IDEAS home Printed from https://ideas.repec.org/p/uts/pwcwps/14.html
   My bibliography  Save this paper

The Relationship Between Uncertainty and the Market Reaction to Information: How is it Influenced by Market and Stock-Specific Characteristics?

Author

Abstract

Numerous empirical studies dating back to Ball and Brown (1968) have investigated how markets react to the receipt of new information. However, it is only recently that authors have focussed on differentiating between, and learning from, how investors react to good and bad news. In this paper we find that investors swing between being optimistic and being pessimistic in their interpretation of the new information driven by not only the prevailing market uncertainty and sentiment but also by a significant number of firm-specific characteristics. Pessimism prevails when uncertainty is high, sentiment is weak and the information is being disseminated by companies that are lowly-valued, have high risk, are thinly traded and/or are small cap stocks. However, investors swing to being optimistic when one reverses some or all of these factors. The conclusion that we draw is that risk, uncertainty and the attitude of investors combine to determine how the markets react to new information and this flows through to asset valuations.

Suggested Citation

  • Ron Bird & Krishna Reddy & Danny Yeung, 2011. "The Relationship Between Uncertainty and the Market Reaction to Information: How is it Influenced by Market and Stock-Specific Characteristics?," Working Paper Series 14, The Paul Woolley Centre for Capital Market Dysfunctionality, University of Technology, Sydney.
  • Handle: RePEc:uts:pwcwps:14
    as

    Download full text from publisher

    File URL: http://www.uts.edu.au/sites/default/files/wp14.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Freeman, Rn & Tse, Sy, 1992. "A Nonlinear Model Of Security Price Responses To Unexpected Earnings," Journal of Accounting Research, Wiley Blackwell, vol. 30(2), pages 185-209.
    2. Fama, Eugene F & French, Kenneth R, 1992. "The Cross-Section of Expected Stock Returns," Journal of Finance, American Finance Association, vol. 47(2), pages 427-465, June.
    3. Lakonishok, Josef & Shleifer, Andrei & Vishny, Robert W, 1994. "Contrarian Investment, Extrapolation, and Risk," Journal of Finance, American Finance Association, vol. 49(5), pages 1541-1578, December.
    4. X. Frank Zhang, 2006. "Information Uncertainty and Stock Returns," Journal of Finance, American Finance Association, vol. 61(1), pages 105-137, February.
    5. Larry G. Epstein & Martin Schneider, 2008. "Ambiguity, Information Quality, and Asset Pricing," Journal of Finance, American Finance Association, vol. 63(1), pages 197-228, February.
    6. Judson A. Caskey, 2009. "Information in Equity Markets with Ambiguity-Averse Investors," The Review of Financial Studies, Society for Financial Studies, vol. 22(9), pages 3595-3627, September.
    7. Mark Schneider & Jonathan W. Leland & Nathaniel T. Wilcox, 2018. "Ambiguity framed," Journal of Risk and Uncertainty, Springer, vol. 57(2), pages 133-151, October.
      • Mark Schneider & Jonathan Leland & Nathaniel T. Wilcox, 2016. "Ambiguity Framed," Working Papers 16-11, Chapman University, Economic Science Institute.
    8. Bird, Ron & Yeung, Danny, 2012. "How do investors react under uncertainty?," Pacific-Basin Finance Journal, Elsevier, vol. 20(2), pages 310-327.
    9. Ron Bird & Daniel Choi & Danny Yeung, 2014. "Market uncertainty, market sentiment, and the post-earnings announcement drift," Review of Quantitative Finance and Accounting, Springer, vol. 43(1), pages 45-73, July.
    10. Epstein, Larry G. & Schneider, Martin, 2003. "Recursive multiple-priors," Journal of Economic Theory, Elsevier, vol. 113(1), pages 1-31, November.
    11. Pastor, Lubos & Stambaugh, Robert F., 2003. "Liquidity Risk and Expected Stock Returns," Journal of Political Economy, University of Chicago Press, vol. 111(3), pages 642-685, June.
    12. Gilboa, Itzhak & Schmeidler, David, 1989. "Maxmin expected utility with non-unique prior," Journal of Mathematical Economics, Elsevier, vol. 18(2), pages 141-153, April.
    13. Veronesi, Pietro, 1999. "Stock Market Overreaction to Bad News in Good Times: A Rational Expectations Equilibrium Model," The Review of Financial Studies, Society for Financial Studies, vol. 12(5), pages 975-1007.
    14. Ron Bird & Xue-Zhong (Tony) He & Satish Thosar & Paul Woolley, 2005. "The case for market inefficiency: Investment style and market pricing," Journal of Asset Management, Palgrave Macmillan, vol. 5(6), pages 365-388, April.
    15. Jennifer Francis & Ryan Lafond & Per Olsson & Katherine Schipper, 2007. "Information Uncertainty and Post-Earnings-Announcement-Drift," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 34(3-4), pages 403-433.
    16. Anderson, Evan W. & Ghysels, Eric & Juergens, Jennifer L., 2009. "The impact of risk and uncertainty on expected returns," Journal of Financial Economics, Elsevier, vol. 94(2), pages 233-263, November.
    17. Dow, James & Werlang, Sergio Ribeiro da Costa, 1992. "Uncertainty Aversion, Risk Aversion, and the Optimal Choice of Portfolio," Econometrica, Econometric Society, vol. 60(1), pages 197-204, January.
    18. repec:bla:jfinan:v:59:y:2004:i:3:p:1367-1404 is not listed on IDEAS
    19. Blume, Lawrence & Easley, David & O'Hara, Maureen, 1994. "Market Statistics and Technical Analysis: The Role of Volume," Journal of Finance, American Finance Association, vol. 49(1), pages 153-181, March.
    20. Jennifer Conrad & Bradford Cornell & Wayne R. Landsman, 2002. "When Is Bad News Really Bad News?," Journal of Finance, American Finance Association, vol. 57(6), pages 2507-2532, December.
    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. Shraddha Mishra & Raj Kumar, 2016. "Investigation of overvalued and undervalued stocks: the case of BSE Sensex," International Journal of Business Excellence, Inderscience Enterprises Ltd, vol. 10(2), pages 177-189.

