Risk Preference and Survival Dynamics

  • Shu-Heng Chen
  • Ya-Chi Huang
Part of the Agent-Based Social Systems book series (ABSS, volume 1)


Using an agent-based multi-asset artificial stock market, we simulate the survival dynamics of investors with different risk preferences. It is found that the survivability of investors is closely related to their risk preferences. Among the eight types of investors considered in this paper, only the CRRA investors with RRA coefficients close to one can survive in the long run. Other types of agents are eventually driven out of the market, including the famous CARA agents and agents who base their decision on the capital asset pricing model.

Key words

Market selection hypothesis Agent-based artificial stock markets Autonomous agents Genetic algorithms 


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  1. 1.
    Blume E, Easley E (1992) Evolution and Market Behavior. Journal of Economic Theory 58: 9–40.MathSciNetCrossRefGoogle Scholar
  2. 2.
    Blume E, Easley E (2003) If You’re So Smart, Why Aren’t You Rich? Belief Selection in Complete and Incomplete Markets. Working paper.Google Scholar
  3. 3.
    Bullard J, Duffy J (1999) Using Genetic Algorithms to Model the Evolution of Heterogeneous Beliefs. Computational Economics 13(1): 41–60.CrossRefGoogle Scholar
  4. 4.
    Chen S-H, Huang Y-C (2004) Risk Preference, Forecasting Accuracy and Survival Dynamics: Simulations Based on a Multi-Asset Agent-Based Artificial Stock Market. Working Paper Series 2004-1, AI-ECON Research Center, National Chengchi University.Google Scholar
  5. 5.
    Grossman SJ, Stiglitz J (1980) On the Impossibility of Informationally Efficienct Markets. American Economic Review 70: 393–408.Google Scholar
  6. 6.
    Huang C-F, Litzenberger RH (1988) Foundations for Financial Economics. Prentice Hall, North-Holland New York.Google Scholar
  7. 7.
    Izumi K, Nakamura S, Ueda K (2004) Development of an Artificial Market Model Based on a Field Study. Information Sciences, forthcoming.Google Scholar
  8. 8.
    Kelly JL (1956) A New Interpretatatin of Information Rate. Bell System Technical Journal 35: 917–926.MathSciNetGoogle Scholar
  9. 9.
    Lucas R (1986) Adaptive Behaviour and Economic Theory. In: Hogarth R, Reder M (eds) Rational Choice: The Contrast between Economics and Psychology. University of Chicago Press: 217–242.Google Scholar
  10. 10.
    Sandroni A (2000) Do Markets Favor Agents Able to Make Accurate Predictions? Econometrica 68: 1303–1341.zbMATHMathSciNetCrossRefGoogle Scholar
  11. 11.
    Sciubba E (1999) The Evolution of Portfolio Rules and the Capital Asset Pricing Model. DAE Working Paper No. 9909, University of Cambridge.Google Scholar
  12. 12.
    Tesfatsion L (2001) Introduction to the Special Issue on Agent-Based Computational Economics. Journal of Economic Dynamics and Control 25: 281–293.zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Tokyo 2005

Authors and Affiliations

  • Shu-Heng Chen
    • 1
  • Ya-Chi Huang
    • 1
  1. 1.AI-ECON Research Center, Department of EconomicsNational Chengchi UniversityTaipeiTaiwan

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