IDEAS home Printed from https://ideas.repec.org/p/sce/scecf1/49.html
   My bibliography  Save this paper

Evolution, Efficiency and Noise Traders in a One-Sided Auction Market

Author

Listed:
  • Guo Ying (Rosemary) Luo

Abstract

This paper uses an evolutionary approach incorporating the idea of natural selection to examine market behavior in a one-sided buyer auction market. Even with no traders' rationality (such as rational expectations and adaptive learning) and with each trader's behavior preprogrammed with its own inherent and fixed probabilities of overpredicting, predicting correctly and underpredicting the fundamental value of the asset, an informationally efficient market can occur. Traders' behavior is consistent with systematic patterns of judgment biases as documented in the psychological literature. Specifically, shares of one unit of a risky asset are sold at the beginning and liquidated at the end of each time period. The asset's liquidation value is the product of its fundamental value and the exponential of a random shock. Buyers enter the market sequentially over time and each buyer merely acts upon its own inherent and fixed probabilities of overpredicting, predicting correctly and underpredicting the fundamental value. As time goes by there is a constant redistribution of wealth toward buyers who make better predictions. This paper shows that if each buyer's initial wealth is sufficiently small relative to the market supply and if the variation in the random shock to the asset is sufficiently small, then as time gets sufficiently large, the proportion of time, that the asset price is arbitrarily close to the fundamental liquidation value, converges to one with probability one. This conclusion is established under a weak restriction regarding the presence of traders with sufficiently low probabilities of overpredicting the fundamental value.

Suggested Citation

  • Guo Ying (Rosemary) Luo, 2001. "Evolution, Efficiency and Noise Traders in a One-Sided Auction Market," Computing in Economics and Finance 2001 49, Society for Computational Economics.
  • Handle: RePEc:sce:scecf1:49
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Plott, Charles R & Sunder, Shyam, 1982. "Efficiency of Experimental Security Markets with Insider Information: An Application of Rational-Expectations Models," Journal of Political Economy, University of Chicago Press, vol. 90(4), pages 663-698, August.
    2. Antoni Bosch-Domenech & Shyam Sunder, 2000. "Tracking the Invisible Hand: Convergence of Double Auctions to Competitive Equilibrium," Computational Economics, Springer;Society for Computational Economics, vol. 16(3), pages 257-284, December.
    3. Lettau, Martin, 1997. "Explaining the facts with adaptive agents: The case of mutual fund flows," Journal of Economic Dynamics and Control, Elsevier, vol. 21(7), pages 1117-1147, June.
    4. Radner, Roy, 1979. "Rational Expectations Equilibrium: Generic Existence and the Information Revealed by Prices," Econometrica, Econometric Society, vol. 47(3), pages 655-678, May.
    5. Hellwig, Martin F., 1980. "On the aggregation of information in competitive markets," Journal of Economic Theory, Elsevier, vol. 22(3), pages 477-498, June.
    6. 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.
    7. Alvaro Sandroni, 2000. "Do Markets Favor Agents Able to Make Accurate Predicitions?," Econometrica, Econometric Society, vol. 68(6), pages 1303-1342, November.
    8. Robert Wilson, 1979. "Auctions of Shares," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 93(4), pages 675-689.
    9. Grossman, Sanford, 1978. "Further results on the informational efficiency of competitive stock markets," Journal of Economic Theory, Elsevier, vol. 18(1), pages 81-101, June.
    10. Gode, Dhananjay K & Sunder, Shyam, 1993. "Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality," Journal of Political Economy, University of Chicago Press, vol. 101(1), pages 119-137, February.
    11. Luo, Guo Ying, 1998. "Market Efficiency and Natural Selection in a Commodity Futures Market," The Review of Financial Studies, Society for Financial Studies, vol. 11(3), pages 647-674.
    12. Blume, Lawrence & Easley, David, 1992. "Evolution and market behavior," Journal of Economic Theory, Elsevier, vol. 58(1), pages 9-40, October.
    13. Forsythe, Robert & Palfrey, Thomas R & Plott, Charles R, 1982. "Asset Valuation in an Experimental Market," Econometrica, Econometric Society, vol. 50(3), pages 537-567, May.
    14. Luo Guo Ying, 1995. "Evolution and Market Competition," Journal of Economic Theory, Elsevier, vol. 67(1), pages 223-250, 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. Luo, Guo Ying, 2012. "Conservative traders, natural selection and market efficiency," Journal of Economic Theory, Elsevier, vol. 147(1), pages 310-335.
    2. Jasmina Hasanhodzic & Andrew Lo & Emanuele Viola, 2011. "A computational view of market efficiency," Quantitative Finance, Taylor & Francis Journals, vol. 11(7), pages 1043-1050.
    3. Andrew W. Lo & Mila Getmansky & Peter A. Lee, 2015. "Hedge Funds: A Dynamic Industry in Transition," Annual Review of Financial Economics, Annual Reviews, vol. 7(1), pages 483-577, December.

