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Noise traders in an agent-based artificial stock market

Author

Listed:
  • Xiaoting Dai

    (Jiangnan University)

  • Jie Zhang

    (Xi’an Jiaotong Liverpool University)

  • Victor Chang

    (Aston University)

Abstract

This paper investigates whether noise traders can survive in the long run and how they influence financial markets by proposing an agent-based artificial stock market, as one simulation model of computational economics. This market contains noise traders, informed and uninformed traders. Informed and uninformed traders can learn from information by using Genetic Programming, while noise traders cannot. The system is first calibrated to real financial markets by replicating several stylized facts. We find that noise traders cannot survive or they just transform to other kind of traders in the long run, and they increase market volatility, price distortion, noise trader risk, and trading volume in the market. However, regulation intervention, e.g., price limits, transaction tax and longer settlement cycle, can affect noise trader’s surviving period and their influence on markets.

Suggested Citation

  • Xiaoting Dai & Jie Zhang & Victor Chang, 2025. "Noise traders in an agent-based artificial stock market," Annals of Operations Research, Springer, vol. 352(3), pages 715-744, September.
    • Handle: RePEc:spr:annopr:v:352:y:2025:i:3:d:10.1007_s10479-023-05528-7
    DOI: 10.1007/s10479-023-05528-7
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