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How Do Investors React Under Uncertainty?

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Abstract

It has long been accepted in finance that risk plays an important role in determining valuation where risk reflects that investors are unsure as to the exact value of future returns but are able to express their prior expectations by way of a probability distribution of these returns. Knights (1921) introduced the concept of uncertainty where we possess incomplete knowledge about this distribution and so are unable to formulate priors over all possible outcomes. A number of writers (Gilboa and Schmeidler, 1989; Epstein and Schneider, 2003) have developed models that suggest that ambiguity, like risk, has a negative impact on valuation. The most common approach taken in these models is to assume that investors take a conservative approach when faced with uncertainty and base their decisions on the worst case scenario (maxmin expected utility). The area on which we concentrate in this paper is how the market faced with uncertainty reacts to the receipt of new information. The proposition being that under maxmin expected utility, the interpretation that the market will place on any information received will become more pessimistic as uncertainty increases, upgrading any bad news and downgrading any good news. Williams (2009) uses changes in the VIX (i.e. implied market volatility) as a measure of market uncertainty in his US study where he evaluates the markets response to the release of earnings news. There is a plethora of evidence dating back to Ball and Brown (1968) that confirms that the market responds positively (negatively) to good (bad) news earnings announcements. Williams finds that this response is conditioned by market uncertainty with there being the predicted asymmetric reaction to good and bad earnings news – the negative reaction to bad news increasing with uncertainty and the positive reaction to good news decreasing. In this study we use Australian data to also examine the impact of uncertainty on the market response to earnings announcements. One important difference in our findings to those of Williams is that it is not only changes in VIX but also the level of VIX that influence how the market responds to earnings information. Although generally confirming a pessimistic response by investors to earnings released at a time of high market uncertainly, we find evidence of a slight optimistic bias in the reaction of investors to earnings released at a time of low market uncertainty. We also find that the level of pessimism engendered when uncertainly is high may be significantly diluted if it occurs contemporaneously with strong market sentiment.

Suggested Citation

  • Ron Bird & Danny Yeung, 2010. "How Do Investors React Under Uncertainty?," Working Paper Series 8, The Paul Woolley Centre for Capital Market Dysfunctionality, University of Technology, Sydney.
  • Handle: RePEc:uts:pwcwps:8
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    1. Arzu Ozoguz, 2009. "Good Times or Bad Times? Investors' Uncertainty and Stock Returns," Review of Financial Studies, Society for Financial Studies, vol. 22(11), pages 4377-4422, November.
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    Citations

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    Cited by:

    1. Qi Nan Zhai, 2015. "Asset Pricing Under Ambiguity and Heterogeneity," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 16.
    2. Kiran Thapa, 2013. "Stock Message Board Recommendations and Share Trading Activity," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 10.
    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. Dutt, Tanuj & Humphery-Jenner, Mark, 2013. "Stock return volatility, operating performance and stock returns: International evidence on drivers of the ‘low volatility’ anomaly," Journal of Banking & Finance, Elsevier, vol. 37(3), pages 999-1017.
    5. Ron Bird & Krishna Reddy & Danny Yeung, 2014. "The relationship between uncertainty and the market reaction to information: Is it influenced by stock-specific characteristics?," International Journal of Behavioural Accounting and Finance, Inderscience Enterprises Ltd, vol. 4(2), pages 113-132.
    6. Dakhlaoui, Imen & Aloui, Chaker, 2016. "The interactive relationship between the US economic policy uncertainty and BRIC stock markets," International Economics, Elsevier, vol. 146(C), pages 141-157.
    7. repec:bla:acctfi:v:57:y:2017:i::p:3-43 is not listed on IDEAS
    8. Laakkonen, Helinä, 2015. "Relevance of uncertainty on the volatility and trading volume in the US Treasury bond futures market," Research Discussion Papers 4/2015, Bank of Finland.
    9. Mai, Van Anh (Vivian) & Ang, Tze Chuan ‘Chewie’ & Fang, Victor, 2016. "Aggregate volatility risk and the cross-section of stock returns: Australian evidence," Pacific-Basin Finance Journal, Elsevier, vol. 36(C), pages 134-149.
    10. Jeffrey J. Coulton & Tami Dinh & Andrew B. Jackson & Tom Smith, 2016. "The impact of sentiment on price discovery," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 56(3), pages 669-694, September.
    11. Javier Giner & Sandra Morini & Rafael Rosillo, 2016. "Optimal Prediction Periods for New and Old Volatility Indexes in USA and German Markets," Computational Economics, Springer;Society for Computational Economics, vol. 47(4), pages 527-549, April.
    12. Kang, Wensheng & Ratti, Ronald A., 2013. "Oil shocks, policy uncertainty and stock market return," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 26(C), pages 305-318.
    13. Amandha Ganegoda & John Evans, 2014. "A framework to manage the measurable, immeasurable and the unidentifiable financial risk," Australian Journal of Management, Australian School of Business, vol. 39(1), pages 5-34, February.
    14. Guerello, Chiara, 2016. "The effect of investors’ confidence on monetary policy transmission mechanism," The North American Journal of Economics and Finance, Elsevier, vol. 37(C), pages 248-266.

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    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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