Modeling the stylized facts in finance through simple nonlinear adaptive systems

Recent work on adaptive systems for modeling financial markets is discussed. Financial markets are viewed as evolutionary systems between different, competing trading strategies. Agents are boundedly rational in the sense that they tend to follow strategies that have performed well, according to realized profits or accumulated wealth, in the recent past. Simple technical trading rules may survive evolutionary competition in a heterogeneous world where prices and beliefs co-evolve over time. Evolutionary models can explain important stylized facts, such as fat tails, clustered volatility, and long memory, of real financial series.

[1]  E. Fama.  EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK* , 1970 .

[2]  Wagner A. Kamakura,et al.  Book Review: Structural Analysis of Discrete Data with Econometric Applications , 1982 .

[3]  W. Arthur,et al.  The Economy as an Evolving Complex System II , 1988 .

[4]  L. Summers,et al.  Noise Trader Risk in Financial Markets , 1990, Journal of Political Economy.

[5]  Carl Chiarella,et al.  The dynamics of speculative behaviour , 1992, Ann. Oper. Res..

[6]  A. Kirman.  Whom Or What Does the Representative Individual Represent , 1992 .

[7]  M. Embrechts,et al.  Exchange Rate Theory: Chaotic Models of Foreign Exchange Markets , 1993 .

[8]  T. Sargent.  Bounded rationality in macroeconomics , 1993 .

[9]  William A. Brock,et al.  PATHWAYS TO RANDOMNESS IN THE ECONOMY: EMERGENT NONLINEARITY AND CHAOS IN ECONOMICS AND FINANCE , 1993 .

[10]  Jiang Wang,et al.  A Model of Competitive Stock Trading Volume , 1994, Journal of Political Economy.

[11]  Christian Jost,et al.  Heterogeneous real-time trading strategies in the foreign exchange market , 1995 .

[12]  André de Palma,et al.  Discrete Choice Theory of Product Differentiation , 1995 .

[13]  T. Lux.  Herd Behaviour, Bubbles and Crashes , 1995 .

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

[15]  William A. Brock,et al.  A rational route to randomness , 1997 .

[16]  J. Farmer.  Market Force, Ecology, and Evolution , 1998, adap-org/9812005.

[17]  W. Brock,et al.  Heterogeneous beliefs and routes to chaos in a simple asset pricing model , 1998 .

[18]  Carl Chiarella,et al.  Heterogeneous Beliefs, Risk and Learning in a Simple Asset Pricing Model , 2002 .

[19]  M. Marchesi,et al.  Scaling and criticality in a stochastic multi-agent model of a financial market , 1999, Nature.

[20]  Andrea Gaunersdorfer,et al.  A Nonlinear Structural Model for Volatility Clustering , 2000 .

[21]  Saangjoon Baak.  Tests for bounded rationality with a linear dynamic model distorted by heterogeneous expectations , 1999 .

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

[23]  J. Chavas.  On information and market dynamics: The case of the U.S. beef market , 2000 .

[24]  Florian Wagener,et al.  Bifurcation Routes to Volatility Clustering , 2000 .

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

[26]  Cars H. Hommes,et al.  Financial markets as nonlinear adaptive evolutionary systems , 2001 .

[27]  M. Marchesi,et al.  VOLATILITY CLUSTERING IN FINANCIAL MARKETS: A MICROSIMULATION OF INTERACTING AGENTS , 2000 .

[28]  J. Farmer,et al.  The price dynamics of common trading strategies , 2000, cond-mat/0012419.

[29]  Andrea Gaunersdorfer,et al.  Endogenous fluctuations in a simple asset pricing model with heterogeneous agents , 2000 .