Percolation Models of Financial Market Dynamics

Microscopic models dealing with the decisions of traders on the market have tried to reproduce real market behaviour. Possibly the simplest of these models is the herding approach of Cont and Bouchaud. Variations include letting the concentration varying between zero and unity (or zero and percolation threshold); changing the price proportionally not to the difference between demand and supply, but to the square root of this difference; influencing the buy/sell decisions by the actual price and price change. As a result, the probability to find a market change greater than some R was found to vary as R-2.9; this distribution gets wings which might correspond to outliers like the 1929 crash on Wall Street; bubbles lead to sharp peaks separated by flat valleys; and the log-periodic variations after the Japanese crash of 1990 were reproduced to get rich from the prediction made in January 1999 by Johansen and Sornette that Nikkei will rise appreciably during 1999. As it did.

[1]  Didier Sornette,et al.  Financial Anti-Bubbles Log-Periodicity in Gold and Nikkei Collapses , 1999, cond-mat/9901268.

[2]  Zhi-Feng Huang,et al.  Self-organized model for information spread in financial markets , 2000, cond-mat/0004314.

[3]  S. Solomon,et al.  Social percolation models , 1999, adap-org/9909001.

[4]  Dietrich Stauffer MARKET VOLATILITY AND THE DISTRIBUTION OF MEAN CLUSTER SIZES FOR PERCOLATION , 2000 .

[5]  M. Basta,et al.  An introduction to percolation , 1994 .

[6]  Dietrich Stauffer,et al.  Crossover in the Cont–Bouchaud percolation model for market fluctuations , 1998 .

[7]  Effect of trading momentum and price resistance on stock market dynamics: a Glauber Monte Carlo simulation , 2001 .

[8]  Kimmo Kaski,et al.  Characteristic times in stock market indices , 1999 .

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

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

[11]  M. Sahini,et al.  Applications of Percolation Theory , 2023, Applied Mathematical Sciences.

[12]  J. Bouchaud,et al.  HERD BEHAVIOR AND AGGREGATE FLUCTUATIONS IN FINANCIAL MARKETS , 1997, Macroeconomic Dynamics.

[13]  Yi-Cheng Zhang,et al.  Toward a theory of marginally efficient markets , 1999 .

[14]  G. J. Rodgers,et al.  Exact solution of a model for crowding and information transmission in financial markets , 1999, cond-mat/9908481.

[15]  H. Markowitz,et al.  Investment rules, margin, and market volatility , 1989 .

[16]  George J. Stigler,et al.  Public Regulation of the Securities Markets , 1964 .

[17]  D. Stauffer,et al.  SEARCH FOR LOG-PERIODIC OSCILLATIONS IN STOCK MARKET SIMULATIONS , 2000 .

[18]  M. Potters,et al.  Theory of Financial Risk , 1997 .

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

[20]  Dietrich Stauffer,et al.  A generalized spin model of financial markets , 1999 .

[21]  G. J. Rodgers,et al.  Democracy versus dictatorship in self-organized models of financial markets , 1999, adap-org/9912003.

[22]  Didier Sornette,et al.  The sharp peak-flat trough pattern and critical speculation , 1998 .

[23]  Thomas Lux,et al.  The stable Paretian hypothesis and the frequency of large returns: an examination of major German stocks , 1996 .

[24]  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.

[25]  INFLUENCE OF FINITE CAPITAL IN THE CONT–BOUCHAUD-MODEL FOR MARKET FLUCTUATIONS , 1999 .

[26]  Dietrich Stauffer,et al.  MONTE CARLO SIMULATION OF VOLATILITY CLUSTERING IN MARKET MODEL WITH HERDING , 1999 .