IDEAS home Printed from https://ideas.repec.org/p/cbt/econwp/14-04.html
   My bibliography  Save this paper

Machine news and volatility: The Dow Jones Industrial Average and the TRNA sentiment series

Author

Listed:

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," Working Papers in Economics 14/04, University of Canterbury, Department of Economics and Finance.
  • Handle: RePEc:cbt:econwp:14/04
    as

    Download full text from publisher

    File URL: https://repec.canterbury.ac.nz/cbt/econwp/1404.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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, June.
    2. 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, August.
    3. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    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. 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.
    6. Smales, Lee A., 2014. "News sentiment in the gold futures market," Journal of Banking & Finance, Elsevier, vol. 49(C), pages 275-286.
    7. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    8. 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.
    9. McAleer, Michael, 2005. "Automated Inference And Learning In Modeling Financial Volatility," Econometric Theory, Cambridge University Press, vol. 21(1), pages 232-261, February.
    10. 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.
    11. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
    12. 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.
    13. Robert Engle, 2001. "GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 157-168, Fall.
    14. 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.
    15. 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.
    16. 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.
    17. 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, June.
    18. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    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. 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.
    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. 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.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yen-Ju Hsu & Yang-Cheng Lu & J. Jimmy Yang, 2021. "News sentiment and stock market volatility," Review of Quantitative Finance and Accounting, Springer, vol. 57(3), pages 1093-1122, October.
    2. Wang, Wenzhao, 2018. "Investor sentiment and the mean-variance relationship: European evidence," Research in International Business and Finance, Elsevier, vol. 46(C), pages 227-239.
    3. 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.
    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. Allen, David E. & Amram, Ron & McAleer, Michael, 2013. "Volatility spillovers from the Chinese stock market to economic neighbours," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 94(C), pages 238-257.
    6. 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.
    7. Allen, David E. & McAleer, Michael & Powell, Robert J. & Singh, Abhay K., 2017. "Volatility Spillovers from Australia's major trading partners across the GFC," International Review of Economics & Finance, Elsevier, vol. 47(C), pages 159-175.
    8. Wang, Wenzhao & Duxbury, Darren, 2021. "Institutional investor sentiment and the mean-variance relationship: Global evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 191(C), pages 415-441.
    9. Yu, Jianfeng & Yuan, Yu, 2011. "Investor sentiment and the mean-variance relation," Journal of Financial Economics, Elsevier, vol. 100(2), pages 367-381, May.
    10. Tim Bollerslev, 2008. "Glossary to ARCH (GARCH)," CREATES Research Papers 2008-49, Department of Economics and Business Economics, Aarhus University.
    11. 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.
    12. 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.
    13. 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.
    14. Georgios Bampinas & Theodore Panagiotidis & Christina Rouska, 2019. "Volatility persistence and asymmetry under the microscope: the role of information demand for gold and oil," Scottish Journal of Political Economy, Scottish Economic Society, vol. 66(1), pages 180-197, February.
    15. Ho, Kin-Yip & Shi, Yanlin & Zhang, Zhaoyong, 2013. "How does news sentiment impact asset volatility? Evidence from long memory and regime-switching approaches," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 436-456.
    16. Svetlana Borovkova & Diego Mahakena, 2015. "News, volatility and jumps: the case of natural gas futures," Quantitative Finance, Taylor & Francis Journals, vol. 15(7), pages 1217-1242, July.
    17. Shen, Shulin & Xia, Le & Shuai, Yulin & Gao, Da, 2022. "Measuring news media sentiment using big data for Chinese stock markets," Pacific-Basin Finance Journal, Elsevier, vol. 74(C).
    18. Rui Fan & Oleksandr Talavera & Vu Tran, 2020. "Social media bots and stock markets," European Financial Management, European Financial Management Association, vol. 26(3), pages 753-777, June.
    19. Felix Irresberger & Fee Elisabeth König & Gregor N. F. Weiß, 2017. "Crisis Sentiment in the U.S. Insurance Sector," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 84(4), pages 1295-1330, December.
    20. Khuu, Joyce & Durand, Robert B. & Smales, Lee A., 2016. "Melancholia and Japanese stock returns – 2003 to 2012," Pacific-Basin Finance Journal, Elsevier, vol. 40(PB), pages 424-437.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cbt:econwp:14/04. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Albert Yee (email available below). General contact details of provider: https://edirc.repec.org/data/decannz.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.