The effect of genetic algorithm learning with a classifier system in limit order markets. (October 2017)

• Record Type: Journal Article
• Authors: Wei, Lijian; Xiong, Xiong; Zhang, Wei; He, Xue-Zhong; Zhang, Yongjie
• Is Part Of: Engineering applications of artificial intelligence. Volume 65(2017:May)
• Page Start: 436
• Page End: 448
• Publication Date: 2017-10
• Subjects: G14 -- C63 -- D82; Limit order book -- Asymmetric information -- Genetic algorithm learning -- Classifier system -- Order submission; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2017.07.006
• Languages: English
• ISSNs: 0952-1976

Abstract

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.