Noise traders in an agent-based artificial stock market

Xiaoting Dai, Jie Zhang, Victor Chang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
JournalAnnals of Operations Research
Publication statusE-pub ahead of print - 2023


  • Agent-based modeling
  • Computational economics
  • Noise traders
  • Price limits
  • Risk and volatility dynamics
  • Simulation models
  • The settlement cycle
  • Transaction tax


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