IDEAS home Printed from https://ideas.repec.org/a/eee/jeborg/v68y2008i3-4p613-625.html
   My bibliography  Save this article

The effects of intelligence on price discovery and market efficiency

Author

Listed:
  • Yeh, Chia-Hsuan

Abstract

The influence of speculation on market performance has long been discussed. Under the framework of bounded rationality in which traders are endowed with different intelligence levels in terms of different learning styles or different representations of intelligence, we examine the effects of traders' intelligence on price discovery based on "intraday" data, and market efficiency. We find that intelligence does help improve market performance. However, the influence of different intelligence levels on the market crucially depends on the characteristics of learning styles or the representation of intelligence.

Suggested Citation

  • Yeh, Chia-Hsuan, 2008. "The effects of intelligence on price discovery and market efficiency," Journal of Economic Behavior & Organization, Elsevier, vol. 68(3-4), pages 613-625, December.
  • Handle: RePEc:eee:jeborg:v:68:y:2008:i:3-4:p:613-625
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-2681(08)00138-8
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. De Long, J Bradford, et al, 1990. "Positive Feedback Investment Strategies and Destabilizing Rational Speculation," Journal of Finance, American Finance Association, vol. 45(2), pages 379-395, June.
    2. Jesse, Richard R, Jr & Radcliffe, Robert C, 1981. "On Speculation and Price Stability under Uncertainty," The Review of Economics and Statistics, MIT Press, vol. 63(1), pages 129-132, February.
    3. 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.
    4. repec:hrv:faseco:33077905 is not listed on IDEAS
    5. Dhananjay K. Gode & Shyam Sunder, 1997. "What Makes Markets Allocationally Efficient?," The Quarterly Journal of Economics, Oxford University Press, vol. 112(2), pages 603-630.
    6. Chiarella, Carl & Dieci, Roberto & Gardini, Laura, 2006. "Asset price and wealth dynamics in a financial market with heterogeneous agents," Journal of Economic Dynamics and Control, Elsevier, vol. 30(9-10), pages 1755-1786.
    7. Grossman, Sanford J & Stiglitz, Joseph E, 1980. "On the Impossibility of Informationally Efficient Markets," American Economic Review, American Economic Association, vol. 70(3), pages 393-408, June.
    8. Hart, Oliver D & Kreps, David M, 1986. "Price Destabilizing Speculation," Journal of Political Economy, University of Chicago Press, vol. 94(5), pages 927-952, October.
    9. 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.
    10. Shleifer, Andrei & Summers, Lawrence H, 1990. "The Noise Trader Approach to Finance," Journal of Economic Perspectives, American Economic Association, vol. 4(2), pages 19-33, Spring.
    11. LeBaron, Blake, 2006. "Agent-based Computational Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 24, pages 1187-1233, Elsevier.
    12. Chen, Shu-Heng & Yeh, Chia-Hsuan, 2002. "On the emergent properties of artificial stock markets: the efficient market hypothesis and the rational expectations hypothesis," Journal of Economic Behavior & Organization, Elsevier, vol. 49(2), pages 217-239, October.
    13. LeBaron, Blake & Arthur, W. Brian & Palmer, Richard, 1999. "Time series properties of an artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 23(9-10), pages 1487-1516, September.
    14. De Long, J Bradford & Shleifer, Andrei & Summers, Lawrence H & Waldmann, Robert J, 1991. "The Survival of Noise Traders in Financial Markets," The Journal of Business, University of Chicago Press, vol. 64(1), pages 1-19, January.
    15. Chen, Shu-Heng & Yeh, Chia-Hsuan, 2001. "Evolving traders and the business school with genetic programming: A new architecture of the agent-based artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 363-393, March.
    16. 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.
    17. Schimmler, Jorg, 1973. "Speculation, Profitability, and Price Stability-A Formal Approach," The Review of Economics and Statistics, MIT Press, vol. 55(1), pages 110-114, February.
    18. Black, Fischer, 1986. "Noise," Journal of Finance, American Finance Association, vol. 41(3), pages 529-543, July.
    19. Paul Brewer & Maria Huang & Brad Nelson & Charles Plott, 2002. "On the Behavioral Foundations of the Law of Supply and Demand: Human Convergence and Robot Randomness," Experimental Economics, Springer;Economic Science Association, vol. 5(3), pages 179-208, December.
    20. Jamal, Karim & Sunder, Shyam, 1996. "Bayesian equilibrium in double auctions populated by biased heuristic traders," Journal of Economic Behavior & Organization, Elsevier, vol. 31(2), pages 273-291, November.
    21. Chen, Shu-Heng & Lux, Thomas & Marchesi, Michele, 2001. "Testing for non-linear structure in an artificial financial market," Journal of Economic Behavior & Organization, Elsevier, vol. 46(3), pages 327-342, November.
    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. Strijbis, Oliver & Arnesen, Sveinung, 2019. "Explaining variance in the accuracy of prediction markets," International Journal of Forecasting, Elsevier, vol. 35(1), pages 408-419.
    2. Viktor Manahov & Mona Soufian & Robert Hudson, 2014. "The Implications Of Trader Cognitive Abilities On Stock Market Properties," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 21(1), pages 1-18, January.
    3. Yeh, Chia-Hsuan & Yang, Chun-Yi, 2010. "Examining the effectiveness of price limits in an artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 34(10), pages 2089-2108, October.
    4. Chia-Hsuan Yeh & Chun-Yi Yang, 2013. "Do price limits hurt the market?," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 8(1), pages 125-153, April.
    5. Soufian, Mona & Forbes, William & Hudson, Robert, 2014. "Adapting financial rationality: Is a new paradigm emerging?," CRITICAL PERSPECTIVES ON ACCOUNTING, Elsevier, vol. 25(8), pages 724-742.
    6. Ladley, Daniel & Lensberg, Terje & Palczewski, Jan & Schenk-Hoppé, Klaus Reiner, 2015. "Fragmentation and stability of markets," Journal of Economic Behavior & Organization, Elsevier, vol. 119(C), pages 466-481.
    7. Lijian Wei & Xiong Xiong & Wei Zhang & Xue-Zhong He & Yongjie Zhang, 2017. "The effect of genetic algorithm learning with a classifier system in limit order markets," Published Paper Series 2017-3, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    8. Manahov, Viktor & Hudson, Robert & Linsley, Philip, 2014. "New evidence about the profitability of small and large stocks and the role of volume obtained using Strongly Typed Genetic Programming," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 33(C), pages 299-316.
    9. Ladley, Daniel, 2020. "The high frequency trade off between speed and sophistication," Journal of Economic Dynamics and Control, Elsevier, vol. 116(C).
    10. Daniel Ladley, 2019. "The Design and Regulation of High Frequency Traders," Discussion Papers in Economics 19/02, Division of Economics, School of Business, University of Leicester.

