IDEAS home Printed from https://ideas.repec.org/p/zbw/vfsc14/100284.html
   My bibliography  Save this paper

The Relation Between Overreaction in Forecasts and Uncertainty: A Nonlinear Approach

Author

Listed:
  • Leppin, Julian Sebstian

Abstract

This paper examines if overreaction of oil price forecasters is affected by uncertainty. Furthermore, it takes into account joint effects of uncertainty and oil price returns on forecast changes. The panel smooth transition regression model from Gonz alez et al. (2005) is applied with univariate and multivariate transition functions to account for nonlinear relations. Data on oil price expectations for different time horizons are taken from the European Central Bank Survey of Professional Forecasters. The results show that forecasters overreact for low levels of uncertainty and underreact for increasing uncertainty. Furthermore, returns are found to be more relevant for forecast changes in short time horizons while uncertainty dominates for longer ones.

Suggested Citation

  • Leppin, Julian Sebstian, 2014. "The Relation Between Overreaction in Forecasts and Uncertainty: A Nonlinear Approach," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100284, Verein für Socialpolitik / German Economic Association.
  • Handle: RePEc:zbw:vfsc14:100284
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/100284/1/VfS_2014_pid_748.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gilles Hilary & Lior Menzly, 2006. "Does Past Success Lead Analysts to Become Overconfident?," Management Science, INFORMS, vol. 52(4), pages 489-500, April.
    2. González, Andrés & Teräsvirta, Timo & van Dijk, Dick & Yang, Yukai, 2005. "Panel Smooth Transition Regression Models," SSE/EFI Working Paper Series in Economics and Finance 604, Stockholm School of Economics, revised 11 Oct 2017.
    3. Reitz, Stefan & Rülke, Jan-Christoph & Stadtmann, Georg, 2012. "Nonlinear expectations in speculative markets – Evidence from the ECB survey of professional forecasters," Journal of Economic Dynamics and Control, Elsevier, vol. 36(9), pages 1349-1363.
    4. Hansen, Bruce E., 1999. "Threshold effects in non-dynamic panels: Estimation, testing, and inference," Journal of Econometrics, Elsevier, vol. 93(2), pages 345-368, December.
    5. Zhaoyang Gu & Jian Xue, 2007. "Do analysts overreact to extreme good news in earnings?," Review of Quantitative Finance and Accounting, Springer, vol. 29(4), pages 415-431, November.
    6. Reitz, Stefan & Rülke, Jan & Stadtmann, Georg, 2012. "Nonlinear Expectations in Speculative Markets," VfS Annual Conference 2012 (Goettingen): New Approaches and Challenges for the Labor Market of the 21st Century 62045, Verein für Socialpolitik / German Economic Association.
    7. Erik Theissen, 2007. "An analysis of private investors' stock market return forecasts," Applied Financial Economics, Taylor & Francis Journals, vol. 17(1), pages 35-43.
    8. De Bondt, Werner F M & Thaler, Richard H, 1990. "Do Security Analysts Overreact?," American Economic Review, American Economic Association, vol. 80(2), pages 52-57, May.
    9. Francesca Pancotto & Filippo Maria Pericoli & Marco Pistagnesi, 2013. "Inefficiency in Survey Exchange Rates Forecasts," Working Papers 1/13, Sapienza University of Rome, DISS.
    10. Abarbanell, Jeffery S., 1991. "Do analysts' earnings forecasts incorporate information in prior stock price changes?," Journal of Accounting and Economics, Elsevier, vol. 14(2), pages 147-165, June.
    11. 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.
    12. Campbell, Sean D. & Sharpe, Steven A., 2009. "Anchoring Bias in Consensus Forecasts and Its Effect on Market Prices," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 44(2), pages 369-390, April.
