IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/16854.html
   My bibliography  Save this paper

Learning to Fail? Evidence from Frequent IPO Investors

Author

Listed:
  • Chiang, Yao-Min
  • Hirshleifer, David
  • Qian, Yiming
  • Sherman, Ann

Abstract

We examine the effects of bidding experience on two groups of investors – individuals and institutions – in terms of their decisions to bid again and their bidding returns. Bidding histories are tracked for all 31,376 individual investors and 1,232 institutional investors across all 84 IPO auctions during 1995-2000 in Taiwan. For individual bidders: (1) high initial returns in IPO auctions increases the likelihood of participating in future auctions; (2) bidder returns steadily decrease as they participate in more auctions; (3) auction selection ability does not improve (and may get worse) with experience; and (4) greater experience is associated with more aggressive bid prices. These findings are consistent with naïve reinforcement learning wherein individuals become unduly optimistic after receiving good returns. In sharp contrast, there is little sign that institutional investors exhibit such behavior.

Suggested Citation

  • Chiang, Yao-Min & Hirshleifer, David & Qian, Yiming & Sherman, Ann, 2009. "Learning to Fail? Evidence from Frequent IPO Investors," MPRA Paper 16854, University Library of Munich, Germany, revised Aug 2009.
  • Handle: RePEc:pra:mprapa:16854
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/16854/1/MPRA_paper_16854.pdf
    File Function: original version
    Download Restriction: no

    File URL: https://mpra.ub.uni-muenchen.de/25005/1/MPRA_paper_25005.pdf
    File Function: revised version
    Download Restriction: no

    File URL: https://mpra.ub.uni-muenchen.de/25231/1/MPRA_paper_25231.pdf
    File Function: revised version
    Download Restriction: no

    References listed on IDEAS

    as
    1. Gervais, Simon & Odean, Terrance, 2001. "Learning to be Overconfident," Review of Financial Studies, Society for Financial Studies, vol. 14(1), pages 1-27.
    2. Gilles Hilary & Lior Menzly, 2006. "Does Past Success Lead Analysts to Become Overconfident?," Management Science, INFORMS, vol. 52(4), pages 489-500, April.
    3. Erev, Ido & Roth, Alvin E, 1998. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," American Economic Review, American Economic Association, vol. 88(4), pages 848-881, September.
    4. Sanford J. Grossman & Richard E. Kihlstrom & Leonard J. Mirman, 1977. "A Bayesian Approach to the Production of Information and Learning By Doing," Review of Economic Studies, Oxford University Press, vol. 44(3), pages 533-547.
    5. Markku Kaustia & Samuli Knüpfer, 2008. "Do Investors Overweight Personal Experience? Evidence from IPO Subscriptions," Journal of Finance, American Finance Association, vol. 63(6), pages 2679-2702, December.
    6. Nicolosi, Gina & Peng, Liang & Zhu, Ning, 2009. "Do individual investors learn from their trading experience?," Journal of Financial Markets, Elsevier, vol. 12(2), pages 317-336, May.
    7. A. Roth & I. Er’ev, 2010. "Learning in Extensive Form Games: Experimental Data and Simple Dynamic Models in the Intermediate Run," Levine's Working Paper Archive 387, David K. Levine.
    8. James J. Choi & David Laibson & Brigitte C. Madrian & Andrew Metrick, 2009. "Reinforcement Learning and Savings Behavior," Journal of Finance, American Finance Association, vol. 64(6), pages 2515-2534, December.
    9. Roth, Alvin E. & Erev, Ido, 1995. "Learning in extensive-form games: Experimental data and simple dynamic models in the intermediate term," Games and Economic Behavior, Elsevier, vol. 8(1), pages 164-212.
    10. Ravi Jagannathan & Ann E. Sherman, 2006. "Why Do IPO Auctions Fail?," NBER Working Papers 12151, National Bureau of Economic Research, Inc.
    11. Arthur, W Brian, 1991. "Designing Economic Agents that Act Like Human Agents: A Behavioral Approach to Bounded Rationality," American Economic Review, American Economic Association, vol. 81(2), pages 353-359, May.
    12. Sherman, Ann E., 2005. "Global trends in IPO methods: Book building versus auctions with endogenous entry," Journal of Financial Economics, Elsevier, vol. 78(3), pages 615-649, December.
    13. Matthew T. Billett & Yiming Qian, 2008. "Are Overconfident CEOs Born or Made? Evidence of Self-Attribution Bias from Frequent Acquirers," Management Science, INFORMS, vol. 54(6), pages 1037-1051, June.
    14. Kent Daniel & David Hirshleifer & Avanidhar Subrahmanyam, 1998. "Investor Psychology and Security Market Under- and Overreactions," Journal of Finance, American Finance Association, vol. 53(6), pages 1839-1885, December.
    15. Colin Camerer & Teck-Hua Ho, 1999. "Experience-weighted Attraction Learning in Normal Form Games," Econometrica, Econometric Society, vol. 67(4), pages 827-874, July.
    16. repec:bla:joares:v:35:y:1997:i::p:131-157 is not listed on IDEAS
    17. Yao-Min Chiang & Yiming Qian & Ann E. Sherman, 2010. "Endogenous Entry and Partial Adjustment in IPO Auctions: Are Institutional Investors Better Informed?," Review of Financial Studies, Society for Financial Studies, vol. 23(3), pages 1200-1230, March.
    18. Amit Seru & Tyler Shumway & Noah Stoffman, 2010. "Learning by Trading," Review of Financial Studies, Society for Financial Studies, vol. 23(2), pages 705-739, February.
    19. Reza Mahani & Dan Bernhardt, 2007. "Financial Speculators' Underperformance: Learning, Self‐Selection, and Endogenous Liquidity," Journal of Finance, American Finance Association, vol. 62(3), pages 1313-1340, June.
    20. Jacob, John & Lys, Thomas Z. & Neale, Margaret A., 1999. "Expertise in forecasting performance of security analysts," Journal of Accounting and Economics, Elsevier, vol. 28(1), pages 51-82, November.
    21. John G. Cross, 1973. "A Stochastic Learning Model of Economic Behavior," The Quarterly Journal of Economics, Oxford University Press, vol. 87(2), pages 239-266.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    IPO; auction; investor behavior; learning; reinforcement learning; institutional investor; individual investor; experience.;

    JEL classification:

    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage

    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:pra:mprapa:16854. 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: (Joachim Winter). General contact details of provider: http://edirc.repec.org/data/vfmunde.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.