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Wealth share analysis with "fundamentalist/chartist" heterogeneous agents

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Listed:
  • Hai-Chuan Xu

    (TJU)

  • Wei Zhang

    (TJU)

  • Xiong Xiong

    (TJU)

  • Wei-Xing Zhou

    (ECUST)

Abstract

We build a multiassets heterogeneous agents model with fundamentalists and chartists, who make investment decisions by maximizing the constant relative risk aversion utility function. We verify that the model can reproduce the main stylized facts in real markets, such as fat-tailed return distribution and long-term memory in volatility. Based on the calibrated model, we study the impacts of the key strategies' parameters on investors' wealth shares. We find that, as chartists' exponential moving average periods increase, their wealth shares also show an increasing trend. This means that higher memory length can help to improve their wealth shares. This effect saturates when the exponential moving average periods are sufficiently long. On the other hand, the mean reversion parameter has no obvious impacts on wealth shares of either type of traders. It suggests that no matter whether fundamentalists take moderate strategy or aggressive strategy on the mistake of stock prices, it will have no different impact on their wealth shares in the long run.

Suggested Citation

  • Hai-Chuan Xu & Wei Zhang & Xiong Xiong & Wei-Xing Zhou, 2014. "Wealth share analysis with "fundamentalist/chartist" heterogeneous agents," Papers 1405.5939, arXiv.org.
    • Handle: RePEc:arx:papers:1405.5939
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    References listed on IDEAS

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    Cited by:

    1. Antoine Kopp & Rebecca Westphal & Didier Sornette, 2022. "Agent-based model generating stylized facts of fixed income markets," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 17(4), pages 947-992, October.
    2. Gao-Feng Gu & Xiong Xiong & Hai-Chuan Xu & Wei Zhang & Yongjie Zhang & Wei Chen & Wei-Xing Zhou, 2021. "An empirical behavioral order-driven model with price limit rules," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-24, December.
    3. Mario A Bertella & Felipe R Pires & Henio H A Rego & Jonathas N Silva & Irena Vodenska & H Eugene Stanley, 2017. "Confidence and self-attribution bias in an artificial stock market," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-20, February.

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