    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. Bird, Ron & Yeung, Danny, 2012. "How do investors react under uncertainty?," Pacific-Basin Finance Journal, Elsevier, vol. 20(2), pages 310-327.
    2. Massimo Guidolin & Francesca Rinaldi, 2013. "Ambiguity in asset pricing and portfolio choice: a review of the literature," Theory and Decision, Springer, vol. 74(2), pages 183-217, February.
    3. Ron Bird & Daniel Choi & Danny Yeung, 2014. "Market uncertainty, market sentiment, and the post-earnings announcement drift," Review of Quantitative Finance and Accounting, Springer, vol. 43(1), pages 45-73, July.
    4. Nawaf Almaskati & Ron Bird & Yue Lu & Danny Leung, 2019. "Corporate Governance, Information Uncertainty and Market Reaction to Information Signals," Working Papers in Economics 19/15, University of Waikato.
    5. Sujoy Mukerji & Han N. Ozsoylev & Jean‐Marc Tallon, 2023. "Trading Ambiguity: A Tale Of Two Heterogeneities," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(3), pages 1127-1164, August.
    6. Agarwal, Vikas & Arisoy, Y. Eser & Naik, Narayan Y., 2017. "Volatility of aggregate volatility and hedge fund returns," Journal of Financial Economics, Elsevier, vol. 125(3), pages 491-510.
    7. Larry G. Epstein & Martin Schneider, 2007. "Learning Under Ambiguity," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 74(4), pages 1275-1303.
    8. Lin, Mei-Chen, 2018. "The impact of aggregate uncertainty on herding in analysts' stock recommendations," International Review of Financial Analysis, Elsevier, vol. 57(C), pages 90-105.
    9. Yu, Edison G., 2018. "Dynamic market participation and endogenous information aggregation," Journal of Economic Theory, Elsevier, vol. 175(C), pages 491-517.
    10. Antoine Billot & Sujoy Mukerji & Jean-Marc Tallon, 2020. "Market Allocations under Ambiguity: A Survey," Revue économique, Presses de Sciences-Po, vol. 71(2), pages 267-282.
    11. Fabrice Collard & Sujoy Mukerji & Kevin Sheppard & Jean‐Marc Tallon, 2018. "Ambiguity and the historical equity premium," Quantitative Economics, Econometric Society, vol. 9(2), pages 945-993, July.
    12. Committee, Nobel Prize, 2013. "Understanding Asset Prices," Nobel Prize in Economics documents 2013-1, Nobel Prize Committee.
    13. Amélie Charles & Olivier Darné & Zakaria Moussa, 2014. "The sensitivity of Fama-French factors to economic uncertainty," Working Papers hal-01015702, HAL.
    14. Daniele Pennesi, 2013. "Asset Prices in an Ambiguous Economy," Carlo Alberto Notebooks 315, Collegio Carlo Alberto.
    15. Martin Schneider, 2010. "The Research Agenda: Martin Schneider on Multiple Priors Preferences and Financial Markets," EconomicDynamics Newsletter, Review of Economic Dynamics, vol. 11(2), April.
    16. James Ming Chen, 2017. "Econophysics and Capital Asset Pricing," Quantitative Perspectives on Behavioral Economics and Finance, Palgrave Macmillan, number 978-3-319-63465-4, February.
    17. Gyamfi-Yeboah, Frank & Ling, David C. & Naranjo, Andy, 2012. "Information, uncertainty, and behavioral effects: Evidence from abnormal returns around real estate investment trust earnings announcements," Journal of International Money and Finance, Elsevier, vol. 31(7), pages 1930-1952.
    18. Philipp K. Illeditsch & Jayant V. Ganguli & Scott Condie, 2021. "Information Inertia," Journal of Finance, American Finance Association, vol. 76(1), pages 443-479, February.
    19. Zhou, Tong, 2021. "Ambiguity, asset illiquidity, and price variability," Journal of Economic Behavior & Organization, Elsevier, vol. 191(C), pages 280-292.
    20. James Ming Chen, 2017. "Systematic Risk in the Macrocosm," Quantitative Perspectives on Behavioral Economics and Finance, in: Econophysics and Capital Asset Pricing, chapter 0, pages 239-274, Palgrave Macmillan.

    More about this item

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:uts:pwcwps:14. 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: Duncan Ford (email available below). General contact details of provider: https://edirc.repec.org/data/pwutsau.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.