192 research outputs found
Correlation, hierarchies, and networks in financial markets
We discuss some methods to quantitatively investigate the properties of
correlation matrices. Correlation matrices play an important role in portfolio
optimization and in several other quantitative descriptions of asset price
dynamics in financial markets. Specifically, we discuss how to define and
obtain hierarchical trees, correlation based trees and networks from a
correlation matrix. The hierarchical clustering and other procedures performed
on the correlation matrix to detect statistically reliable aspects of the
correlation matrix are seen as filtering procedures of the correlation matrix.
We also discuss a method to associate a hierarchically nested factor model to a
hierarchical tree obtained from a correlation matrix. The information retained
in filtering procedures and its stability with respect to statistical
fluctuations is quantified by using the Kullback-Leibler distance.Comment: 37 pages, 9 figures, 3 table
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
Statistically validated networks in bipartite complex systems
Many complex systems present an intrinsic bipartite nature and are often
described and modeled in terms of networks [1-5]. Examples include movies and
actors [1, 2, 4], authors and scientific papers [6-9], email accounts and
emails [10], plants and animals that pollinate them [11, 12]. Bipartite
networks are often very heterogeneous in the number of relationships that the
elements of one set establish with the elements of the other set. When one
constructs a projected network with nodes from only one set, the system
heterogeneity makes it very difficult to identify preferential links between
the elements. Here we introduce an unsupervised method to statistically
validate each link of the projected network against a null hypothesis taking
into account the heterogeneity of the system. We apply our method to three
different systems, namely the set of clusters of orthologous genes (COG) in
completely sequenced genomes [13, 14], a set of daily returns of 500 US
financial stocks, and the set of world movies of the IMDb database [15]. In all
these systems, both different in size and level of heterogeneity, we find that
our method is able to detect network structures which are informative about the
system and are not simply expression of its heterogeneity. Specifically, our
method (i) identifies the preferential relationships between the elements, (ii)
naturally highlights the clustered structure of investigated systems, and (iii)
allows to classify links according to the type of statistically validated
relationships between the connected nodes.Comment: Main text: 13 pages, 3 figures, and 1 Table. Supplementary
information: 15 pages, 3 figures, and 2 Table
Generation of hierarchically correlated multivariate symbolic sequences
We introduce an algorithm to generate multivariate series of symbols from a
finite alphabet with a given hierarchical structure of similarities. The target
hierarchical structure of similarities is arbitrary, for instance the one
obtained by some hierarchical clustering procedure as applied to an empirical
matrix of Hamming distances. The algorithm can be interpreted as the finite
alphabet equivalent of the recently introduced hierarchically nested factor
model (M. Tumminello et al. EPL 78 (3) 30006 (2007)). The algorithm is based on
a generating mechanism that is different from the one used in the mutation rate
approach. We apply the proposed methodology for investigating the relationship
between the bootstrap value associated with a node of a phylogeny and the
probability of finding that node in the true phylogeny.Comment: 7 pages, 6 figures, 1 tabl
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
Cathepsin D expression levels in nongynecological solid tumors: Clinical and therapeutic implications
Cathepsin D is a lysosomal acid proteinase which is involved in the malignant progression of breast cancer and other gynecological tumors. Clinical investigations have shown that in breast cancer patients cathepsin D overexpression was significantly correlated with a shorter free-time disease and overall survival, whereas in patients with ovarian or endometrial cancer this phenomenon was associated with tumor aggressiveness and a degree of chemoresistance to various antitumor drugs such as anthracyclines, cis-platinum and vinca alkaloids. Therefore, a lot of research has been undertaken to evaluate the role and the prognostic value of cathepsin D also in other solid neoplasms. However, conflicting results have been generated from these studies. The discrepancies in these results may, in part, be explained with the different methodological approaches used in order to determine the levels of expression of the enzyme in tumor tissues and body fluids. Further investigations using well-standardized techniques may better define the clinical significance of cathepsin D expression in solid tumors. Nevertheless, evidence emerging from these studies indicates that this proteinase seems to facilitate early phases of tumor progression such as cell proliferation and local dissemination. These findings support the concept that cathepsin D may be a useful marker for identifying patients with highly malignant tumor phenotypes who may need more aggressive clinical treatment; this enzyme may also be considered as a potential target for a novel therapeutic approach in the treatment of solid neoplasms
Hierarchically nested factor model from multivariate data
We show how to achieve a statistical description of the hierarchical
structure of a multivariate data set. Specifically we show that the similarity
matrix resulting from a hierarchical clustering procedure is the correlation
matrix of a factor model, the hierarchically nested factor model. In this
model, factors are mutually independent and hierarchically organized. Finally,
we use a bootstrap based procedure to reduce the number of factors in the model
with the aim of retaining only those factors significantly robust with respect
to the statistical uncertainty due to the finite length of data records.Comment: 7 pages, 5 figures; accepted for publication in Europhys. Lett. ; the
Appendix corresponds to the additional material of the accepted letter
La Ricerca-Azione Partecipativa come pedagogia della con-vers-azione. Un percorso di co-progettazione per la costruzione di una comunitĂ educante
This paper shows the early results of a Participatory Action-Research (PAR) in which University, School, and Third Sector of the city of Palermo dialogue with each other to design new educational systems’ architectures. Within the theoretical framework provided by Universal Design for Learning, Outdoor Education and the Capability Approach, the chosen methodology is the informal conversation of the World Cafè. The working tables reflected on the implications of the intertwining among educational agencies, technological innovations, and territorial fabric. From these significant exchanges emerged perspectives of emancipation and co-construction of inclusive contexts for learning and social innovation
Random matrix approach to the dynamics of stock inventory variations
We study the cross-correlation matrix of inventory variations of the
most active individual and institutional investors in an emerging market to
understand the dynamics of inventory variations. We find that the distribution
of cross-correlation coefficient has a power-law form in the bulk
followed by exponential tails and there are more positive coefficients than
negative ones. In addition, it is more possible that two individuals or two
institutions have stronger inventory variation correlation than one individual
and one institution. We find that the largest and the second largest
eigenvalues ( and ) of the correlation matrix cannot be
explained by the random matrix theory and the projection of inventory
variations on the first eigenvector are linearly correlated with
stock returns, where individual investors play a dominating role. The investors
are classified into three categories based on the cross-correlation
coefficients between inventory variations and stock returns. Half
individuals are reversing investors who exhibit evident buy and sell herding
behaviors, while 6% individuals are trending investors. For institutions, only
10% and 8% investors are trending and reversing investors. A strong Granger
causality is unveiled from stock returns to inventory variations, which means
that a large proportion of individuals hold the reversing trading strategy and
a small part of individuals hold the trending strategy. Comparing with the case
of Spanish market, Chinese investors exhibit common and market-specific
behaviors. Our empirical findings have scientific significance in the
understanding of investors' trading behaviors and in the construction of
agent-based models for stock markets.Comment: 10 REVTEX pages including 7 figure
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