Long-memory in an order-driven market

Blake LeBaron, Ryuichi Yamamoto

Research output: Contribution to journalArticle

44 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)85-89
Number of pages5
JournalPhysica A: Statistical Mechanics and its Applications
Volume383
Issue number1 SPEC. ISS.
DOIs
Publication statusPublished - 2007 Sep 1
Externally publishedYes

Fingerprint

Long Memory
learning
volatility
Stock Returns
Evolutionary Dynamics
Volatility
Market
simulation
Learning
Simulation

Keywords

  • Agent-based
  • Long-memory
  • Microstructure
  • Order flow

ASJC Scopus subject areas

  • Mathematical Physics
  • Statistical and Nonlinear Physics

Cite this

Long-memory in an order-driven market. / LeBaron, Blake; Yamamoto, Ryuichi.

In: Physica A: Statistical Mechanics and its Applications, Vol. 383, No. 1 SPEC. ISS., 01.09.2007, p. 85-89.

Research output: Contribution to journalArticle

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