The effect of genetic algorithm learning with a classifier system in limit order markets

Lijian Wei, Xiong Xiong*, Wei Zhang, Xue Zhong He, Yongjie Zhang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


By introducing a genetic algorithm with a classifier system as a learning mechanism for uninformed traders into a dynamic limit order market with asymmetric information, this paper examines the effect of the learning on traders’ trading behavior, market liquidity and efficiency. We show that the learning is effective and valuable with respect to information acquisition, forecasting, buy–sell order choice accuracies, and profit opportunity for uninformed traders. It improves information dissemination efficiency and reduces the information advantage of informed traders and hence the value of the private information. In particular, the learning and information become more valuable with higher volatility, less informed traders, and longer information lag. Furthermore, the learning makes not only uninformed but also informed traders submit more limit orders and hence increases market liquidity supply.

Original languageEnglish
Pages (from-to)436-448
Number of pages13
JournalEngineering Applications of Artificial Intelligence
Publication statusPublished - Oct 2017
Externally publishedYes


  • Asymmetric information
  • Classifier system
  • Genetic algorithm learning
  • Limit order book
  • Order submission


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