Simulating the Evolution of Portfolio Behavior in a Multiple-Asset Agent-Based Artificial Stock Market

[Blume and Easley (1992)] show that if agents have the same savings rule, an expected discounted logarithmic utility maximizer with correct beliefs will dominate. If no agent adopts this rule, then agents with incorrect beliefs, but equally averse to risk as logarithmic utility maximizers, may eventually hold more wealth than the agent with correct beliefs. In other words, a trader with correct beliefs can be driven out of the market by traders with incorrect beliefs. However, [Sandroni (2000)] shows that, among agents who have the same intertemporal discount factor and who choose savings endogenously, the most prosperous will be those making accurate predictions. Agents with inaccurate predictions will be driven out of the market regardless of their preferences. By using the extended agent-based artificial stock market, we simulate the evolution of portfolio behavior, and investigate the characteristics of the long-run surviving population of investors. Our agent-based simulation results are largely consistent with [Blume and Easley (1992)], and we conclude that preference is the key factor determining agents’ survivability.

[1]  J. J. Kelly.  A new interpretation of information rate , 1956 .

[2]  Alvaro Sandroni.  Do markets favor agents able to make accurate predictions , 2000 .

[3]  George G. Szpiro.  The emergence of risk aversion , 1997 .

[4]  R. Jagannathan,et al.  The Conditional CAPM and the Cross-Section of Expected Returns , 1996 .

[5]  Shu-Heng Chen,et al.  On the emergent properties of artificial stock markets: the efficient market hypothesis and the rational expectations hypothesis , 2002 .

[6]  R. Palmer,et al.  Asset Pricing Under Endogenous Expectations in an Artificial Stock Market , 1996 .

[7]  B. LeBaron.  EVOLUTION AND TIME HORIZONS IN AN AGENT-BASED STOCK MARKET , 1999, Macroeconomic Dynamics.

[8]  N. J. Vriend,et al.  An Illustration of the Essential Difference between Individual and Social Learning , 1999 .

[9]  Nicholas S. P. Tay,et al.  Fuzzy Inductive Reasoning, Expectation Formation and the Behavior of Security Prices , 2001 .

[10]  A. Lo,et al.  Frontiers of finance: evolution and efficient markets. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[11]  R. Palmer,et al.  Time series properties of an artificial stock market , 1999 .

[12]  J. Holland,et al.  Artificial Adaptive Agents in Economic Theory , 1991 .

[13]  Serge Hayward.  Heterogeneous Agents Past and Forward Time Horizons in Setting Up a Computational Model , 2004 .

[14]  Blake LeBaron,et al.  Agent-based computational finance : Suggested readings and early research , 2000 .

[15]  Chi-Fu Huang,et al.  Foundations for financial economics , 1988 .

[16]  Shu-Heng Chen,et al.  Evolving traders and the business school with genetic programming: A new architecture of the agent-based artificial stock market , 2001 .

[17]  M. Lettau.  Explaining the facts with adaptive agents: The case of mutual fund flows , 1997 .

[18]  Leigh Tesfatsion,et al.  Introduction to the CE Special Issue on Agent-Based Computational Economics , 2001 .

[19]  O. Cacho,et al.  A Genetic Algorithm Approach to Farm Investment , 1999 .

[20]  L. Blume,et al.  Evolution and market behavior , 1992 .