The endogenous dynamics of financial markets: Interaction and information dissemination

We investigate the process that different interactions between investors will prompt information to propagate along a differentiated path and construct a financial market model. As information spreads, increasingly investors are attracted to participate in trading, then the “herding effect” is magnified gradually, which will induce the topology of market network to change and the price to fluctuate. Especially, under different initial conditions or parameters, the peak and fat-tail property is produced and the obtained statistic values coincide with empirical results: the power-law exponents between the peak value of return probability distribution and the time scales range from 0.579 to 0.747, and the exponents between the accumulation distribution and the return on the tail are close to 3. Besides, the extent of volatility clustering in our produced price series is close to that of S&P 500 and locates between NASDAQ and HSI. All the results obtained here indicate that the continuous variation of the “herding effect” resulting from information propagation among interacting investors may be the origin of stylized facts of price fluctuations.

[1]  Didier Sornette,et al.  Critical Market Crashes , 2003, cond-mat/0301543.

[2]  Didier Sornette,et al.  Market Fluctuations: multiplicative and percolation models, size effects and predictions , 2002 .

[3]  A. Tversky,et al.  Prospect Theory : An Analysis of Decision under Risk Author ( s ) : , 2007 .

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

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

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

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

[8]  Asset returns and volatility clustering in financial time series , 2010, 1002.0284.

[9]  Wei-Xing Zhou,et al.  On the probability distribution of stock returns in the Mike-Farmer model , 2008, 0805.3593.

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

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

[12]  A. Tversky,et al.  Prospect theory: analysis of decision under risk , 1979 .

[13]  Anirban Chakraborti,et al.  DISTRIBUTIONS OF MONEY IN MODEL MARKETS OF ECONOMY , 2002 .

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

[15]  P. Gnaciński,et al.  Amplified imitation in percolation model of stock market , 2004 .

[16]  Tao Zhou,et al.  Modeling Stock Market Based on Genetic Cellular Automata , 2004 .

[17]  V. Plerou,et al.  Scale invariance and universality of economic fluctuations , 2000 .

[18]  Xavier Gabaix,et al.  Scaling and correlation in financial time series , 2000 .

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

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

[21]  Yingchao Zhang,et al.  PHASE SYNCHRONIZATION DETECTION OF FINANCIAL MARKET CRISES , 2011 .

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

[23]  Ashok Razdan.  Scaling in the bombay stock exchange index , 2002 .

[24]  D. Sornette,et al.  Self-organized percolation model for stock market fluctuations , 1999, cond-mat/9906434.

[25]  F. Lillo,et al.  The Long Memory of the Efficient Market , 2003, cond-mat/0311053.

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

[27]  Bingyong Tang,et al.  Minority game and anomalies in financial markets , 2004 .

[28]  Tao Zhou,et al.  Diffusion entropy analysis on the scaling behavior of financial markets , 2005, physics/0508117.

[29]  M. Defleur,et al.  Theories of mass communication , 1968 .

[30]  Dietrich Stauffer,et al.  Sharp peaks in the percolation model for stock markets , 2000 .

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

[32]  D. Stauffer,et al.  Agent-based Models of Financial Markets , 2007, physics/0701140.

[33]  Xu Cai,et al.  Non-linear characteristics and long-range correlations in Asian stock markets , 2007 .

[34]  Michael Schwind,et al.  Frankfurt Artificial Stock Market: a microscopic stock market model with heterogeneous interacting agents in small-world communication networks , 2008 .

[35]  Adrian Pagan,et al.  The econometrics of financial markets , 1996 .

[36]  Chunxia Yang,et al.  PERIODIC COMPONENTS AND CHARACTERISTIC TIME SCALES IN THE FINANCIAL MARKET , 2008 .

[37]  Dietrich Stauffer,et al.  FUNDAMENTAL JUDGEMENT IN CONT-BOUCHAUD HERDING MODEL OF MARKET FLUCTUATIONS , 1999 .

[38]  Cont-Bouchaud percolation model including Tobin tax , 2002, cond-mat/0205320.

[39]  Johannes A. Skjeltorp.  Scaling in the Norwegian stock market , 2000 .