Noise traders in an agent-based artificial stock market

Xiaoting Dai; Jie Zhang; Victor Chang

doi:10.1007/s10479-023-05528-7

Noise traders in an agent-based artificial stock market

Xiaoting Dai, Jie Zhang, Victor Chang

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Number of pages	30
Journal	Annals of Operations Research
Early online date	7 Aug 2023
DOIs	https://doi.org/10.1007/s10479-023-05528-7
Publication status	E-pub ahead of print - 7 Aug 2023

Bibliographical note

Funding 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].
Copyright © The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Keywords

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

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10.1007/s10479-023-05528-7Licence: CC BY 4.0

noise traders
© The Author(s) 2023 This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/
Final published version, 2.08 MBLicence: CC BY 4.0

Cite this

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title = "Noise traders in an agent-based artificial stock market",

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{\textquoteright}s surviving period and their influence on markets.",

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author = "Xiaoting Dai and Jie Zhang and Victor Chang",

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PY - 2023/8/7

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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.

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KW - Risk and volatility dynamics

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Noise traders in an agent-based artificial stock market

Abstract

Bibliographical note

Keywords

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