    13. Pierdzioch, Christian & Rülke, Jan Christoph & Stadtmann, Georg, 2010. "New evidence of anti-herding of oil-price forecasters," Energy Economics, Elsevier, vol. 32(6), pages 1456-1459, November.
    14. Abarbanell, Jeffrey S & Bernard, Victor L, 1992. "Tests of Analysts' Overreaction/Underreaction to Earnings Information as an Explanation for Anomalous Stock Price Behavior," Journal of Finance, American Finance Association, vol. 47(3), pages 1181-1207, July.
    15. David Hirshleifer, 2001. "Investor Psychology and Asset Pricing," Journal of Finance, American Finance Association, vol. 56(4), pages 1533-1597, August.
    16. Carlos Bowles & Roberta Friz & Veronique Genre & Geoff Kenny & Aidan Meyler & Tuomas Rautanen, 2007. "The ECB survey of professional forecasters (SPF) – A review after eight years’ experience," Occasional Paper Series 59, European Central Bank.
    17. Amir, Eli & Ganzach, Yoav, 1998. "Overreaction and underreaction in analysts' forecasts," Journal of Economic Behavior & Organization, Elsevier, vol. 37(3), pages 333-347, November.
    18. Juan Angel Garcia, 2003. "An introduction to the ECB’s survey of professional forecasters," Occasional Paper Series 08, European Central Bank.
    19. Lof, Matthijs, 2012. "Heterogeneity in stock prices: A STAR model with multivariate transition function," Journal of Economic Dynamics and Control, Elsevier, vol. 36(12), pages 1845-1854.
    20. Kenny, Geoff & Genre, Véronique & Bowles, Carlos & Friz, Roberta & Meyler, Aidan & Rautanen, Tuomas, 2007. "The ECB survey of professional forecasters (SPF) - A review after eight years' experience," Occasional Paper Series 59, European Central Bank.
    21. Michael B. Clement & Senyo Y. Tse, 2005. "Financial Analyst Characteristics and Herding Behavior in Forecasting," Journal of Finance, American Finance Association, vol. 60(1), pages 307-341, February.
    22. Welch, Ivo, 2000. "Herding among security analysts," Journal of Financial Economics, Elsevier, vol. 58(3), pages 369-396, December.
    23. Werner F. M. De Bondt & William P. Forbes*, 1999. "Herding in analyst earnings forecasts: evidence from the United Kingdom," European Financial Management, European Financial Management Association, vol. 5(2), pages 143-163, July.
    24. Harrison Hong & Jeffrey D. Kubik & Amit Solomon, 2000. "Security Analysts' Career Concerns and Herding of Earnings Forecasts," RAND Journal of Economics, The RAND Corporation, vol. 31(1), pages 121-144, Spring.
    25. López-Villavicencio, Antonia & Mignon, Valérie, 2011. "On the impact of inflation on output growth: Does the level of inflation matter?," Journal of Macroeconomics, Elsevier, vol. 33(3), pages 455-464, September.
    26. X. Frank Zhang, 2006. "Information Uncertainty and Analyst Forecast Behavior," Contemporary Accounting Research, John Wiley & Sons, vol. 23(2), pages 565-590, June.
    27. De Bondt, Werner P. M., 1993. "Betting on trends: Intuitive forecasts of financial risk and return," International Journal of Forecasting, Elsevier, vol. 9(3), pages 355-371, November.
    28. John C. Easterwood & Stacey R. Nutt, 1999. "Inefficiency in Analysts' Earnings Forecasts: Systematic Misreaction or Systematic Optimism?," Journal of Finance, American Finance Association, vol. 54(5), pages 1777-1797, October.
    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. Leppin, Julian Sebastian, 2014. "The relation between overreaction in forecasts and uncertainty: A nonlinear approachvon," HWWI Research Papers 158, Hamburg Institute of International Economics (HWWI).
    2. Taoufik Elkemali, 2023. "Uncertainty and Financial Analysts’ Optimism: A Comparison between High-Tech and Low-Tech European Firms," Sustainability, MDPI, vol. 15(3), pages 1-22, January.
    3. Beshears, John & Milkman, Katherine L., 2011. "Do sell-side stock analysts exhibit escalation of commitment?," Journal of Economic Behavior & Organization, Elsevier, vol. 77(3), pages 304-317, March.
