IDEAS home Printed from https://ideas.repec.org/a/eee/finana/v77y2021ics1057521921001800.html
   My bibliography  Save this article

Hunting the quicksilver: Using textual news and causality analysis to predict market volatility

Author

Listed:
  • Banerjee, Ameet Kumar
  • Dionisio, Andreia
  • Pradhan, H.K.
  • Mahapatra, Biplab

Abstract

This paper proposes that the dynamics of bond volatility may be understood by studying textual news sentiments. In this new approach, a modified framework is used to understand the atypical characteristics of bond market news. The paper proceeds in two steps. First, a word list of sentiment terms is generated using three sentiment word lists to determine negative and positive news sentiment scores. Second, four measures of volatility are estimated and combined with a nonlinear technique adapted from information theory to understand the correlation and direction of causality between sentiment scores and measures of volatility. This paper shows that sentiments extracted from textual news published in the newspapers can explain bond returns volatility or the quicksilver. The empirical results support that news sentiment is highly correlated with the measures of volatility and that information flows unidirectionally from news to volatility. This study, perhaps the earliest work in text mining to examine the run of causality between news signals and bond return volatility, adapts a nonlinear technique from information theory to describe the nonlinear behavior of Indian debt markets and understand the volatility dynamics of the benchmark bond.

Suggested Citation

  • Banerjee, Ameet Kumar & Dionisio, Andreia & Pradhan, H.K. & Mahapatra, Biplab, 2021. "Hunting the quicksilver: Using textual news and causality analysis to predict market volatility," International Review of Financial Analysis, Elsevier, vol. 77(C).
    • Handle: RePEc:eee:finana:v:77:y:2021:i:c:s1057521921001800
    DOI: 10.1016/j.irfa.2021.101848
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1057521921001800
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.irfa.2021.101848?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Altavilla, Carlo & Giannone, Domenico & Modugno, Michele, 2017. "Low frequency effects of macroeconomic news on government bond yields," Journal of Monetary Economics, Elsevier, vol. 92(C), pages 31-46.
    2. John Y. Campbell & Martin Lettau & Burton G. Malkiel & Yexiao Xu, 2001. "Have Individual Stocks Become More Volatile? An Empirical Exploration of Idiosyncratic Risk," Journal of Finance, American Finance Association, vol. 56(1), pages 1-43, February.
    3. Sanjiv R. Das & Mike Y. Chen, 2007. "Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web," Management Science, INFORMS, vol. 53(9), pages 1375-1388, September.
    4. Nina Gotthelf & Matthias W. Uhl, 2019. "News Sentiment: A New Yield Curve Factor," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 20(1), pages 31-41, January.
    5. Evans, Kevin P., 2011. "Intraday jumps and US macroeconomic news announcements," Journal of Banking & Finance, Elsevier, vol. 35(10), pages 2511-2527, October.
    6. Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 7(2), pages 174-196, Spring.
    7. Výrost, Tomáš & Lyócsa, Štefan & Baumöhl, Eduard, 2015. "Granger causality stock market networks: Temporal proximity and preferential attachment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 262-276.
    8. Price, S. McKay & Doran, James S. & Peterson, David R. & Bliss, Barbara A., 2012. "Earnings conference calls and stock returns: The incremental informativeness of textual tone," Journal of Banking & Finance, Elsevier, vol. 36(4), pages 992-1011.
    9. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    10. 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.
    11. 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, September.
    12. Hong Miao & Sanjay Ramchander & J. Kenton Zumwalt, 2014. "S&P 500 Index‐Futures Price Jumps and Macroeconomic News," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 34(10), pages 980-1001, October.
    13. Ameet Kumar Banerjee & H. K. Pradhan & Trilochan Tripathy & A. Kanagaraj, 2020. "Macroeconomic news surprises, volume and volatility relationship in index futures market," Applied Economics, Taylor & Francis Journals, vol. 52(3), pages 275-287, January.
    14. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    15. 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.
    16. Elder, John & Miao, Hong & Ramchander, Sanjay, 2012. "Impact of macroeconomic news on metal futures," Journal of Banking & Finance, Elsevier, vol. 36(1), pages 51-65.
    17. McNeil, Alexander J. & Frey, Rudiger, 2000. "Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 271-300, November.
