Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4942615 | Engineering Applications of Artificial Intelligence | 2017 | 13 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Lijian Wei, Xiong Xiong, Wei Zhang, Xue-Zhong He, Yongjie Zhang,