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Long-memory in an order-driven market

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  • LeBaron, Blake
  • Yamamoto, Ryuichi

Abstract

This paper introduces an order-driven market with heterogeneous investors, who submit limit or market orders according to their own trading rules. The trading rules are repeatedly updated via simple learning and adaptation of the investors. We analyze markets with and without learning and adaptation. The simulation results show that our model with learning and adaptation successfully replicates long-memories in trading volume, stock return volatility, and signs of market orders in an informationally efficient market. We also discuss why evolutionary dynamics are important in generating these features.

Suggested Citation

  • LeBaron, Blake & Yamamoto, Ryuichi, 2007. "Long-memory in an order-driven market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 383(1), pages 85-89.
    • Handle: RePEc:eee:phsmap:v:383:y:2007:i:1:p:85-89
    DOI: 10.1016/j.physa.2007.04.090
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    References listed on IDEAS

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