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Long-Memory in an Order-Driven Market

By Blake Lebaron and Ryuichi Yamamoto


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. We also discuss why evolutionary dynamics are important in generating these long memory features. 1 1

Year: 2006
DOI identifier: 10.2139/ssrn.942305
OAI identifier: oai:CiteSeerX.psu:
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