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Majority-vote model for financial markets

Author

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  • Vilela, André L.M.
  • Wang, Chao
  • Nelson, Kenric P.
  • Stanley, H. Eugene

Abstract

We use a heterogeneous agent-based two-state sociophysics model to simulate financial markets. Focusing on stock market trader dynamics, we propose a model with two kinds of individual – the contrarian agent and the noise trader – in which the dynamics of buying and selling investors are governed by local and global interactions. We define an antiferromagnetic coupling that relates the option of contrarian agents to global magnetization and a ferromagnetic interaction that connects noise traders to their local neighborhood. Our model presents such stylized facts of real financial markets as clustered volatility, power-law distributed returns, and the long-time correlation of the absolute returns with exponential decay. We also observe that the distribution of logarithmic returns can be fitted by the Student’s t distribution in which its degree of freedom changes with the percentage of contrarian agents in the market.

Suggested Citation

  • Vilela, André L.M. & Wang, Chao & Nelson, Kenric P. & Stanley, H. Eugene, 2019. "Majority-vote model for financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 762-770.
    • Handle: RePEc:eee:phsmap:v:515:y:2019:i:c:p:762-770
    DOI: 10.1016/j.physa.2018.10.007
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    References listed on IDEAS

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    Cited by:

    1. Quanbo Zha & Gang Kou & Hengjie Zhang & Haiming Liang & Xia Chen & Cong-Cong Li & Yucheng Dong, 2020. "Opinion dynamics in finance and business: a literature review and research opportunities," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-22, December.
    2. Gao, Yang & Li, Yunhai & Wang, Yaojun & Wang, Chao & Liu, Chao, 2019. "Asymptotic comparison of three spread estimators based on Roll’s model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 420-432.
    3. Bornholdt, Stefan, 2022. "A q-spin Potts model of markets: Gain–loss asymmetry in stock indices as an emergent phenomenon," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 588(C).
    4. Nelson, Kenric P., 2022. "Independent Approximates enable closed-form estimation of heavy-tailed distributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 601(C).
    5. Peralta, Antonio F. & Khalil, Nagi & Toral, Raúl, 2020. "Ordering dynamics in the voter model with aging," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 552(C).
    6. Zubillaga, Bernardo J. & Vilela, André L.M. & Wang, Chao & Nelson, Kenric P. & Stanley, H. Eugene, 2022. "A three-state opinion formation model for financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 588(C).
    7. Yuan, Qianshun & Semba, Sherehe & Zhang, Jing & Weng, Tongfeng & Gu, Changgui & Yang, Huijie, 2021. "Multi-scale transition matrix approach to time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    8. Oliveira, Igor V.G. & Wang, Chao & Dong, Gaogao & Du, Ruijin & Fiore, Carlos E. & Vilela, André L.M. & Stanley, H. Eugene, 2024. "Entropy production on cooperative opinion dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    9. Stefan Bornholdt, 2021. "A q-spin Potts model of markets: Gain-loss asymmetry in stock indices as an emergent phenomenon," Papers 2112.06290, arXiv.org.
    10. Dimitris Tsintsaris & Milan Tsompanoglou & Evangelos Ioannidis, 2024. "Dynamics of Social Influence and Knowledge in Networks: Sociophysics Models and Applications in Social Trading, Behavioral Finance and Business," Mathematics, MDPI, vol. 12(8), pages 1-27, April.
    11. Pathak, Seemantini & Chiu, Shih-Chi (Sana), 2020. "Firm-advisor ties and financial performance in the context of corporate divestiture," Journal of Business Research, Elsevier, vol. 121(C), pages 315-328.
    12. Fraiman, Nicolas & Lin, Tzu-Chi & Olvera-Cravioto, Mariana, 2023. "Stochastic recursions on directed random graphs," Stochastic Processes and their Applications, Elsevier, vol. 166(C).
    13. Trinidad Segovia, J.E. & Fernández-Martínez, M. & Sánchez-Granero, M.A., 2019. "A novel approach to detect volatility clusters in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).

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