    18. Dionisio, Andreia & Menezes, Rui & Mendes, Diana A., 2004. "Mutual information: a measure of dependency for nonlinear time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 344(1), pages 326-329.
    19. Nowak, Sylwia & Andritzky, Jochen & Jobst, Andreas & Tamirisa, Natalia, 2011. "Macroeconomic fundamentals, price discovery, and volatility dynamics in emerging bond markets," Journal of Banking & Finance, Elsevier, vol. 35(10), pages 2584-2597, October.
    20. Brian C Ross, 2014. "Mutual Information between Discrete and Continuous Data Sets," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-5, February.
    21. Gan, Baoqing & Alexeev, Vitali & Bird, Ron & Yeung, Danny, 2020. "Sensitivity to sentiment: News vs social media," International Review of Financial Analysis, Elsevier, vol. 67(C).
    22. Chen, Cathy Yi-Hsuan & Després, Roméo & Guo, Li & Renault, Thomas, 2019. "What makes cryptocurrencies special? Investor sentiment and return predictability during the bubble," IRTG 1792 Discussion Papers 2019-016, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    23. Maasoumi, Esfandiar & Racine, Jeff, 2002. "Entropy and predictability of stock market returns," Journal of Econometrics, Elsevier, vol. 107(1-2), pages 291-312, March.
    24. Ronny Luss & Alexandre D'Aspremont, 2015. "Predicting abnormal returns from news using text classification," Quantitative Finance, Taylor & Francis Journals, vol. 15(6), pages 999-1012, June.
    25. 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.
    26. Jacob Boudoukh & Ronen Feldman & Shimon Kogan & Matthew Richardson, 2019. "Information, Trading, and Volatility: Evidence from Firm-Specific News," The Review of Financial Studies, Society for Financial Studies, vol. 32(3), pages 992-1033.
    27. Stephen P. Ferris & (Grace) Qing Hao & (Stella) Min-Yu Liao, 2013. "The Effect of Issuer Conservatism on IPO Pricing and Performance," Review of Finance, European Finance Association, vol. 17(3), pages 993-1027.
    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. Gangopadhyay, Partha & Das, Narasingha & Kumar, Satish & Tanin, Tauhidul Islam, 2024. "Information warfare: Analyzing COVID-19 news and its economic fallout in the US," Research in International Business and Finance, Elsevier, vol. 70(PB).
    2. Banerjee, Ameet Kumar & Akhtaruzzaman, Md & Dionisio, Andreia & Almeida, Dora & Sensoy, Ahmet, 2022. "Nonlinear nexus between cryptocurrency returns and COVID-19 news sentiment," Journal of Behavioral and Experimental Finance, Elsevier, vol. 36(C).
    3. Banerjee, Ameet Kumar & Akhtaruzzaman, Md & Sensoy, Ahmet & Goodell, John W., 2024. "Volatility spillovers and hedging strategies between impact investing and agricultural commodities," International Review of Financial Analysis, Elsevier, vol. 94(C).
    4. Aharon, David Y. & Baig, Ahmed S. & Jacoby, Gady & Wu, Zhenyu, 2024. "Greenhouse gas emissions and the stability of equity markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 92(C).
    5. Banerjee, Ameet Kumar & Pradhan, H.K. & Sensoy, Ahmet & Goodell, John W., 2024. "Assessing the US financial sector post three bank collapses: Signals from fintech and financial sector ETFs," International Review of Financial Analysis, Elsevier, vol. 91(C).
    6. Banerjee, Ameet Kumar & Sensoy, Ahmet & Rahman, Molla Ramizur & Palma, Alessia, 2024. "Commonality in volatility among green, brown, and sustainable energy indices," Finance Research Letters, Elsevier, vol. 64(C).
    7. Banerjee, Ameet Kumar & Sensoy, Ahmet & Goodell, John W., 2024. "Connectivity and spillover during crises: Highlighting the prominent and growing role of green energy," Energy Economics, Elsevier, vol. 129(C).
    8. Banerjee, Ameet Kumar, 2024. "Environmental sustainability and the time-varying changing dynamics of green and brown energy ETFs," Finance Research Letters, Elsevier, vol. 62(PB).
    9. Banerjee, Ameet Kumar & Sensoy, Ahmet & Goodell, John W. & Mahapatra, Biplab, 2024. "Impact of media hype and fake news on commodity futures prices: A deep learning approach over the COVID-19 period," Finance Research Letters, Elsevier, vol. 59(C).
    10. Banerjee, Ameet Kumar & Özer, Zeynep Sueda & Rahman, Molla Ramizur & Sensoy, Ahmet, 2024. "How does the time-varying dynamics of spillover between clean and brown energy ETFs change with the intervention of climate risk and climate policy uncertainty?," International Review of Economics & Finance, Elsevier, vol. 93(PA), pages 442-468.
    11. Ahmed, Shamima & Banerjee, Ameet Kumar & James, Wendy & Moussa, Faten, 2024. "Is the Evergrande crisis spilling beyond China?," Research in International Business and Finance, Elsevier, vol. 67(PB).

