TY - JOUR
T1 - Noise traders in an agent-based artificial stock market
AU - Dai, Xiaoting
AU - Zhang, Jie
AU - Chang, Victor
N1 - Funding Information:
The authors would like to thank Professor Victor Chang for the research support from VC Research (VCR 0000146) and acknowledge the support of Humanities and Social Sciences Youth Foundation, Ministry of Education of the People’s Republic of China [award number: 22YJC790161].
Publisher Copyright:
© 2023, The Author(s).
PY - 2023/8/7
Y1 - 2023/8/7
N2 - 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.
AB - 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.
KW - Agent-based modeling
KW - Computational economics
KW - Noise traders
KW - Price limits
KW - Risk and volatility dynamics
KW - Simulation models
KW - The settlement cycle
KW - Transaction tax
UR - http://www.scopus.com/inward/record.url?scp=85166941797&partnerID=8YFLogxK
U2 - 10.1007/s10479-023-05528-7
DO - 10.1007/s10479-023-05528-7
M3 - Article
AN - SCOPUS:85166941797
SN - 0254-5330
JO - Annals of Operations Research
JF - Annals of Operations Research
ER -