Learning and Evolution of Trading Strategies in Limit Order Markets
AbstractHow 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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Bibliographic InfoPaper provided by Quantitative Finance Research Centre, University of Technology, Sydney in its series Research Paper Series with number 335.
Date of creation: 01 Aug 2013
Date of revision:
Limit order book; evolution; genetic algorithm learning; asymmetric information; trading strategy;
Find related papers by JEL classification:
- G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
- D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-08-31 (All new papers)
- NEP-CMP-2013-08-31 (Computational Economics)
- NEP-CTA-2013-08-31 (Contract Theory & Applications)
- NEP-MST-2013-08-31 (Market Microstructure)
- NEP-ORE-2013-08-31 (Operations Research)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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