Shu-Heng Chen
ABSTRACT
We investigate the dynamics of trader behaviors using a co-evolutionary genetic programming system to simulate a double-auction market. The objective of this study is two-fold. First, we seek to evaluate how, if any, the difference in trader rationality/intelligence influences trading behavior. Second, besides rationality, we also analyze how, if any, the co-evolution between two learnable traders impacts their trading behaviors. We have found that traders with different degrees of rationality may exhibit different behavior depending on the type of market they are in. When the market has a profit zone to explore, the more intelligent trader demonstrate more intelligent behaviors. Also, when the market has two learnable buyers, their co-evolution produced more profitable transactions than when there was only one learnable buyer in the market. We have analyzed the learnable traders' strategies and found their behavior are very similar to humans in decision making. We will conduct human subject experiments to validate these results in the near future.
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Cross Ref
- S.-H. Chen and C.-C. Tai. Trading restrictions, price dynamics and allocative efficiency in double auction markets: an analysis based on agent-based modeling and simulations. Advances in Complex Systems, 6(3):283 -- 302, 2003.Google ScholarCross Ref
- S.-H. Chen, R.-J. Zeng, and T. Yu. Co-evolving trading strategies to analyze bounded rationality in double auction markets. In R. Riolo, T. Soule and B. Worzel, editors, Genetic Programming: Theory and Practice VI, pages 195 -- 213. Springer, 2009.Google Scholar
- S.-H. Chen, R.-J. Zeng, and T. Yu. Bounded Rationality and Market Micro-Behaviors: Case Studies Based on Agent-Based Double Auction Markets submitted, 2009.Google Scholar
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Index Terms
- Analysis of micro-behavior and bounded rationality in double auction markets using co-evolutionary GP
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