Computational Economics

, Volume 22, Issue 2–3, pp 255–272 | Cite as

Traders' Long-Run Wealth in an Artificial Financial Market

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


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.

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


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Arthur, W., Holland, J., LeBaron, B., Palmer, R. and Tayler, P. (1997). Asset pricing under endogeneous expectations in an artificial stock market. In W. Arthur, S. Durlauf and D. Lane (eds.), The Economy as an Evolving Complex System II. Addison Wesley Longman.Google Scholar
  2. Beck, K. (1999). Extreme Programming Explained. Embrace Change. Addison-Wesley, Reading, Massachusetts.Google Scholar
  3. Black, F. (1986). Noise. J. Finance, 41(3), 529–543.Google Scholar
  4. Bouchaud, J.-P. and Mezard, M. (2000).Wealth condensation in a simple model of economy. Physica A, 282(3-4), 536–545.Google Scholar
  5. Briys, E. and de Varenne, F. (2000). 1000 years of risk management. Risk, 47–48.Google Scholar
  6. Cont, R. and Bouchaud, J.-P. (2000). Herd behavior and aggregate fluctuations in financial markets. Macroecon. Dyn., 4(2), 170–196.Google Scholar
  7. De Long, J., Shleifer, A., Summers, L. and Waldmann, R. (1990). Noise trader risk in financial markets. J. Polit. Econ., 98(4), 703–738.Google Scholar
  8. De Long, J., Shleifer, A., Summers, L. and Waldmann, R. (1991). The survival of noise traders in financial markets. J. Bus., 64(1), 1–19.Google Scholar
  9. Engle, R. and Granger, C. (1987). Cointegration and error-correction: representation, estimation, and testing. Econometrica, 55, 251–276.Google Scholar
  10. Friedman, M. (1953). The Case for Flexible Exchange Rates. University of Chicago Press, Chicago.Google Scholar
  11. Huang, Z. and Solomon, S. (2001a). Finite market size as a source of extreme wealth inequality and market instability. Physica A, 294, 503–513.Google Scholar
  12. Huang, Z. and Solomon, S. (2001b). Power, Levy, exponential and Gaussian-like regimes in auto catalytic financial systems. Eur. Phys. J. B., 20, 601–607.Google Scholar
  13. LeBaron, B. (1999). Time series properties of artificial stock market. J. Econ. Dyn. Control, 23(9-10), 1487–1516.Google Scholar
  14. LeBaron, B. (2000). Agent-based computational finance: suggested readings and early research. J. Econ. Dyn. Control, 24(5-7), 679–702.Google Scholar
  15. Levy, M., Levy, H. and Solomon, S. (2000). Microscopic Simulation of FinancialMarkets. Academic Press, New York.Google Scholar
  16. Levy, M. and Solomon, S. (1997). New evidence in the power-law distribution of wealth. Physica A, 242(1-2), 90–94.Google Scholar
  17. Liu, Y., Gopikrishnan, P., Cizeau, P., Meyer, M., Peng, C. and Stanley, H. (1999). Statistical properties of the volatility of price fluctuations. Phys. Rev. E, 60(2), 1390–1400.Google Scholar
  18. Lux, T. (1997). Time variation of second moments from a noise trader infection model. J. Econ. Dyn. Control, 22(1), 1–38.Google Scholar
  19. Lux, T. and Marchesi, M. (1999). Scaling and criticality in a stochastic multi-agent model of a financial market. Nature, 397(6718), 498–500.Google Scholar
  20. Mantegna, R. and Stanley, H. (1999). An Introduction to Econophysics: Correlations and Complexity in Finance. Cambridge University Press, Cambridge, U.K.Google Scholar
  21. Montroll, E. and Schlesinger, M. (1982). On 1/f noise and other distributions with long tails. P. Natl. Acad. Sci. U.S.A., 79, 3380–3383.Google Scholar
  22. O'Hara, M. (1995). Market Microstructure Theory. Blackwell, Oxford, U.K.Google Scholar
  23. Palmer, R., Arthur, W., Holland, J., LeBaron, B. and Tayler, P. (1994). Artificial economic life: a simple model of a stock market. Physica D, 75, 264–274.Google Scholar
  24. Pareto, V. (1897). Course d'Economie Politique. Lausanne, CH.Google Scholar
  25. Raberto, M., Cincotti, S., Focardi, S. and Marchesi, M. (2001). Agent-based simulation of a financial market. Physica A, 219, 319–327.Google Scholar
  26. Roman, H., Porto, M. and Giovanardi, N. (2001). Anomalous scaling of stock price dynamics within ARCH-models. Eur. Phys. J. B., 21(2), 155–158.Google Scholar
  27. Succi, G. and Marchesi, M. (2001). Extreme Programming Examined. Addison-Wesley, Reading, Massachusetts.Google Scholar
  28. Takayasu, H. (1990). Fractals in the Physical Sciences. John Wiley and Sons, New York.Google Scholar
  29. Wild, C. and Seber, G. (2000). Chance Encounters. A First Course in Data Analysis and Inference. John Wiley and Sons, New York.Google Scholar

Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  • Marco Raberto
    • 1
  • Silvano Cincotti
    • 1
  • Sergio M. Focardi
    • 2
  • Michele Marchesi
    • 3
  1. 1.DIBEUniversity of GenovaGenovaItaly
  2. 2.The Intertek GroupParisFrance
  3. 3.DIEEUniversity of CagliariCagliariItaly

Personalised recommendations