Risk Preference, Forecasting Accuracy and Survival Dynamics:Simulations Based on a Multi-Asset Agent-Based Artificial Stock Market

Abstract The relevance of risk preference and forecasting accuracy to the survival of investors is an issue that has recently attracted a number of theoretical studies. By using agent-based computational modeling, this paper extends the existing studies to an economy where adaptive behaviors are autonomous and complex heterogeneous. Specifically, a computational multi-asset artificial stock market corresponding to Blume and Easley [Blume, L., Easley, D., 1992. Evolution and market behavior. Journal of Economic Theory 58, 9–40] and Sandroni [Sandroni, A., 2000. Do markets favor agents able to make accurate predictions? Econometrica 68, 1303–1341] is constructed and studied. Through simulation, we present results that contradict the market selection hypothesis.

[1]  Harold Borko,et al.  Computer applications in the behavioral sciences , 1963 .

[2]  The Evolution of Portfolio Rules and the Capital Asset Pricing Model , 2006 .

[3]  Stephen Spear,et al.  Learning Rational Expectations under Computability Constraints , 1989 .

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

[5]  M. Rabin.  Psychology and Economics , 1997 .

[6]  J. Duffy,et al.  Using Genetic Algorithms to Model the Evolution of Heterogeneous Beliefs , 1999 .

[7]  Kenneth E. Boulding,et al.  Essays in Positive Economics. , 1954 .

[8]  F. Lutz,et al.  The case for flexible exchange rates , 1954 .

[9]  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 .

[10]  Leigh Tesfatsion,et al.  Agent-Based Computational Economics: Growing Economies From the Bottom Up , 2002, Artificial Life.

[11]  C. Hommes.  Chapter 23 Heterogeneous Agent Models in Economics and Finance , 2006 .

[12]  Nicolaas J. Vriend,et al.  An Illustration of the Essential Difference between Individual and Social Learning, and its Consequences for Computational Analyses , 1998 .

[13]  Shu-Heng Chen,et al.  Risk Preference and Survival Dynamics , 2005 .

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

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

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

[17]  Nicolaas J. Vriend,et al.  An Illustration of the Essential Difference between Individual and Social Learning, and its Consequences for Computational Analyses , 1998 .

[18]  Jasmina Arifovic,et al.  Expectations and Currency Crisis - An experimental approach , 2003 .

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

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

[21]  Myron J Gordon,et al.  Experimental Evidence on Alternative Portfolio Decision Rules , 1972 .

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

[23]  C. Hommes.  Heterogeneous Agent Models in Economics and Finance , 2005 .

[24]  A. Alchian.  Uncertainty, Evolution, and Economic Theory , 1950, Journal of Political Economy.

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

[26]  Herbert J. Hallworth.  Computer Simulation of Cognitive Processes. , 1969 .

[27]  M. Blume,et al.  The Demand for Risky Assets , 1975 .

[28]  R. Thaler,et al.  A Survey of Behavioral Finance , 2002 .

[29]  David I. Laibson,et al.  Life-cycle consumption and hyperbolic discount functions , 1998 .

[30]  L. Blume,et al.  If You're so Smart, Why Aren't You Rich? Belief Selection in Complete and Incomplete Markets , 2001 .

[31]  Leigh Tesfatsion,et al.  Handbook of Computational Economics, Volume 2: Agent-Based Computational Economics (Handbook of Computational Economics) , 2006 .

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