55 research outputs found
The magnetic reversal in dot arrays recognized by the self-organized adaptive neural network
The remagnetization dynamics of monolayer dot array superlattice XY 2-D spin
model with dipole-dipole interactions is simulated. Within the proposed model
of array, the square dots are described by the spatially modulated
exchange-couplings. The dipole-dipole interactions are approximated by the
hierarchical sums and spin dynamics is considered in regime of the
Landau-Lifshitz equation. The simulation of reversal for spins
exhibits formation of nonuniform intra-dot configurations with nonlinear
wave/anti-wave pairs developed at intra-dot and inter-dot scales. Several
geometric and parametric dependences are calculated and compared with
oversimplified four-spin model of reversal. The role of initial conditions and
the occurrence of coherent rotation mode is also investigated. The emphasis is
on the classification of intra-dot or inter-dot (interfacial) magnetic
configurations done by adaptive neural network with varying number of neurons.Comment: 16 figure
Properties of iterative Monte Carlo single histogram reweighting
We present iterative Monte Carlo algorithm for which the temperature variable
is attracted by a critical point. The algorithm combines techniques of single
histogram reweighting and linear filtering. The 2d Ising model of ferromagnet
is studied numerically as an illustration. In that case, the iterations
uncovered stationary regime with invariant probability distribution function of
temperature which is peaked nearly the pseudocritical temperature of specific
heat. The sequence of generated temperatures is analyzed in terms of stochastic
autoregressive model. The error of histogram reweighting can be better
understood within the suggested model. The presented model yields a simple
relation, connecting variance of pseudocritical temperature and parameter of
linear filtering.Comment: 3 figure
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
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
Analysis of the Lead-Lag Relationship on South Africa capital market
Despite the efficient market hypothesis (EHM), lead-lag relationships can be observed mainly between financial derivatives and underlying asset prices, prices of large and small companies, etc. In our paper, we examined the lead-
lag relationship between prices of open ended funds and an all-share index as a representative of the capital market. Along with more traditional methods of using cross correlations, partial correlation and Toda-Yamamoto causality tests, we also analysed the speed of adjustment of assets to their intrinsic values and identified the most prevalent lag using rolling time windows. The analysis was performed using data from the South Africa capital market
Analysis of the Lead-Lag Relationship on South Africa capital market
Despite the efficient market hypothesis (EHM), lead-lag relationships can be observed mainly between financial derivatives and underlying asset prices, prices of large and small companies, etc. In our paper, we examined the lead-
lag relationship between prices of open ended funds and an all-share index as a representative of the capital market. Along with more traditional methods of using cross correlations, partial correlation and Toda-Yamamoto causality tests, we also analysed the speed of adjustment of assets to their intrinsic values and identified the most prevalent lag using rolling time windows. The analysis was performed using data from the South Africa capital market
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
Magnetic dot arrays modeling via the system of the radial basis function networks
Two dimensional square lattice general model of the magnetic dot array is
introduced. In this model the intradot self-energy is predicted via the neural
network and interdot magnetostatic coupling is approximated by the collection
of several dipolar terms. The model has been applied to disk-shaped cluster
involving 193 ultrathin dots and 772 interaction centers. In this case among
the intradot magnetic structures retrieved by neural networks the important
role play single-vortex magnetization modes. Several aspects of the model have
been understood numerically by means of the simulated annealing method.Comment: 16 pages, 8 figure
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