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Finite market size as a source of extreme wealth inequality and market instability

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  • Huang, Zhi-Feng
  • Solomon, Sorin

Abstract

We study the finite-size effects in some scaling systems, and show that the finite number of agents N leads to a cut-off in the upper value of the Pareto law for the relative individual wealth. The exponent α of the Pareto law obtained in stochastic multiplicative market models is crucially affected by the fact that N is always finite in real systems. We show that any finite value of N leads to properties which can differ crucially from the naive theoretical results obtained by assuming an infinite N. In particular, finite N may cause in the absence of an appropriate social policy extreme wealth inequality α<1 and market instability.

Suggested Citation

  • Huang, Zhi-Feng & Solomon, Sorin, 2001. "Finite market size as a source of extreme wealth inequality and market instability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 294(3), pages 503-513.
    • Handle: RePEc:eee:phsmap:v:294:y:2001:i:3:p:503-513
    DOI: 10.1016/S0378-4371(01)00113-3
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    Cited by:

    1. Sorin Solomon & Nataša Golo, 2015. "Microeconomic structure determines macroeconomic dynamics: Aoki defeats the representative agent," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 10(1), pages 5-30, April.
    2. Marco Raberto & Silvano Cincotti & Sergio Focardi & Michele Marchesi, 2003. "Traders' Long-Run Wealth in an Artificial Financial Market," Computational Economics, Springer;Society for Computational Economics, vol. 22(2), pages 255-272, October.
    3. E. Samanidou & E. Zschischang & D. Stauffer & T. Lux, 2007. "Agent-based Models of Financial Markets," Papers physics/0701140, arXiv.org.
    4. Natasa Golo & Guy Kelman & David S. Bree & Leanne Usher & Marco Lamieri & Sorin Solomon, 2015. "Many-to-one contagion of economic growth rate across trade credit network of firms," Papers 1506.01734, arXiv.org.
    5. A. Corcos & J-P Eckmann & A. Malaspinas & Y. Malevergne & D. Sornette, 2002. "Imitation and contrarian behaviour: hyperbolic bubbles, crashes and chaos," Quantitative Finance, Taylor & Francis Journals, vol. 2(4), pages 264-281.
    6. E. Samanidou & E. Zschischang & D. Stauffer & T. Lux, 2001. "Microscopic Models of Financial Markets," Papers cond-mat/0110354, arXiv.org.
    7. Yonatan Berman & Yoash Shapira & Eshel Ben-Jacob, 2015. "Modeling the Origin and Possible Control of the Wealth Inequality Surge," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-21, June.
    8. Max Greenberg & H. Oliver Gao, 2024. "Twenty-five years of random asset exchange modeling," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 97(6), pages 1-27, June.
    9. Marco Airoldi & Vito Antonelli & Bruno Bassetti & Andrea Martinelli & Marco Picariello, 2004. "Long Range Interaction Generating Fat-Tails in Finance," GE, Growth, Math methods 0404006, University Library of Munich, Germany, revised 27 Apr 2004.

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