555 research outputs found
Persistence in a Random Bond Ising Model of Socio-Econo Dynamics
We study the persistence phenomenon in a socio-econo dynamics model using
computer simulations at a finite temperature on hypercubic lattices in
dimensions up to 5. The model includes a ` social\rq local field which contains
the magnetization at time . The nearest neighbour quenched interactions are
drawn from a binary distribution which is a function of the bond concentration,
. The decay of the persistence probability in the model depends on both the
spatial dimension and . We find no evidence of ` blocking\rq in this model.
We also discuss the implications of our results for possible applications in
the social and economic fields. It is suggested that the absence, or otherwise,
of blocking could be used as a criterion to decide on the validity of a given
model in different scenarios.Comment: 11 pages, 4 figure
Economic sector identification in a set of stocks traded at the New York Stock Exchange: a comparative analysis
We review some methods recently used in the literature to detect the
existence of a certain degree of common behavior of stock returns belonging to
the same economic sector. Specifically, we discuss methods based on random
matrix theory and hierarchical clustering techniques. We apply these methods to
a set of stocks traded at the New York Stock Exchange. The investigated time
series are recorded at a daily time horizon.
All the considered methods are able to detect economic information and the
presence of clusters characterized by the economic sector of stocks. However,
different methodologies provide different information about the considered set.
Our comparative analysis suggests that the application of just a single method
could not be able to extract all the economic information present in the
correlation coefficient matrix of a set of stocks.Comment: 13 pages, 8 figures, 2 Table
Sector identification in a set of stock return time series traded at the London Stock Exchange
We compare some methods recently used in the literature to detect the
existence of a certain degree of common behavior of stock returns belonging to
the same economic sector. Specifically, we discuss methods based on random
matrix theory and hierarchical clustering techniques. We apply these methods to
a portfolio of stocks traded at the London Stock Exchange. The investigated
time series are recorded both at a daily time horizon and at a 5-minute time
horizon. The correlation coefficient matrix is very different at different time
horizons confirming that more structured correlation coefficient matrices are
observed for long time horizons. All the considered methods are able to detect
economic information and the presence of clusters characterized by the economic
sector of stocks. However different methods present a different degree of
sensitivity with respect to different sectors. Our comparative analysis
suggests that the application of just a single method could not be able to
extract all the economic information present in the correlation coefficient
matrix of a stock portfolio.Comment: 28 pages, 13 figures, 3 Tables. Proceedings of the conference on
"Applications of Random Matrices to Economy and other Complex Systems",
Krakow (Poland), May 25-28 2005. Submitted for pubblication to Acta Phys. Po
Mean Field Voter Model of Election to the House of Representatives in Japan
In this study, we propose a mechanical model of a plurality election based on
a mean field voter model. We assume that there are three candidates in each
electoral district, i.e., one from the ruling party, one from the main
opposition party, and one from other political parties. The voters are
classified as fixed supporters and herding (floating) voters with ratios of
and , respectively. Fixed supporters make decisions based on their
information and herding voters make the same choice as another randomly
selected voter. The equilibrium vote-share probability density of herding
voters follows a Dirichlet distribution. We estimate the composition of fixed
supporters in each electoral district and using data from elections to the
House of Representatives in Japan (43rd to 47th). The spatial inhomogeneity of
fixed supporters explains the long-range spatial and temporal correlations. The
estimated values of are close to the estimates obtained from a survey.Comment: 11 pages, 7 figure
Networks of equities in financial markets
We review the recent approach of correlation based networks of financial
equities. We investigate portfolio of stocks at different time horizons,
financial indices and volatility time series and we show that meaningful
economic information can be extracted from noise dressed correlation matrices.
We show that the method can be used to falsify widespread market models by
directly comparing the topological properties of networks of real and
artificial markets.Comment: 9 pages, 8 figures. Accepted for publication in EPJ
Data clustering and noise undressing for correlation matrices
We discuss a new approach to data clustering. We find that maximum likelihood
leads naturally to an Hamiltonian of Potts variables which depends on the
correlation matrix and whose low temperature behavior describes the correlation
structure of the data. For random, uncorrelated data sets no correlation
structure emerges. On the other hand for data sets with a built-in cluster
structure, the method is able to detect and recover efficiently that structure.
Finally we apply the method to financial time series, where the low temperature
behavior reveals a non trivial clustering.Comment: 8 pages, 5 figures, completely rewritten and enlarged version of
cond-mat/0003241. Submitted to Phys. Rev.
Community characterization of heterogeneous complex systems
We introduce an analytical statistical method to characterize the communities
detected in heterogeneous complex systems. By posing a suitable null
hypothesis, our method makes use of the hypergeometric distribution to assess
the probability that a given property is over-expressed in the elements of a
community with respect to all the elements of the investigated set. We apply
our method to two specific complex networks, namely a network of world movies
and a network of physics preprints. The characterization of the elements and of
the communities is done in terms of languages and countries for the movie
network and of journals and subject categories for papers. We find that our
method is able to characterize clearly the identified communities. Moreover our
method works well both for large and for small communities.Comment: 8 pages, 1 figure and 2 table
Scaling of the distribution of price fluctuations of individual companies
We present a phenomenological study of stock price fluctuations of individual
companies. We systematically analyze two different databases covering
securities from the three major US stock markets: (a) the New York Stock
Exchange, (b) the American Stock Exchange, and (c) the National Association of
Securities Dealers Automated Quotation stock market. Specifically, we consider
(i) the trades and quotes database, for which we analyze 40 million records for
1000 US companies for the 2-year period 1994--95, and (ii) the Center for
Research and Security Prices database, for which we analyze 35 million daily
records for approximately 16,000 companies in the 35-year period 1962--96. We
study the probability distribution of returns over varying time scales , where varies by a factor of ---from 5 min up to
4 years. For time scales from 5~min up to approximately 16~days, we
find that the tails of the distributions can be well described by a power-law
decay, characterized by an exponent ---well outside the
stable L\'evy regime . For time scales days, we observe results consistent with a slow
convergence to Gaussian behavior. We also analyze the role of cross
correlations between the returns of different companies and relate these
correlations to the distribution of returns for market indices.Comment: 10pages 2 column format with 11 eps figures. LaTeX file requiring
epsf, multicol,revtex. Submitted to PR
Networks in biological systems: An investigation of the Gene Ontology as an evolving network
Many biological systems can be described as networks where different elements interact, in order to perform biological processes. We introduce a network associated with the Gene Ontology. Specifically, we construct a correlation-based
network where the vertices are the terms of the Gene Ontology and the link between each two terms is weighted on the basis of the number of genes that they have in common. We analyze a filtered network obtained from the correlation-based network and we characterize its evolution over different releases of the Gene Ontology
Accounting for risk of non linear portfolios: a novel Fourier approach
The presence of non linear instruments is responsible for the emergence of
non Gaussian features in the price changes distribution of realistic
portfolios, even for Normally distributed risk factors. This is especially true
for the benchmark Delta Gamma Normal model, which in general exhibits
exponentially damped power law tails. We show how the knowledge of the model
characteristic function leads to Fourier representations for two standard risk
measures, the Value at Risk and the Expected Shortfall, and for their
sensitivities with respect to the model parameters. We detail the numerical
implementation of our formulae and we emphasizes the reliability and efficiency
of our results in comparison with Monte Carlo simulation.Comment: 10 pages, 12 figures. Final version accepted for publication on Eur.
Phys. J.
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