Learning, information processing and order submission in limit order markets

Authored by Lijian Wei, Carl Chiarella, Xue-Zhong He

Date Published: 2015

DOI: 10.1016/j.jedc.2015.09.013

Sponsors: Chinese National Natural Science Foundation Australian Research Council (ARC)

Platforms: No platforms listed

Model Documentation: Other Narrative

Model Code URLs: Model code not found

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. (C) 2015 Elsevier B.V. All rights reserved.

Tags

exchange behavior Liquidity Genetic algorithm Model Rules Financial-markets Traders Book