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Media Attention vs. Sentiment as Drivers of Conditional Volatility Predictions: An Application to Brexit

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  • Guidolin, Massimo
  • Pedio, Manuela

Abstract

Using data on international, on-line media coverage and tone of the Brexit referendum, we test whether it is media coverage or tone to provide the largest forecasting performance improvements in the prediction of the conditional variance of weekly FTSE 100 stock returns. We find that versions of standard symmetric and asymmetric Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models augmented to include media coverage and especially media tone scores outperform traditional GARCH models both in- and out-of-sample.

Suggested Citation

  • Guidolin, Massimo & Pedio, Manuela, 2021. "Media Attention vs. Sentiment as Drivers of Conditional Volatility Predictions: An Application to Brexit," Finance Research Letters, Elsevier, vol. 42(C).
    • Handle: RePEc:eee:finlet:v:42:y:2021:i:c:s1544612321000246
    DOI: 10.1016/j.frl.2021.101943
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    1. Fariba Karimi & Matthias Raddant, 2016. "Cascades in Real Interbank Markets," Computational Economics, Springer;Society for Computational Economics, vol. 47(1), pages 49-66, January.
    2. Asger Lunde & Peter R. Hansen, 2005. "A forecast comparison of volatility models: does anything beat a GARCH(1,1)?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 873-889.
    3. Berry, Thomas D & Howe, Keith M, 1994. "Public Information Arrival," Journal of Finance, American Finance Association, vol. 49(4), pages 1331-1346, September.
    4. Blasco, Natividad & Corredor, Pilar & Del Rio, Cristina & Santamaria, Rafael, 2005. "Bad news and Dow Jones make the Spanish stocks go round," European Journal of Operational Research, Elsevier, vol. 163(1), pages 253-275, May.
    5. Campbell, John Y. & Hentschel, Ludger, 1992. "No news is good news *1: An asymmetric model of changing volatility in stock returns," Journal of Financial Economics, Elsevier, vol. 31(3), pages 281-318, June.
    6. Goddard, John & Kita, Arben & Wang, Qingwei, 2015. "Investor attention and FX market volatility," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 38(C), pages 79-96.
    7. Robert Engle, 2002. "New frontiers for arch models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 425-446.
    8. Gregory W. Brown & Michael T. Cliff, 2005. "Investor Sentiment and Asset Valuation," The Journal of Business, University of Chicago Press, vol. 78(2), pages 405-440, March.
    9. Zhi Da & Joseph Engelberg & Pengjie Gao, 2015. "Editor's Choice The Sum of All FEARS Investor Sentiment and Asset Prices," The Review of Financial Studies, Society for Financial Studies, vol. 28(1), pages 1-32.
    10. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    11. Davies, Ronald B. & Studnicka, Zuzanna, 2018. "The heterogeneous impact of Brexit: Early indications from the FTSE," European Economic Review, Elsevier, vol. 110(C), pages 1-17.
    12. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
    13. Arshad, Shaista & Rizvi, Syed Aun R. & Haroon, Omair, 2020. "Impact of Brexit vote on the London stock exchange: A sectorial analysis of its volatility and efficiency," Finance Research Letters, Elsevier, vol. 34(C).
    14. Mitchell, Mark L & Mulherin, J Harold, 1994. "The Impact of Public Information on the Stock Market," Journal of Finance, American Finance Association, vol. 49(3), pages 923-950, July.
    15. Broadstock, David C. & Zhang, Dayong, 2019. "Social-media and intraday stock returns: The pricing power of sentiment," Finance Research Letters, Elsevier, vol. 30(C), pages 116-123.
    16. 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.
    17. Ansgar Belke & Irina Dubova & Thomas Osowski, 2018. "Policy uncertainty and international financial markets: the case of Brexit," Applied Economics, Taylor & Francis Journals, vol. 50(34-35), pages 3752-3770, July.
    18. Wohlfarth, Paul, 2018. "Measuring the impact of monetary policy attention on global asset volatility using search data," Economics Letters, Elsevier, vol. 173(C), pages 15-18.
    19. 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.
    20. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    21. Thomas Dimpfl & Stephan Jank, 2016. "Can Internet Search Queries Help to Predict Stock Market Volatility?," European Financial Management, European Financial Management Association, vol. 22(2), pages 171-192, March.
    22. Shen, Dehua & Urquhart, Andrew & Wang, Pengfei, 2019. "Does twitter predict Bitcoin?," Economics Letters, Elsevier, vol. 174(C), pages 118-122.
    23. Tim Loughran & Bill Mcdonald, 2016. "Textual Analysis in Accounting and Finance: A Survey," Journal of Accounting Research, Wiley Blackwell, vol. 54(4), pages 1187-1230, September.
    24. Yang, Dennis & Zhang, Qiang, 2000. "Drift-Independent Volatility Estimation Based on High, Low, Open, and Close Prices," The Journal of Business, University of Chicago Press, vol. 73(3), pages 477-491, July.
    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. repec:bla:jfinan:v:59:y:2004:i:3:p:1259-1294 is not listed on IDEAS
    27. Adesina, Tola, 2017. "Estimating volatility persistence under a Brexit-vote structural break," Finance Research Letters, Elsevier, vol. 23(C), pages 65-68.
    28. Vlastakis, Nikolaos & Markellos, Raphael N., 2012. "Information demand and stock market volatility," Journal of Banking & Finance, Elsevier, vol. 36(6), pages 1808-1821.
    29. Lily Fang & Joel Peress, 2009. "Media Coverage and the Cross‐section of Stock Returns," Journal of Finance, American Finance Association, vol. 64(5), pages 2023-2052, October.
    30. Rafal Kierzenkowski & Nigel Pain & Elena Rusticelli & Sanne Zwart, 2016. "The Economic Consequences of Brexit: A Taxing Decision," OECD Economic Policy Papers 16, OECD Publishing.
    31. Massimo Guidolin & Alexei G. Orlov & Manuela Pedio, 2018. "How good can heuristic-based forecasts be? A comparative performance of econometric and heuristic models for UK and US asset returns," Quantitative Finance, Taylor & Francis Journals, vol. 18(1), pages 139-169, January.
    32. Hailiang Chen & Prabuddha De & Yu (Jeffrey) Hu & Byoung-Hyoun Hwang, 2014. "Wisdom of Crowds: The Value of Stock Opinions Transmitted Through Social Media," The Review of Financial Studies, Society for Financial Studies, vol. 27(5), pages 1367-1403.
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    Cited by:

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    3. Lyócsa, Štefan & Halousková, Martina & Haugom, Erik, 2023. "The US banking crisis in 2023: Intraday attention and price variation of banks at risk," Finance Research Letters, Elsevier, vol. 57(C).
    4. Szczygielski, Jan Jakub & Charteris, Ailie & Bwanya, Princess Rutendo & Brzeszczyński, Janusz, 2024. "Google search trends and stock markets: Sentiment, attention or uncertainty?," International Review of Financial Analysis, Elsevier, vol. 91(C).
    5. Hartvig, Áron Dénes & Pap, Áron & Pálos, Péter, 2023. "EU Climate Change News Index: Forecasting EU ETS prices with online news," Finance Research Letters, Elsevier, vol. 54(C).
    6. Szymon Lis, 2024. "Investor Sentiment in Asset Pricing Models: A Review of Empirical Evidence," Papers 2411.13180, arXiv.org.

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    More about this item

    Keywords

    Attention; Sentiment; Text Mining; Forecasting; Conditional Variance; GARCH model; Brexit;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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