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Can Intelligence Help Improve Market Performance?


  • Chia-Hsuan Yeh


The issue regarding the influence of intelligence on market efficiency has been discussed for a long time. Gode and Sunder (1993) mentioned that the aggregate behavior of zero-intelligence traders is able to generate an efficient market. They introduced two types of markets composed of zero-intelligence traders: one that imposes a budget constraint on traders and the other that does not. They found that the inclusion of a budget constraint was enough for the market to induce allocative efficiency. Therefore, learning, intelligence, and profit motivation are not necessary. However, Cliff and Bruten (1997) demonstrated that zero intelligence is not enough and that traders with remarkably simple adaptive mechanisms (zero-intelligence-plus traders) could perform very similarly to groups of human traders. On the other hand, Chen, Tai, and Chie (2002) reached a different conclusion. In their computer simulations, smartness did not enhance market performance. In fact, the experiment with smarter traders resulted in less stable price dynamics and lower allocative efficiency. In this paper, based on the framework of artificial stock market with many heterogeneous agents, the way of modeling the intelligence is proposed to study the influence of intelligence on market performance. This modeling together with the characteristics of adaptive learning make our results more convincible.

Suggested Citation

  • Chia-Hsuan Yeh, 2004. "Can Intelligence Help Improve Market Performance?," Computing in Economics and Finance 2004 106, Society for Computational Economics.
  • Handle: RePEc:sce:scecf4:106

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    References listed on IDEAS

    1. Andersson, Michael K. & Gredenhoff, Mikael P., 1997. "Bootstrap Testing for Fractional Integration," SSE/EFI Working Paper Series in Economics and Finance 188, Stockholm School of Economics.
    2. Lo, Andrew W, 1991. "Long-Term Memory in Stock Market Prices," Econometrica, Econometric Society, vol. 59(5), pages 1279-1313, September.
    3. Davidson, Russell & MacKinnon, James G., 1999. "The Size Distortion Of Bootstrap Tests," Econometric Theory, Cambridge University Press, vol. 15(03), pages 361-376, June.
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    More about this item


    Artificial Stock Market; Double Auction; Bid-Ask Spread; Agent-Based Modeling; Boolean Functions; Genetic Programming; Strongly Typed Genetic Programming;

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates


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