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 Pacic). The expansion of on-line nancial news sources, such as internet news and social media sources, provides instantaneous access to nancial news. Commercial agencies have started developing their own ltered nancial 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 rst dierences 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 nancial news sentiment scores and the volatility of the DJIA return series. We demonstrate how this calibration of machine ltered news can improve volatility measures.
|Date of creation:||14 Jan 2014|
|Date of revision:|
|Contact details of provider:|| Phone: 913942604|
Web page: https://www.ucm.es/icae
More information through EDIRC
|Order Information:|| Postal: Facultad de Ciencias Económicas y Empresariales. Pabellón prefabricado, 1ª Planta, ala norte. Campus de Somosaguas, 28223 - POZUELO DE ALARCÓN (MADRID)|
Web: https://www.ucm.es/fundamentos-analisis-economico2/documentos-de-trabajo-del-icae Email:
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- 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.
- 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.
- 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.
- Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-70, March.
- 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.
- McAleer, Michael, 2005. "Automated Inference And Learning In Modeling Financial Volatility," Econometric Theory, Cambridge University Press, vol. 21(01), pages 232-261, February.
- 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.
- 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.
- 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.
- 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.
- Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
When requesting a correction, please mention this item's handle: RePEc:ucm:doicae:1402. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Águeda González Abad)
If references are entirely missing, you can add them using this form.