information in fragmented markets," Financial Markets and Portfolio Management, Springer, vol. 26(2), pages 179-215, June.
- 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.
- Malcolm Baker & Jeffrey Wurgler, 2004.
"Investor Sentiment and the Cross-Section of Stock Returns,"
NBER Working Papers
10449, National Bureau of Economic Research, Inc.
- Malcolm Baker & Jeffrey Wurgler, 2006. "Investor Sentiment and the Cross-Section of Stock Returns," Journal of Finance, American Finance Association, vol. 61(4), pages 1645-1680, 08.
- Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
- Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
- McAleer, Michael, 2005. "Automated Inference And Learning In Modeling Financial Volatility," Econometric Theory, Cambridge University Press, vol. 21(01), pages 232-261, February.
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