    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. Dindo, Pietro & Massari, Filippo, 2020. "The wisdom of the crowd in dynamic economies," Theoretical Economics, Econometric Society, vol. 15(4), November.
    2. Chueh-Yung Tsao & Ya-Chi Huang, 2018. "Revisiting the issue of survivability and market efficiency with the Santa Fe Artificial Stock Market," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 13(3), pages 537-560, October.
    3. Brock, W.A. & Hommes, C.H. & Wagener, F.O.O., 2009. "More hedging instruments may destabilize markets," Journal of Economic Dynamics and Control, Elsevier, vol. 33(11), pages 1912-1928, November.
    4. Andrew W. Lo & H. Allen Orr & Ruixun Zhang, 2018. "The growth of relative wealth and the Kelly criterion," Journal of Bioeconomics, Springer, vol. 20(1), pages 49-67, April.
    5. Emanuela Sciubba, 2006. "The evolution of portfolio rules and the capital asset pricing model," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 29(1), pages 123-150, September.
    6. Emanuela Sciubba, 2005. "Asymmetric information and survival in financial markets," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 25(2), pages 353-379, February.
    7. James Dow & Gary Gorton, 2006. "Noise Traders," NBER Working Papers 12256, National Bureau of Economic Research, Inc.
    8. Lawrence Choo & Todd R. Kaplan & Ro’i Zultan, 2019. "Information aggregation in Arrow–Debreu markets: an experiment," Experimental Economics, Springer;Economic Science Association, vol. 22(3), pages 625-652, September.
    9. Hirota, Shinichi & Sunder, Shyam, 2007. "Price bubbles sans dividend anchors: Evidence from laboratory stock markets," Journal of Economic Dynamics and Control, Elsevier, vol. 31(6), pages 1875-1909, June.
    10. Guidolin, Massimo & Ricci, Andrea, 2020. "Arbitrage risk and a sentiment as causes of persistent mispricing: The European evidence," The Quarterly Review of Economics and Finance, Elsevier, vol. 76(C), pages 1-11.
    11. Jordi Mondria & Xavier Vives & Liyan Yang, 2022. "Costly Interpretation of Asset Prices," Management Science, INFORMS, vol. 68(1), pages 52-74, January.
    12. Hommes, Cars H., 2006. "Heterogeneous Agent Models in Economics and Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 23, pages 1109-1186, Elsevier.
    13. Muendler, Marc-Andreas, 2007. "The possibility of informationally efficient markets," Journal of Economic Theory, Elsevier, vol. 133(1), pages 467-483, March.
    14. Lunawat, Radhika, 2021. "Learning from trading activity in laboratory security markets with higher-order uncertainty," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 90(C).
    15. Breitmayer, Bastian & Massari, Filippo & Pelster, Matthias, 2019. "Swarm intelligence? Stock opinions of the crowd and stock returns," International Review of Economics & Finance, Elsevier, vol. 64(C), pages 443-464.
    16. Masahiro Watanabe, 2002. "Price Volatility and Investor Behavior in an Overlapping Generations Model with Information Asymmetry," Yale School of Management Working Papers amz2636, Yale School of Management, revised 01 Jul 2002.
    17. Calvet, Laurent-Emmanuel & Grandmont, Jean-Michel & Lemaire, Isabelle, 2018. "Aggregation of heterogenous beliefs, asset pricing, and risk sharing in complete financial markets," Research in Economics, Elsevier, vol. 72(1), pages 117-146.
    18. Verrecchia, Robert E., 2001. "Essays on disclosure," Journal of Accounting and Economics, Elsevier, vol. 32(1-3), pages 97-180, December.
    19. Tarek Coury & Emanuela Sciubba, 2012. "Belief heterogeneity and survival in incomplete markets," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 49(1), pages 37-58, January.
    20. Choo, Lawrence, 2016. "Market competition for decision rights: An experiment based on the “Hat Puzzle Problem”," MPRA Paper 73408, University Library of Munich, Germany.

    More about this item

    Keywords

    Evolution; Natural selection; Behavioral finance; Market behavior;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games

    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:sce:scecf1:49. 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: Christopher F. Baum (email available below). General contact details of provider: https://edirc.repec.org/data/sceeeea.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.