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Forecaster overconfidence and market survey performance

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  • Deaves, Richard
  • Lei, Jin
  • Schröder, Michael

Abstract

We document using the ZEW panel of German stock market forecasters that weak forecasters tend to be overconfident in the sense that they provide extreme forecasts and their confidence intervals are less likely to contain eventual realizations. Moderate filters based on forecast accuracy over short rolling windows are somewhat successful in improving predictability. While poor performance can be due to various factors, a filter based on a prior tendency to provide extreme forecasts also improves predictability.

Suggested Citation

  • Deaves, Richard & Lei, Jin & Schröder, Michael, 2015. "Forecaster overconfidence and market survey performance," Frankfurt School - Working Paper Series 218, Frankfurt School of Finance and Management.
    • Handle: RePEc:zbw:fsfmwp:218
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    References listed on IDEAS

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    1. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    2. William N. Goetzmann & Alok Kumar, 2008. "Equity Portfolio Diversification," Review of Finance, European Finance Association, vol. 12(3), pages 433-463.
    3. Deaves, Richard & Lüders, Erik & Schröder, Michael, 2010. "The dynamics of overconfidence: Evidence from stock market forecasters," Journal of Economic Behavior & Organization, Elsevier, vol. 75(3), pages 402-412, September.
    4. Jerker Denrell & Christina Fang, 2010. "Predicting the Next Big Thing: Success as a Signal of Poor Judgment," Management Science, INFORMS, vol. 56(10), pages 1653-1667, October.
    5. Ferreira, Miguel A. & Santa-Clara, Pedro, 2011. "Forecasting stock market returns: The sum of the parts is more than the whole," Journal of Financial Economics, Elsevier, vol. 100(3), pages 514-537, June.
    6. repec:bla:jfinan:v:53:y:1998:i:6:p:1839-1885 is not listed on IDEAS
    7. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    8. Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2014. "Forecasting the Equity Risk Premium: The Role of Technical Indicators," Management Science, INFORMS, vol. 60(7), pages 1772-1791, July.
    9. Malmendier, Ulrike & Tate, Geoffrey, 2008. "Who makes acquisitions? CEO overconfidence and the market's reaction," Journal of Financial Economics, Elsevier, vol. 89(1), pages 20-43, July.
    10. Marco Aiolfi & Carlo Ambrogio Favero, "undated". "Model Uncertainty, Thick Modelling and the predictability of Stock Returns," Working Papers 221, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    11. David Hirshleifer & Siew Hong Teoh, 2003. "Herd Behaviour and Cascading in Capital Markets: a Review and Synthesis," European Financial Management, European Financial Management Association, vol. 9(1), pages 25-66, March.
    12. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    13. David E. Rapach & Jack K. Strauss & Guofu Zhou, 2010. "Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy," The Review of Financial Studies, Society for Financial Studies, vol. 23(2), pages 821-862, February.
    14. Dan Lovallo & Colin Camerer, 1999. "Overconfidence and Excess Entry: An Experimental Approach," American Economic Review, American Economic Association, vol. 89(1), pages 306-318, March.
    15. Dick, Christian D. & Menkhoff, Lukas, 2013. "Exchange rate expectations of chartists and fundamentalists," Journal of Economic Dynamics and Control, Elsevier, vol. 37(7), pages 1362-1383.
    16. Brad M. Barber & Terrance Odean, 2000. "Trading Is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors," Journal of Finance, American Finance Association, vol. 55(2), pages 773-806, April.
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    Citations

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    Cited by:

    1. Brückbauer, Frank & Schröder, Michael, 2021. "Data resource profile: The ZEW FMS dataset," ZEW Discussion Papers 21-100, ZEW - Leibniz Centre for European Economic Research.
    2. Mariana Sedliačiková & Patrik Aláč & Mária Moresová, 2020. "How Behavioral Aspects Influence the Sustainable Financial Decisions of Shareholders: An Empirical Study and Proposal for a Relevant Decision-Making Concept," Sustainability, MDPI, vol. 12(12), pages 1-18, June.
    3. Brückbauer Frank & Schröder Michael, 2023. "The ZEW Financial Market Survey Panel," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 243(3-4), pages 451-469, June.
    4. Christoph Buehren & Tim Meyer & Christian Pierdzioch, 2020. "Experimental Evidence on Forecaster (anti-) Herding in Sports Markets," MAGKS Papers on Economics 202038, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    5. Brückbauer, Frank, 2022. "Do financial market experts know their theory? New evidence from survey data," ZEW Discussion Papers 20-092, ZEW - Leibniz Centre for European Economic Research, revised 2022.

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    More about this item

    Keywords

    Overconfidence; Forecasting Performance; Stock Market;
    All these keywords.

    JEL classification:

    • G02 - Financial Economics - - General - - - Behavioral Finance: Underlying Principles
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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