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Traders’ long-run wealth in an artificial financial market

Author

Listed:
  • Marco Raberto
  • Silvano Cincott
  • Sergio M. Focardi
  • Michele Marchesi

Abstract

In this paper, we study the long-run wealth distribution of agents with different trading strategies in the framework of the Genoa Artificial Stock Market.The Genoa market is an agent-based simulated market able to reproduce the main stylised facts observed in financial markets, i.e., fat-tailed distribution of returns and volatility clustering. Various populations of traders have been introduced in a`thermal bath' made by random traders who make random buy and sell decisions constrained by the available limited resources and depending on past price volatility. We study both trend following and trend contrarian behaviour; fundamentalist traders (i.e., traders believing that stocks have a fundamental price depending on factors external to the market) are also investigated. Results show that the strategy alone does not allow forecasting which population will prevail. Trading strategies yield different results in different market conditions. Generally, in a closed market (a market with no money creation process), we find that trend followers lose relevance and money to other populations of traders and eventually disappear, whereas in an open market (a market with money inflows), trend followers can survive, but their strategy is less profitable than the strategy of other populations. Copyright Kluwer Academic Publishers 2003
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Suggested Citation

  • Marco Raberto & Silvano Cincott & Sergio M. Focardi & Michele Marchesi, 2002. "Traders’ long-run wealth in an artificial financial market," Computing in Economics and Finance 2002 301, Society for Computational Economics.
  • Handle: RePEc:sce:scecf2:301
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    Citations

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    Cited by:

    1. J. Emeterio Navarro-Barrientos & Frank E. Walter & Frank Schweitzer, 2008. "Risk-Seeking Versus Risk-Avoiding Investments In Noisy Periodic Environments," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 19(06), pages 971-994.
    2. Mikhail Goykhman, 2017. "Wealth dynamics in a sentiment-driven market," Papers 1705.07092, arXiv.org.
    3. 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.
    4. Lavička, H. & Lichard, T. & Novotný, J., 2016. "Sand in the wheels or wheels in the sand? Tobin taxes and market crashes," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 328-342.
    5. Navarro-Barrientos, Jesús Emeterio & Cantero-Álvarez, Rubén & Matias Rodrigues, João F. & Schweitzer, Frank, 2008. "Investments in random environments," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(8), pages 2035-2046.
    6. Sabrina Ecca & Michele Marchesi & Alessio Setzu, 2008. "Modeling and Simulation of an Artificial Stock Option Market," Computational Economics, Springer;Society for Computational Economics, vol. 32(1), pages 37-53, September.
    7. Cross, Rod & Grinfeld, Michael & Lamba, Harbir & Seaman, Tim, 2005. "A threshold model of investor psychology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 354(C), pages 463-478.
    8. Daniel Fricke & Thomas Lux, 2015. "The effects of a financial transaction tax in an artificial financial market," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 10(1), pages 119-150, April.
    9. 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.
    10. Alfarano, Simone & Lux, Thomas & Wagner, Friedrich, 2008. "Time variation of higher moments in a financial market with heterogeneous agents: An analytical approach," Journal of Economic Dynamics and Control, Elsevier, vol. 32(1), pages 101-136, January.
    11. Luisanna Cocco & Michele Marchesi, 2016. "Modeling and Simulation of the Economics of Mining in the Bitcoin Market," Papers 1605.01354, arXiv.org.
    12. Recchioni, Maria Cristina & Tedeschi, Gabriele & Gallegati, Mauro, 2015. "A calibration procedure for analyzing stock price dynamics in an agent-based framework," Journal of Economic Dynamics and Control, Elsevier, vol. 60(C), pages 1-25.
    13. Olivier Brandouy & Philippe Mathieu & Iryna Veryzhenko, 2012. "Risk Aversion Impact on Investment Strategy Performance: A Multi Agent-Based Analysis," Post-Print hal-00826144, HAL.
    14. Mannaro, Katiuscia & Marchesi, Michele & Setzu, Alessio, 2008. "Using an artificial financial market for assessing the impact of Tobin-like transaction taxes," Journal of Economic Behavior & Organization, Elsevier, vol. 67(2), pages 445-462, August.
    15. Victor M. Yakovenko & J. Barkley Rosser, 2009. "Colloquium: Statistical mechanics of money, wealth, and income," Papers 0905.1518, arXiv.org, revised Dec 2009.
    16. Manahov, Viktor & Hudson, Robert & Linsley, Philip, 2014. "New evidence about the profitability of small and large stocks and the role of volume obtained using Strongly Typed Genetic Programming," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 33(C), pages 299-316.
    17. 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.
    18. Luisanna Cocco & Giulio Concas & Michele Marchesi, 2014. "Using an Artificial Financial Market for studying a Cryptocurrency Market," Papers 1406.6496, arXiv.org.
    19. Marco Raberto & Andrea Teglio & Silvano Cincotti, 2008. "Integrating Real and Financial Markets in an Agent-Based Economic Model: An Application to Monetary Policy Design," Computational Economics, Springer;Society for Computational Economics, vol. 32(1), pages 147-162, September.
    20. repec:eee:phsmap:v:488:y:2017:i:c:p:132-148 is not listed on IDEAS
    21. 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.

    More about this item

    Keywords

    artificial financial markets; market simulations; wealth distribution; trading strategies; trading behaviour; asset prices; econophysics;

    JEL classification:

    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General

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