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Corporate Earnings Announcements and Stock Market Bubbles

Author

Listed:
  • Elie Bouri

    (School of Business, Lebanese American University, Lebanon)

  • Ufuk Can

    (Central Bank of the Republic of Turkiye, Adana, Turkiye; Centre for Applied Macroeconomic Analysis, Australian National University, Canberra, Australia; Economic Research Forum, Cairo, Egypt)

  • Oguzhan Cepni

    (Ostim Technical University, Ankara, Turkiye; University of Edinburgh Business School, Centre for Business, Climate Change, and Sustainability; Department of Economics, Copenhagen Business School, Denmark)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)

Abstract

We examine how corporate earnings (CE) announcement shocks influence stock market bubbles in the US using daily data from January 1990 to June 2025. After identifying positive and negative bubbles over short, medium, and long-term periods, Local Projections (LP) combine information from corporate earnings announcements with the varying intensity of shocks experienced on those specific days. Results show that positive earnings shocks boost positive bubbles, particularly at the medium to long-term, while reducing negative bubbles at the short-term. Therefore, favourable earnings news can fuel prolonged speculative episodes by increasing investor optimism, and lead to deep� crashes but only mild recoveries.

Suggested Citation

  • Elie Bouri & Ufuk Can & Oguzhan Cepni & Rangan Gupta, 2025. "Corporate Earnings Announcements and Stock Market Bubbles," Working Papers 202543, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202543
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    References listed on IDEAS

    as
    1. Carmen M. Reinhart & Kenneth S. Rogoff, 2009. "The Aftermath of Financial Crises," American Economic Review, American Economic Association, vol. 99(2), pages 466-472, May.
    2. Baker, Scott R. & Bloom, Nicholas & Davis, Steven J. & Sammo, Marco C., 2021. "What triggers stock market jumps?," LSE Research Online Documents on Economics 113913, London School of Economics and Political Science, LSE Library.
    3. Vladimir Filimonov & Didier Sornette, 2011. "A Stable and Robust Calibration Scheme of the Log-Periodic Power Law Model," Papers 1108.0099, arXiv.org, revised Jun 2013.
    4. Gupta, Rangan & Nel, Jacobus & Nielsen, Joshua & Pierdzioch, Christian, 2025. "Stock market volatility and multi-scale positive and negative bubbles," The North American Journal of Economics and Finance, Elsevier, vol. 75(PA).
    5. Jain, Archana & Jain, Chinmay & Khanapure, Revansiddha Basavaraj, 2020. "Pre-earnings announcement returns and momentum," Economics Letters, Elsevier, vol. 196(C).
    6. Mirela S. Miescu & Haroon Mumtaz, 2024. "Corporate Earnings Announcements And Economic Activity," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 65(4), pages 1777-1793, November.
    7. Wei-Fong Pan, 2020. "Does Investor Sentiment Drive Stock Market Bubbles? Beware of Excessive Optimism!," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 21(1), pages 27-41, January.
    8. Whitney Newey & Kenneth West, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    9. Muller, Ulrich A. & Dacorogna, Michel M. & Dave, Rakhal D. & Olsen, Richard B. & Pictet, Olivier V. & von Weizsacker, Jacob E., 1997. "Volatilities of different time resolutions -- Analyzing the dynamics of market components," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 213-239, June.
    10. Narayan, Paresh Kumar & Sharma, Susan Sunila & Phan, Dinh Hoang Bach, 2016. "Asset price bubbles and economic welfare," International Review of Financial Analysis, Elsevier, vol. 44(C), pages 139-148.
    11. Filimonov, V. & Sornette, D., 2013. "A stable and robust calibration scheme of the log-periodic power law model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(17), pages 3698-3707.
    12. Caraiani, Petre & Gupta, Rangan & Nel, Jacobus & Nielsen, Joshua, 2023. "Monetary policy and bubbles in G7 economies using a panel VAR approach: Implications for sustainable development," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 133-155.
    13. Shapiro, Adam Hale & Sudhof, Moritz & Wilson, Daniel J., 2022. "Measuring news sentiment," Journal of Econometrics, Elsevier, vol. 228(2), pages 221-243.
    14. Riza Demirer & Guilherme Demos & Rangan Gupta & Didier Sornette, 2019. "On the predictability of stock market bubbles: evidence from LPPLS confidence multi-scale indicators," Quantitative Finance, Taylor & Francis Journals, vol. 19(5), pages 843-858, May.
    15. Pavel Savor & Mungo Wilson, 2016. "Earnings Announcements and Systematic Risk," Journal of Finance, American Finance Association, vol. 71(1), pages 83-138, February.
    16. Caraiani, Petre & Călin, Adrian Cantemir, 2018. "The effects of monetary policy on stock market bubbles at zero lower bound: Revisiting the evidence," Economics Letters, Elsevier, vol. 169(C), pages 55-58.
    17. Barberis, Nicholas & Greenwood, Robin & Jin, Lawrence & Shleifer, Andrei, 2018. "Extrapolation and bubbles," Journal of Financial Economics, Elsevier, vol. 129(2), pages 203-227.
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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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