7 research outputs found
Structurally dynamic spin market networks
The agent-based model of stock price dynamics on a directed evolving complex
network is suggested and studied by direct simulation. The stationary regime is
maintained as a result of the balance between the extremal dynamics, adaptivity
of strategic variables and reconnection rules. The inherent structure of node
agent "brain" is modeled by a recursive neural network with local and global
inputs and feedback connections. For specific parametric combination the
complex network displays small-world phenomenon combined with scale-free
behavior. The identification of a local leader (network hub, agent whose
strategies are frequently adapted by its neighbors) is carried out by repeated
random walk process through network. The simulations show empirically relevant
dynamics of price returns and volatility clustering. The additional emerging
aspects of stylized market statistics are Zipfian distributions of fitness.Comment: 13 pages, 5 figures, accepted in IJMPC, references added, minor
changes in model, new results and modified figure
The co-evolutionary dynamics of directed network of spin market agents
The spin market model [S. Bornholdt, Int.J.Mod.Phys. C 12 (2001) 667] is
extended into co-evolutionary version, where strategies of interacting and
competitive traders are represented by local and global couplings between the
nodes of dynamic directed stochastic network. The co-evolutionary principles
are applied in the frame of Bak - Sneppen self-organized dynamics [P. Bak, K.
Sneppen, Phys. Rev. Letter 71 (1993) 4083] that includes the processes of
selection and extinction actuated by the local (node) fitness. The local
fitness is related to orientation of spin agent with respect to instant
magnetization. The stationary regime characterized by a fat tailed distribution
of the log-price returns with index (out of the Levy range)
is identified numerically. The non-trivial consequence of the extremal dynamics
is the partially power-law decay (an effective exponent varies between -0.3 and
-0.6) of the autocorrelation function of volatility. Broad-scale network
topology with node degree distribution characterized by the exponent
from the range of social networks is obtained.Comment: 10 pages, 4 figures. accepted for publication in Physica
A self-adjusted Monte Carlo simulation as model of financial markets with central regulation
Properties of the self-adjusted Monte Carlo algorithm applied to 2d Ising
ferromagnet are studied numerically. The endogenous feedback form expressed in
terms of the instant running averages is suggested in order to generate a
biased random walk of the temperature that converges to criticality without an
external tuning. The robustness of a stationary regime with respect to partial
accessibility of the information is demonstrated. Several statistical and
scaling aspects have been identified which allow to establish an alternative
spin lattice model of the financial market. It turns out that our model alike
model suggested by S. Bornholdt, Int. J. Mod. Phys. C {\bf 12} (2001) 667, may
be described by L\'evy-type stationary distribution of feedback variations with
unique exponent . However, the differences reflected by
Hurst exponents suggest that resemblances between the studied models seem to be
nontrivial.Comment: 19 pages, 9 figures, 30 reference