Modeling and analysis of an agent-based model for Chinese stock market

Abstract We constructed an agent-based stock market model which concisely describe investorsʼ heterogeneity and adaptability by introducing price sensitivity and feedback time. Under different parameters, the peak and fat-tail property of return distribution is produced and the obtained statistic values coincide with empirical results: the center peak exponents range from −0.787 to −0.661, and the tail exponents range from −4.29 to −2.37. Besides, long-term correlation in volatility is examined by DFA1 method, and the obtained exponent α is 0.803, which also coincides with the exponent of 0.78 found in real market.

[1]  R. Mantegna,et al.  Scaling behaviour in the dynamics of an economic index , 1995, Nature.

[2]  Peter Winker,et al.  A global optimization heuristic for estimating agent based models , 2003, Comput. Stat. Data Anal..

[3]  Chunxia Yang,et al.  The endogenous dynamics of financial markets: Interaction and information dissemination , 2012 .

[4]  Didier Sornette,et al.  Self-organizing Ising model of financial markets , 2005, physics/0503230.

[5]  W. B. Tayler,et al.  Momentum, Reversal, and Uninformed Traders in Laboratory Markets , 2008 .

[6]  H. Stanley,et al.  Quantifying signals with power-law correlations: a comparative study of detrended fluctuation analysis and detrended moving average techniques. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[7]  Pak Ming Hui,et al.  The distribution and scaling of fluctuations for Hang Seng index in Hong Kong stock market , 2001 .

[8]  J. Nacher,et al.  A model for the dynamic behavior of financial assets affected by news: The case of Tohoku–Kanto earthquake , 2011 .

[9]  A. Harvey,et al.  Detrending, stylized facts and the business cycle , 1993 .

[10]  V. Plerou,et al.  Scaling of the distribution of fluctuations of financial market indices. , 1999, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[11]  M. Marchesi,et al.  Agent-based simulation of a financial market , 2001, cond-mat/0103600.

[12]  T. Lux,et al.  Estimation of Agent-Based Models: The Case of an Asymmetric Herding Model , 2005 .

[13]  Wei-Xing Zhou,et al.  Tests of nonuniversality of the stock return distributions in an emerging market. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[14]  V. Plerou,et al.  Scaling of the distribution of price fluctuations of individual companies. , 1999, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[15]  Self-Organized Percolation and Critical Sales Fluctuations , 2000 .

[16]  Paolo Pellizzari,et al.  Fundamentalists clashing over the book: a study of order-driven stock markets , 2003 .

[17]  C. Ye,et al.  Non-classical oscillator model for persistent fluctuations in stock markets , 2008 .

[18]  R. Cont.  Empirical properties of asset returns: stylized facts and statistical issues , 2001 .

[19]  Andrea Zaccaria,et al.  Minimal agent based model for financial markets I , 2008, 0808.3562.

[20]  Shu-Heng Chen,et al.  Risk Preference, Forecasting Accuracy and Survival Dynamics:Simulations Based on a Multi-Asset Agent-Based Artificial Stock Market , 2008 .

[21]  Wei-Xing Zhou,et al.  Nonuniversal distributions of stock returns in an emerging market , 2010, 1003.5984.

[22]  T. Frank,et al.  Time-dependent solutions for stochastic systems with delays: Perturbation theory and applications to financial physics , 2006 .

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

[24]  Sergio Da Silva,et al.  Jump diffusion models and the evolution of financial prices , 2011 .

[25]  Ryuichi Yamamoto.  Order Aggressiveness, Pre-Trade Transparency, and Long Memory in an Order-Driven Market , 2011 .

[26]  Szabolcs Mike,et al.  An Empirical Behavioral Model of Liquidity and Volatility , 2007, 0709.0159.

[27]  F. Lillo,et al.  Specialization and herding behavior of trading firms in a financial market , 2007, 0707.0385.

[28]  Financial market model based on self-organized percolation , 2005 .

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

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

[31]  P. Gopikrishnan,et al.  Inverse cubic law for the distribution of stock price variations , 1998, cond-mat/9803374.

[32]  B. LeBaron,et al.  Long-memory in an order-driven market , 2007 .

[33]  Wei‐Xing Zhou,et al.  Emergence of long memory in stock volatility from a modified Mike-Farmer model , 2008, 0807.4639.

[34]  J. Kertész,et al.  Random matrix approach to the dynamics of stock inventory variations , 2012, 1201.0433.

[35]  J. Bouchaud,et al.  Herd Behavior and Aggregate Fluctuations in Financial Markets , 1997 .

[36]  S. Havlin,et al.  Comparison of detrending methods for fluctuation analysis , 2008, 0804.4081.

[37]  I. Gabitov,et al.  Long scale evolution of a nonlinear stochastic dynamic system for modeling market price bubbles , 2000 .

[38]  Wei-Xing Zhou,et al.  Empirical distributions of Chinese stock returns at different microscopic timescales , 2007, 0708.3472.

[39]  F. Slanina.  Critical comparison of several order-book models for stock-market fluctuations , 2008, 0801.0631.