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The effects of intelligence on price discovery and market efficiency

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  • 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.

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  • 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
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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    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. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. Nicholas Kaldor, 1939. "Speculation and Economic Stability," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 7(1), pages 1-27.
    12. 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.
    13. 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.
    14. W. Brian Arthur & Paul Tayler, "undated". "Asset Pricing Under Endogenous Expectations in an Artificial Stock Market," Computing in Economics and Finance 1997 57, Society for Computational Economics.
    15. 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.
    16. 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.
    17. Dhananjay K. Gode & Shyam Sunder, 1997. "What Makes Markets Allocationally Efficient?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 112(2), pages 603-630.
    18. 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.
    19. 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.
    20. Shu-Heng Chen & Chung-Ching Tai, 2003. "Trading Restrictions, Price Dynamics And Allocative Efficiency In Double Auction Markets: Analysis Based On Agent-Based Modeling And Simulations," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 6(03), pages 283-302.
    21. 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.
    22. Black, Fischer, 1986. "Noise," Journal of Finance, American Finance Association, vol. 41(3), pages 529-543, July.
    23. 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.
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    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.
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    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.
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