Noise traders in an agent-based artificial stock market

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.

Automated summary

This paper investigates the long-term survival of noise traders and their influence on financial markets by proposing an agent-based artificial stock market. The market consists of noise traders, informed and uninformed traders. Informed and uninformed traders can learn from information using Genetic Programming, while noise traders cannot. The system is calibrated to real financial markets and replicates several stylized facts. The findings suggest that noise traders either cannot survive in the long run or transform into other types of traders, and they increase market volatility, price distortion, noise trader risk, and trading volume. Regulatory interventions, such as price limits, transaction tax, and longer settlement cycles, can affect the survival period of noise traders and their influence on markets.