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

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

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.

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File URL: http://www.sciencedirect.com/science/article/pii/S0378437102019027
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Article provided by Elsevier in its journal Physica A: Statistical Mechanics and its Applications.

Volume (Year): 324 (2003)
Issue (Month): 1 ()
Pages: 227-233

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Handle: RePEc:eee:phsmap:v:324:y:2003:i:1:p:227-233
DOI: 10.1016/S0378-4371(02)01902-7
Contact details of provider: Web page: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/

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  1. De Long, J Bradford, et al, 1991. "The Survival of Noise Traders in Financial Markets," The Journal of Business, University of Chicago Press, vol. 64(1), pages 1-19, January.
  2. Marco Raberto & Silvano Cincotti & Sergio M. Focardi & Michele Marchesi, 2001. "Agent-based simulation of a financial market," Papers cond-mat/0103600, arXiv.org, revised Mar 2001.
  3. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, 03.
  4. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-72, June.
  5. 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.
  6. 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.
  7. Blume, Lawrence & Easley, David, 1992. "Evolution and market behavior," Journal of Economic Theory, Elsevier, vol. 58(1), pages 9-40, October.
  8. Parkes, David C. & Huberman, Bernardo A., 2001. "Multiagent Cooperative Search for Portfolio Selection," Games and Economic Behavior, Elsevier, vol. 35(1-2), pages 124-165, April.
  9. Tesfatsion, Leigh S., 2002. "Economic Agents and Markets As Emergent Phenomena," Staff General Research Papers Archive 10033, Iowa State University, Department of Economics.
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