The effect of genetic algorithm learning with a classifier system in limit order markets

Lijian Wei, Xiong Xiong, Wei Zhang, Xuezhong (Tony) He () and Yongjie Zhang
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Lijian Wei: Business School, Sun Yat-Sen University
Xiong Xiong: College of Management and Economics, Tianjin University
Wei Zhang: College of Management and Economics, Tianjin University
Yongjie Zhang: College of Management and Economics, Tianjin University

Published Paper Series from Finance Discipline Group, UTS Business School, University of Technology, Sydney

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.

Keywords: Limit order book; Asymmetric information; Genetic algorithm learning; Classifier system; Order submission (search for similar items in EconPapers)
JEL-codes: C63 D82 G14 (search for similar items in EconPapers)
Pages: 13 pages
Date: 2017-01-01
New Economics Papers: this item is included in nep-cmp and nep-mst
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Citations: View citations in EconPapers (2)

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