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Behavioral Equilibrium and Evolutionary Dynamics in Asset Markets

Author

Listed:
  • Igor V. Evstigneev

    (University of Manchester - Economics, School of Social Sciences)

  • Thorsten Hens

    (University of Zurich - Department of Banking and Finance; Norwegian School of Economics and Business Administration (NHH); Swiss Finance Institute)

  • Valeriya Potapova

    (University of Manchester - Economics, School of Social Sciences)

  • Klaus Reiner Schenk-Hoppé

    (University of Manchester - Department of Economics; Norwegian School of Economics (NHH) - Department of Finance)

Abstract

This paper analyzes a dynamic stochastic equilibrium model of an asset market based on behavioral and evolutionary principles. The core of the model is a non-traditional game-theoretic framework combining elements of stochastic dynamic games and evolutionary game theory. Its key characteristic feature is that it relies only on objectively observable market data and does not use hidden individual agents' characteristics (such as their utilities and beliefs). A central goal of the study is to identify an investment strategy that allows an investor to survive in the market selection process, i.e., to keep with probability one a strictly positive, bounded away from zero share of market wealth over an infinite time horizon, irrespective of the strategies used by the other players. The main results show that under very general assumptions, such a strategy exists, is asymptotically unique and easily computable.

Suggested Citation

  • Igor V. Evstigneev & Thorsten Hens & Valeriya Potapova & Klaus Reiner Schenk-Hoppé, 2020. "Behavioral Equilibrium and Evolutionary Dynamics in Asset Markets," Swiss Finance Institute Research Paper Series 20-19, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp2019
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    Cited by:

    1. Mikhail Zhitlukhin, 2022. "Optimal growth strategies for a representative agent in a continuous-time asset market," Papers 2211.05316, arXiv.org.
    2. Hirshleifer, David & Lo, Andrew W. & Zhang, Ruixun, 2023. "Social contagion and the survival of diverse investment styles," Journal of Economic Dynamics and Control, Elsevier, vol. 154(C).
    3. Zerong Chen, 2025. "Evolutionary Finance: Models with Long-Lived Assets," Economics Discussion Paper Series 2501, Economics, The University of Manchester.
    4. Gong, Qingbin & Diao, Xundi, 2023. "The impacts of investor network and herd behavior on market stability: Social learning, network structure, and heterogeneity," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1388-1398.
    5. Rabah Amir & Igor V. Evstigneev & Valeriya Potapova, 2024. "Unbeatable strategies," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 77(4), pages 891-920, June.
    6. Pastushkov, A., 2025. "Evolutionary and agent-based computational finance: The new paradigms for asset pricing," Journal of the New Economic Association, New Economic Association, vol. 66(1), pages 196-222.
    7. I. V. Evstigneev & T. Hens & M. J. Vanaei, 2023. "Evolutionary finance: a model with endogenous asset payoffs," Journal of Bioeconomics, Springer, vol. 25(2), pages 117-143, August.
    8. Mikhail Zhitlukhin, 2021. "Capital growth and survival strategies in a market with endogenous prices," Papers 2101.09777, arXiv.org.

    More about this item

    Keywords

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    JEL classification:

    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • D53 - Microeconomics - - General Equilibrium and Disequilibrium - - - Financial Markets
    • D58 - Microeconomics - - General Equilibrium and Disequilibrium - - - Computable and Other Applied General Equilibrium Models
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G02 - Financial Economics - - General - - - Behavioral Finance: Underlying Principles

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