Learning, information processing and order submission in limit order markets

Publication Type:
Journal Article
Citation:
Journal of Economic Dynamics and Control, 2015, 61 pp. 245 - 268
Issue Date:
2015-01-01
Full metadata record
Files in This Item:
Filename Description Size
ThumbnailEvolution_Reversion4.pdfAccepted Manuscript version640.39 kB
Adobe PDF
© 2015 Elsevier B.V. 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.
Please use this identifier to cite or link to this item: