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Can Artificial Traders Learn and Err Like Human Traders? A New Direction for Computational Intelligence in Behavioral Finance

Chapter
Part of the Springer Optimization and Its Applications book series (SOIA, volume 70)

Abstract

The microstructure of markets involves not only human traders’ learning and erring processes but also their heterogeneity. Much of this part has not been taken into account in the agent-based artificial markets, despite the fact that various computational intelligence tools have been applied to artificial-agent modeling. One possible reason for this little progress is due to the lack of good-quality data by which the learning and erring patterns of human traders can be easily archived and analyzed. In this chapter, we take a pioneering step in this direction by, first, conducting double auction market experiments and obtaining a dataset involving about 165 human traders. The controlled laboratory setting then enables us to anchor the observing trading behavior of human traders to a benchmark (a global optimum) and to develop a learning index by which the learning and erring patterns can be better studied, in particular, in light of traders’ personal attributes, such as their cognitive capacity and personality. The behavior of artificial traders driven by genetic programming (GP) is also studied in parallel to human traders; however, how to represent the observed heterogeneity using GP remains a challenging issue.

Keywords

Genetic Programming Work Memory Capacity Cognitive Capacity Artificial Agent Reservation Price 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The authors are grateful to Professor Kuo-Shu Yang for his generous permission for using his developed Chinese version of the Big-Five personality test. We are also grateful to Professor Li-Jen Weng and Professor Lei-Xieng Yang for their advice and guidance on the psychological tests implemented in this study. NSC research grants no. 98-2410-H-004-045-MY3, no. 99-2811-H-004-014, and no. 100-2410-H-029-001 are also gratefully acknowledged.

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Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  • Shu-Heng Chen
    • 1
  • Kuo-Chuan Shih
    • 1
  • Chung-Ching Tai
    • 2
  1. 1.AIECON Research Center, Department of EconomicsNational Chengchi UniversityChengchiTaiwan
  2. 2.Department of EconomicsTunghai UniversityTunghaiTaiwan

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