IDEAS home Printed from https://ideas.repec.org/p/apc/wpaper/140.html
   My bibliography  Save this paper

Can successful forecasters help stabilize asset prices in a learning to forecast experiment?

Author

Listed:
  • Dávid Kopányi

    (University of Amsterdam and Tinbergen Institute)

  • Jean Paul Rabanal

    (Monash University)

  • Olga A. Rud

    (University of Melbourne)

  • Jan Tuinstra

    (University of Amsterdam and Tinbergen Institute)

Abstract

We conduct a Learning to Forecast asset pricing experiment where the market impact of individual forecasts evolves endogenously based on the forecasters’ past accuracy. We investigate how endogenous impacts affect price stability and mispricing relative to the fundamental price. Our results suggest that endogenous impacts can destabilize markets when impacts are quite sensitive to forecast accuracy: Price dispersion increases compared to the baseline treatment where impacts are constant and independent of forecast accuracy. On the other hand, mispricing can be reduced when markets are relatively stable and impacts are moderately sensitive to forecast accuracy.

Suggested Citation

  • Dávid Kopányi & Jean Paul Rabanal & Olga A. Rud & Jan Tuinstra, 2019. "Can successful forecasters help stabilize asset prices in a learning to forecast experiment?," Working Papers 140, Peruvian Economic Association.
  • Handle: RePEc:apc:wpaper:140
    as

    Download full text from publisher

    File URL: http://perueconomics.org/wp-content/uploads/2019/01/WP-140.pdf
    File Function: Application/pdf
    Download Restriction: no

    References listed on IDEAS

    as
    1. Brock, William A. & Hommes, Cars H., 1998. "Heterogeneous beliefs and routes to chaos in a simple asset pricing model," Journal of Economic Dynamics and Control, Elsevier, vol. 22(8-9), pages 1235-1274, August.
    2. Agarwal, Vikas & Green, T. Clifton & Ren, Honglin, 2018. "Alpha or beta in the eye of the beholder: What drives hedge fund flows?," Journal of Financial Economics, Elsevier, vol. 127(3), pages 417-434.
    3. Mikhail Anufriev & Cars Hommes, 2012. "Evolutionary Selection of Individual Expectations and Aggregate Outcomes in Asset Pricing Experiments," American Economic Journal: Microeconomics, American Economic Association, vol. 4(4), pages 35-64, November.
    4. Heemeijer, Peter & Hommes, Cars & Sonnemans, Joep & Tuinstra, Jan, 2009. "Price stability and volatility in markets with positive and negative expectations feedback: An experimental investigation," Journal of Economic Dynamics and Control, Elsevier, vol. 33(5), pages 1052-1072, May.
    5. Marimon, Ramon & Sunder, Shyam, 1993. "Indeterminacy of Equilibria in a Hyperinflationary World: Experimental Evidence," Econometrica, Econometric Society, vol. 61(5), pages 1073-1107, September.
    6. C. H. Hommes, 2001. "Financial markets as nonlinear adaptive evolutionary systems," Quantitative Finance, Taylor & Francis Journals, vol. 1(1), pages 149-167.
    7. Thomas Stöckl & Jürgen Huber & Michael Kirchler, 2010. "Bubble measures in experimental asset markets," Experimental Economics, Springer;Economic Science Association, vol. 13(3), pages 284-298, September.
    8. Hommes, Cars & Sonnemans, Joep & Tuinstra, Jan & van de Velden, Henk, 2008. "Expectations and bubbles in asset pricing experiments," Journal of Economic Behavior & Organization, Elsevier, vol. 67(1), pages 116-133, July.
    9. Bao, Te & Duffy, John & Hommes, Cars, 2013. "Learning, forecasting and optimizing: An experimental study," European Economic Review, Elsevier, vol. 61(C), pages 186-204.
    10. Robin Greenwood & Andrei Shleifer, 2014. "Expectations of Returns and Expected Returns," Review of Financial Studies, Society for Financial Studies, vol. 27(3), pages 714-746.
    11. Bao, Te & Hommes, Cars & Sonnemans, Joep & Tuinstra, Jan, 2012. "Individual expectations, limited rationality and aggregate outcomes," Journal of Economic Dynamics and Control, Elsevier, vol. 36(8), pages 1101-1120.
    12. Friedman, Daniel, 1991. "Evolutionary Games in Economics," Econometrica, Econometric Society, vol. 59(3), pages 637-666, May.
    13. Bao, Te & Duffy, John, 2016. "Adaptive versus eductive learning: Theory and evidence," European Economic Review, Elsevier, vol. 83(C), pages 64-89.
    14. Te Bao & Cars Hommes & Tomasz Makarewicz, 2017. "Bubble Formation and (In)Efficient Markets in Learning‐to‐forecast and optimise Experiments," Economic Journal, Royal Economic Society, vol. 127(605), pages 581-609, October.
    15. Hommes, Cars, 2011. "The heterogeneous expectations hypothesis: Some evidence from the lab," Journal of Economic Dynamics and Control, Elsevier, vol. 35(1), pages 1-24, January.
    16. De Long, J Bradford & Andrei Shleifer & Lawrence H. Summers & Robert J. Waldmann, 1990. "Noise Trader Risk in Financial Markets," Journal of Political Economy, University of Chicago Press, vol. 98(4), pages 703-738, August.
    17. Erik R. Sirri & Peter Tufano, 1998. "Costly Search and Mutual Fund Flows," Journal of Finance, American Finance Association, vol. 53(5), pages 1589-1622, October.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Experimental finance; market impact; expectation formation; asset pricing; learning to forecast;

    JEL classification:

    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
    • D53 - Microeconomics - - General Equilibrium and Disequilibrium - - - Financial Markets
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

    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:apc:wpaper:140. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Nelson Ramírez-Rondán). General contact details of provider: http://edirc.repec.org/data/peruvea.html .

    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 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.