    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. Kearney, Colm & Liu, Sha, 2014. "Textual sentiment in finance: A survey of methods and models," International Review of Financial Analysis, Elsevier, vol. 33(C), pages 171-185.
    2. Banerjee, Ameet Kumar & Akhtaruzzaman, Md & Dionisio, Andreia & Almeida, Dora & Sensoy, Ahmet, 2022. "Nonlinear nexus between cryptocurrency returns and COVID-19 news sentiment," Journal of Behavioral and Experimental Finance, Elsevier, vol. 36(C).
    3. Thomas Renault, 2020. "Sentiment analysis and machine learning in finance: a comparison of methods and models on one million messages," Digital Finance, Springer, vol. 2(1), pages 1-13, September.
    4. Ingrid E. Fisher & Margaret R. Garnsey & Mark E. Hughes, 2016. "Natural Language Processing in Accounting, Auditing and Finance: A Synthesis of the Literature with a Roadmap for Future Research," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(3), pages 157-214, July.
    5. Aysan, Ahmet Faruk & Caporin, Massimiliano & Cepni, Oguzhan, 2024. "Not all words are equal: Sentiment and jumps in the cryptocurrency market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 91(C).
    6. Christina Bannier & Thomas Pauls & Andreas Walter, 2019. "Content analysis of business communication: introducing a German dictionary," Journal of Business Economics, Springer, vol. 89(1), pages 79-123, February.
    7. Miwa, Kotaro, 2022. "The informational role of analysts’ textual statements," Research in International Business and Finance, Elsevier, vol. 59(C).
    8. Alomari, Mohammad & Al Rababa’a, Abdel Razzaq & El-Nader, Ghaith & Alkhataybeh, Ahmad & Ur Rehman, Mobeen, 2021. "Examining the effects of news and media sentiments on volatility and correlation: Evidence from the UK," The Quarterly Review of Economics and Finance, Elsevier, vol. 82(C), pages 280-297.
    9. Yan Luo & Linying Zhou, 2020. "Textual tone in corporate financial disclosures: a survey of the literature," International Journal of Disclosure and Governance, Palgrave Macmillan, vol. 17(2), pages 101-110, September.
    10. Santos, Douglas G. & Candido, Osvaldo & Tófoli, Paula V., 2022. "Forecasting risk measures using intraday and overnight information," The North American Journal of Economics and Finance, Elsevier, vol. 60(C).
    11. Bee, Marco & Dupuis, Debbie J. & Trapin, Luca, 2016. "Realizing the extremes: Estimation of tail-risk measures from a high-frequency perspective," Journal of Empirical Finance, Elsevier, vol. 36(C), pages 86-99.
    12. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2013. "Financial Risk Measurement for Financial Risk Management," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, volume 2, chapter 0, pages 1127-1220, Elsevier.
    13. Miwa, Kotaro, 2021. "Language barriers in analyst reports," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 223-236.
    14. Scott R. Baker & Nicholas Bloom & Steven J. Davis & Marco C. Sammon, 2021. "What Triggers Stock Market Jumps?," NBER Working Papers 28687, National Bureau of Economic Research, Inc.
    15. 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.
    16. Maciej Wujec, 2021. "Analysis of the Financial Information Contained in the Texts of Current Reports: A Deep Learning Approach," JRFM, MDPI, vol. 14(12), pages 1-17, December.
    17. Seshadri Tirunillai & Gerard J. Tellis, 2012. "Does Chatter Really Matter? Dynamics of User-Generated Content and Stock Performance," Marketing Science, INFORMS, vol. 31(2), pages 198-215, March.
    18. Allen, Linda & Bali, Turan G., 2007. "Cyclicality in catastrophic and operational risk measurements," Journal of Banking & Finance, Elsevier, vol. 31(4), pages 1191-1235, April.
    19. Smales, L.A., 2021. "Geopolitical risk and volatility spillovers in oil and stock markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 358-366.
    20. Banerjee, Ameet Kumar & Pradhan, H.K., 2022. "Intraday analysis of macroeconomic news surprises, and asymmetries in Indian benchmark bond," Finance Research Letters, Elsevier, vol. 45(C).

    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:eee:finana:v:77:y:2021:i:c:s1057521921001800. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/620166 .

    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.