Learning and Evolution of Trading Strategies in Limit Order Markets
How do traders process and learn from market information, what trading strategies should they use, and how does learning affect the market? This paper proposes a learning model of an arti cial limit order market with asymmetric information to address these issues. Using a genetic algorithm as a learning mechanism, we show that learning, in particular the learning from uninformed traders, improves market informational efficiency and has a significant impact on the stylized facts of limit order markets, order submission, liquidity supply and consumption, the hump shaped order book near the quote, and the bid-ask spread. Moreover, the learning affects the evolution process of the trading strategies for all traders. The model provides some insights into market efficiency, the interaction of traders, the dynamics of limit order books, and the evolution of trading strategies.
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- Lijian Wei & Wei Zhang & Xue-Zhong He & Yongjie Zhang, 2013. "Learning and Information Dissemination in Limit Order Markets," Research Paper Series 333, Quantitative Finance Research Centre, University of Technology, Sydney.
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- Carl Chiarella & Giulia Iori, 2005. "The Impact of Heterogeneous Trading Rules on the Limit Order Book and Order Flows," Research Paper Series 152, Quantitative Finance Research Centre, University of Technology, Sydney.
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"Limit-Order Submission Strategies under Asymmetric Information,"
CESifo Working Paper Series
3054, CESifo Group Munich.
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"Estimating the Gains from Trade in Limit-Order Markets,"
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American Finance Association, vol. 61(6), pages 2753-2804, December.
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