Long-memory in an order-driven market

Blake LeBaron, Ryuichi Yamamoto

研究成果: Article査読

47 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)85-89
ページ数5
ジャーナルPhysica A: Statistical Mechanics and its Applications
383
1 SPEC. ISS.
DOI
出版ステータスPublished - 2007 9 1
外部発表はい

ASJC Scopus subject areas

  • 統計学および確率
  • 凝縮系物理学

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