Learning, information processing and order submission in limit order markets

Carl Chiarella, Xue Zhong He, Lijian Wei*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)

Abstract

By introducing a genetic algorithm learning with a classifier system into a limit order market, this paper provides a unified framework of microstructure and agent-based models of limit order markets that allows traders to determine their order submission endogenously according to market conditions. It examines how traders process and learn from market information and how the learning affects limit order markets. It is found that, measured by the average usage of different group of market information, trading rules under the learning become stationary in the long run. Also informed traders pay more attention to the last transaction sign while uninformed traders pay more attention to technical rules. Learning of uninformed traders improves market information efficiency, but not necessarily when informed traders learn. Opposite to the learning of informed traders, learning makes uninformed traders submit less aggressive limit orders and more market orders. Furthermore private values can have significant impact in the short run, but not in the long run. One implication is that the probability of informed trading (PIN) is positively related to the volatility and the bid-ask spread.

Original languageEnglish
Pages (from-to)245-268
Number of pages24
JournalJournal of Economic Dynamics and Control
Volume61
DOIs
Publication statusPublished - Dec 2015
Externally publishedYes

Keywords

  • Asymmetric information
  • Genetic algorithm learning
  • Limit order book
  • Order submission
  • Probability of informed trading

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