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Who wins? Study of long-run trader survival in an artificial stock market

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  • Cincotti, Silvano
  • M. Focardi, Sergio
  • Marchesi, Michele
  • Raberto, Marco

Abstract

We introduce a multi-asset artificial financial market with finite amount of cash and number of stocks. The background trading is characterized by a random trading strategy constrained by the finiteness of resources and by market volatility. Stock price processes exhibit volatility clustering, fat-tailed distribution of returns and reversion to the mean. Three active trading strategies have been introduced and studied in two different market conditions: steady market and growing market with asset inflation. We show that the profitability of each strategy depends both on the periodicity of portfolio reallocation and on the market condition. The best performing strategy is the one that exploits the mean reversion characteristic of asset price processes.

Suggested Citation

  • Cincotti, Silvano & M. Focardi, Sergio & Marchesi, Michele & Raberto, Marco, 2003. "Who wins? Study of long-run trader survival in an artificial stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 324(1), pages 227-233.
    • Handle: RePEc:eee:phsmap:v:324:y:2003:i:1:p:227-233
    DOI: 10.1016/S0378-4371(02)01902-7
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    References listed on IDEAS

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    1. De Long, J Bradford & Shleifer, Andrei & Summers, Lawrence H & Waldmann, Robert J, 1991. "The Survival of Noise Traders in Financial Markets," The Journal of Business, University of Chicago Press, vol. 64(1), pages 1-19, January.
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    4. 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.
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    6. Raberto, Marco & Scalas, Enrico & Cuniberti, Gianaurelio & Riani, Massimo, 1999. "Volatility in the Italian stock market: an empirical study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 269(1), pages 148-155.
    7. Raberto, Marco & Cincotti, Silvano & Focardi, Sergio M. & Marchesi, Michele, 2001. "Agent-based simulation of a financial market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 299(1), pages 319-327.
    8. Tesfatsion, Leigh S., 2002. "Economic Agents and Markets As Emergent Phenomena," Staff General Research Papers Archive 10033, Iowa State University, Department of Economics.
    9. Blume, Lawrence & Easley, David, 1992. "Evolution and market behavior," Journal of Economic Theory, Elsevier, vol. 58(1), pages 9-40, October.
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    Citations

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    Cited by:

    1. Derveeuw, Julien & Beaufils, Bruno & Mathieu, Philippe & Brandouy, Olivier, 2007. "Testing double auction as a component within a generic market model architecture," MPRA Paper 4918, University Library of Munich, Germany.
    2. Marco Raberto & Silvano Cincotti, 2004. "Multi-agent modeling and simulation of a sequential monetary production economy," Computing in Economics and Finance 2004 260, Society for Computational Economics.
    3. Luisanna Cocco & Michele Marchesi, 2016. "Modeling and Simulation of the Economics of Mining in the Bitcoin Market," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-31, October.
    4. Linda Ponta & Silvano Cincotti, 2018. "Traders’ Networks of Interactions and Structural Properties of Financial Markets: An Agent-Based Approach," Complexity, Hindawi, vol. 2018, pages 1-9, January.
    5. Ponta, Linda & Trinh, Mailan & Raberto, Marco & Scalas, Enrico & Cincotti, Silvano, 2019. "Modeling non-stationarities in high-frequency financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 173-196.
    6. Liu, Xinghua & Gregor, Shirley & Yang, Jianmei, 2008. "The effects of behavioral and structural assumptions in artificial stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(11), pages 2535-2546.
    7. Raberto, Marco & Cincotti, Silvano, 2005. "Modeling and simulation of a double auction artificial financial market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 355(1), pages 34-45.
    8. Soufian, Mona & Forbes, William & Hudson, Robert, 2014. "Adapting financial rationality: Is a new paradigm emerging?," CRITICAL PERSPECTIVES ON ACCOUNTING, Elsevier, vol. 25(8), pages 724-742.
    9. Thiago W. Alves & Ionut Florescu & George Calhoun & Dragos Bozdog, 2020. "SHIFT: A Highly Realistic Financial Market Simulation Platform," Papers 2002.11158, arXiv.org, revised Aug 2020.
    10. Luisanna Cocco & Giulio Concas & Michele Marchesi, 2017. "Using an artificial financial market for studying a cryptocurrency market," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 12(2), pages 345-365, July.
    11. Manahov, Viktor & Urquhart, Andrew, 2021. "The efficiency of Bitcoin: A strongly typed genetic programming approach to smart electronic Bitcoin markets," International Review of Financial Analysis, Elsevier, vol. 73(C).
    12. Erika Corona & Sabrina Ecca & Michele Marchesi & Alessio Setzu, 2008. "The Interplay Between Two Stock Markets and a Related Foreign Exchange Market: A Simulation Approach," Computational Economics, Springer;Society for Computational Economics, vol. 32(1), pages 99-119, September.

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