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Media, sentiment and market performance in the long run

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  • Roman Kräussl
  • Elizaveta Mirgorodskaya

Abstract

This paper investigates the impact of media pessimism on financial market returns and volatility in the long run. We hypothesize that media sentiment translates into investor sentiment. Based on the underreaction and overreaction hypotheses [Barberis, N., A. Shleifer, and R. Vishny. 1998. “A Model of Investor Sentiment.” Journal of Empirical Economics 49 (3): 307–343], we suggest that media pessimism has an effect on market performance after a lag of several months. We construct a monthly media pessimism indicator by taking the ratio of the number of newspaper articles that contain predetermined negative words to the number of newspaper articles that contain predetermined positive words in the headline and in the lead paragraph. Our results indicate that media pessimism is associated with negative (positive) market returns 14–17 (24–25) months in advance and positive market volatilities 1–20 months in advance. Our results are statistically and economically significant. We find evidence for Granger causality of media pessimism on market performance. Our media pessimism indicator possesses additional predictive power for the Baker and Wurgler [2006. “Investor Sentiment and the Cross-section of Stock Returns.” Journal of Finance 61 (4): 1645–1680] investor sentiment index and the Chicago Board Options Exchange Market Volatility Index.

Suggested Citation

  • Roman Kräussl & Elizaveta Mirgorodskaya, 2017. "Media, sentiment and market performance in the long run," The European Journal of Finance, Taylor & Francis Journals, vol. 23(11), pages 1059-1082, September.
    • Handle: RePEc:taf:eurjfi:v:23:y:2017:i:11:p:1059-1082
    DOI: 10.1080/1351847X.2016.1226188
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    Cited by:

    1. Carlini, Federico & Farina, Vincenzo & Gufler, Ivan & Previtali, Daniele, 2024. "Do stress and overstatement in the news affect the stock market? Evidence from COVID-19 news in The Wall Street Journal," International Review of Financial Analysis, Elsevier, vol. 93(C).
    2. Prajwal Eachempati & Praveen Ranjan Srivastava, 2021. "Accounting for unadjusted news sentiment for asset pricing," Qualitative Research in Financial Markets, Emerald Group Publishing Limited, vol. 13(3), pages 383-422, May.
    3. Walker, Clive B., 2024. "Going mainstream: Cryptocurrency narratives in newspapers," International Review of Financial Analysis, Elsevier, vol. 94(C).
    4. Yang, Shanxiang & Liu, Zhechen & Wang, Xinjie, 2020. "News sentiment, credit spreads, and information asymmetry," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    5. Mariya Gubareva & Zaghum Umar, 2023. "Emerging market debt and the COVID‐19 pandemic: A time–frequency analysis of spreads and total returns dynamics," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 112-126, January.
    6. Simionescu, Mihaela, 2022. "Econometrics of sentiments- sentometrics and machine learning: The improvement of inflation predictions in Romania using sentiment analysis," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    7. Umar, Zaghum & Adekoya, Oluwasegun Babatunde & Oliyide, Johnson Ayobami & Gubareva, Mariya, 2021. "Media sentiment and short stocks performance during a systemic crisis," International Review of Financial Analysis, Elsevier, vol. 78(C).
    8. Florin Cornel Dumiter & Florin Turcaș & Ștefania Amalia Nicoară & Cristian Bențe & Marius Boiță, 2023. "The Impact of Sentiment Indices on the Stock Exchange—The Connections between Quantitative Sentiment Indicators, Technical Analysis, and Stock Market," Mathematics, MDPI, vol. 11(14), pages 1-26, July.
    9. Hanna, Alan J. & Turner, John D. & Walker, Clive B., 2017. "News media and investor sentiment over the long run," QUCEH Working Paper Series 2017-06, Queen's University Belfast, Queen's University Centre for Economic History.
    10. Juanjuan Wang & Shujie Zhou & Wentong Liu & Lin Jiang, 2024. "An ensemble model for stock index prediction based on media attention and emotional causal inference," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 1998-2020, September.
    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. Andres Algaba & David Ardia & Keven Bluteau & Samuel Borms & Kris Boudt, 2020. "Econometrics Meets Sentiment: An Overview Of Methodology And Applications," Journal of Economic Surveys, Wiley Blackwell, vol. 34(3), pages 512-547, July.
    13. Demirovic, Amer & Kabiri, Ali & Tuckett, David & Nyman, Rickard, 2020. "A common risk factor and the correlation between equity and corporate bond returns," LSE Research Online Documents on Economics 116902, London School of Economics and Political Science, LSE Library.
    14. Bennani, Hamza, 2020. "Central bank communication in the media and investor sentiment," Journal of Economic Behavior & Organization, Elsevier, vol. 176(C), pages 431-444.
    15. Tom Marty & Bruce Vanstone & Tobias Hahn, 2020. "News media analytics in finance: a survey," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(2), pages 1385-1434, June.
    16. Amer Demirovic & Ali Kabiri & David Tuckett & Rickard Nyman, 2020. "A common risk factor and the correlation between equity and corporate bond returns," Journal of Asset Management, Palgrave Macmillan, vol. 21(2), pages 119-134, March.
    17. Zhang, Heng-Guo & CAO, Tingting & Li, Houxuan & Xu, Tiantian, 2021. "Dynamic measurement of news-driven information friction in China's carbon market: Theory and evidence," Energy Economics, Elsevier, vol. 95(C).
    18. S., Glogger & S., Heiden & D., Schneller, 2019. "Bearing the bear: Sentiment-based disagreement in multi-criteria portfolio optimization," Finance Research Letters, Elsevier, vol. 31(C), pages 47-53.
    19. Jan Kleinnijenhuis, 2018. "News, Ads, Chats, and Property Rights over Algorithms," Media and Communication, Cogitatio Press, vol. 6(3), pages 77-82.

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