    4. Po‐Chang Chen & Ganapathi S. Narayanamoorthy & Theodore Sougiannis & Hui Zhou, 2020. "Analyst underreaction and the post‐forecast revision drift," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 47(9-10), pages 1151-1181, October.
    5. 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.
    6. Fujiwara, Ippei & Ichiue, Hibiki & Nakazono, Yoshiyuki & Shigemi, Yosuke, 2013. "Financial markets forecasts revisited: Are they rational, stubborn or jumpy?," Economics Letters, Elsevier, vol. 118(3), pages 526-530.
    7. Zhang, Chao & Shrider, David G. & Han, Dun & Wu, Yanran, 2022. "Accurate forecasts attract clients; Biased forecasts keep them happy," International Review of Financial Analysis, Elsevier, vol. 81(C).
    8. Kumar, Alok & Rantala, Ville & Xu, Rosy, 2022. "Social learning and analyst behavior," Journal of Financial Economics, Elsevier, vol. 143(1), pages 434-461.
    9. Raj Aggarwal & Brian M. Lucey & Fergal A. O'Connor, 2014. "Rationality in Precious Metals Forward Markets: Evidence of Behavioural Deviations in the Gold Markets," The Institute for International Integration Studies Discussion Paper Series iiisdp462, IIIS.
    10. Ramnath, Sundaresh & Rock, Steve & Shane, Philip, 2008. "The financial analyst forecasting literature: A taxonomy with suggestions for further research," International Journal of Forecasting, Elsevier, vol. 24(1), pages 34-75.
    11. Ciccone, Stephen J., 2005. "Trends in analyst earnings forecast properties," International Review of Financial Analysis, Elsevier, vol. 14(1), pages 1-22.
    12. Wu, Runze, 2023. "Sports Mood Index and sell-side analysts," The Quarterly Review of Economics and Finance, Elsevier, vol. 92(C), pages 35-48.
    13. Beyer, Anne & Cohen, Daniel A. & Lys, Thomas Z. & Walther, Beverly R., 2010. "The financial reporting environment: Review of the recent literature," Journal of Accounting and Economics, Elsevier, vol. 50(2-3), pages 296-343, December.
    14. Le, Cao Hoang Anh & Shan, Yaowen & Taylor, Stephen, 2024. "International economic policy uncertainty and analysts' earnings forecasts," Pacific-Basin Finance Journal, Elsevier, vol. 85(C).
    15. Anna M. Cianci & Satoris S. Culbertson, 2010. "The Impact of Motivational and Cognitive Factors on Optimistic Earnings Forecasts," Chapters, in: Brian Bruce (ed.), Handbook of Behavioral Finance, chapter 11, Edward Elgar Publishing.
    16. K. C. Kenneth Chu & W. H. Sophia Zhai, 2021. "Distress risk puzzle and analyst forecast optimism," Review of Quantitative Finance and Accounting, Springer, vol. 57(2), pages 429-460, August.
    17. Anolli, Mario & Beccalli, Elena & Molyneux, Philip, 2014. "Bank earnings forecasts, risk and the crisis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 29(C), pages 309-335.
    18. Low, Rand Kwong Yew & Tan, Enoch, 2016. "The role of analyst forecasts in the momentum effect," International Review of Financial Analysis, Elsevier, vol. 48(C), pages 67-84.
    19. Linnainmaa, Juhani T. & Torous, Walter & Yae, James, 2016. "Reading the tea leaves: Model uncertainty, robust forecasts, and the autocorrelation of analysts’ forecast errors," Journal of Financial Economics, Elsevier, vol. 122(1), pages 42-64.
    20. Wu, Yanran & Zhang, Chao, 2022. "Hard to arbitrage, hard for analysts to forecast," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).

    More about this item

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G29 - Financial Economics - - Financial Institutions and Services - - - Other
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

    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:zbw:vfsc14:100284. 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/vfsocea.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.