    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. Yeh, Chia-Hsuan & Yang, Chun-Yi, 2010. "Examining the effectiveness of price limits in an artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 34(10), pages 2089-2108, October.
    2. Manahov, Viktor & Urquhart, Andrew, 2021. "The efficiency of Bitcoin: A strongly typed genetic programming approach to smart electronic Bitcoin markets," International Review of Financial Analysis, Elsevier, vol. 73(C).
    3. Tedeschi, Gabriele & Iori, Giulia & Gallegati, Mauro, 2012. "Herding effects in order driven markets: The rise and fall of gurus," Journal of Economic Behavior & Organization, Elsevier, vol. 81(1), pages 82-96.
    4. Linn, Scott C. & Stanhouse, Bryan E., 1997. "The economic advantage of least squares learning in a risky asset market," Journal of Economics and Business, Elsevier, vol. 49(4), pages 303-319.
    5. 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.
    6. 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.
    7. Chia-Hsuan Yeh, 2007. "The role of intelligence in time series properties," Computational Economics, Springer;Society for Computational Economics, vol. 30(2), pages 95-123, September.
    8. Ryuichi Yamamoto, 2011. "Volatility clustering and herding agents: does it matter what they observe?," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 6(1), pages 41-59, May.
    9. Jasmina Hasanhodzic & Andrew Lo & Emanuele Viola, 2011. "A computational view of market efficiency," Quantitative Finance, Taylor & Francis Journals, vol. 11(7), pages 1043-1050.
    10. Goldbaum, David, 2017. "Divergent Behavior in Markets with Idiosyncratic Private Information," Review of Behavioral Economics, now publishers, vol. 4(2), pages 181-213, September.
    11. Po-Keng Cheng & Young Shin Kim, 2017. "Speculative bubbles and crashes: Fundamentalists and positive‐feedback trading," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1381370-138, January.
    12. Miller, Edward M., 2000. "Equilibrium with divergence of opinion," Review of Financial Economics, Elsevier, vol. 9(1), pages 27-41.
    13. Ernst Fehr & Jean-Robert Tyran, 2005. "Individual Irrationality and Aggregate Outcomes," Journal of Economic Perspectives, American Economic Association, vol. 19(4), pages 43-66, Fall.
    14. Chau, Frankie & Deesomsak, Rataporn & Koutmos, Dimitrios, 2016. "Does investor sentiment really matter?," International Review of Financial Analysis, Elsevier, vol. 48(C), pages 221-232.
    15. Ledenyov, Dimitri O. & Ledenyov, Viktor O., 2015. "Wave function method to forecast foreign currencies exchange rates at ultra high frequency electronic trading in foreign currencies exchange markets," MPRA Paper 67470, University Library of Munich, Germany.
    16. Kyubin Yim & Gabjin Oh & Seunghwan Kim, 2016. "Understanding Financial Market States Using an Artificial Double Auction Market," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-15, March.
    17. Alexandru Mandes & Peter Winker, 2017. "Complexity and model comparison in agent based modeling of financial markets," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 12(3), pages 469-506, October.
    18. Gaunersdorfer, Andrea, 2000. "Endogenous fluctuations in a simple asset pricing model with heterogeneous agents," Journal of Economic Dynamics and Control, Elsevier, vol. 24(5-7), pages 799-831, June.
    19. Cheol‐Ho Park & Scott H. Irwin, 2007. "What Do We Know About The Profitability Of Technical Analysis?," Journal of Economic Surveys, Wiley Blackwell, vol. 21(4), pages 786-826, September.
    20. Adrian, Tobias, 2009. "Inference, arbitrage, and asset price volatility," Journal of Financial Intermediation, Elsevier, vol. 18(1), pages 49-64, January.

    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:eee:jeborg:v:68:y:2008:i:3-4:p:613-625. 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: . General contact details of provider: http://www.elsevier.com/locate/jebo .

    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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jebo .

    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.