1,207 research outputs found
A universal mechanism for long-range cross-correlations
Cross-correlations are thought to emerge through interaction between particles. Here we present a universal dynamical mechanism capable of generating power-law cross-correlations between non-interacting particles exposed to an external potential. This phenomenon can occur as an ensemble property when the external potential induces intermittent dynamics of Pomeau-Manneville type, providing laminar and stochastic phases of motion in a system with a large number of particles. In this case, the ensemble of particle-trajectories forms a random fractal in time. The underlying statistical self-similarity is the origin of the observed power-law cross-correlations. Furthermore, we have strong indications that a sufficient condition for the emergence of these long-range cross-correlations is the divergence of the mean residence time in the laminar phase of the single particle motion (sporadic dynamics). We argue that the proposed mechanism may be relevant for the occurrence of collective behaviour in critical systems
Nucleotide-Induced Conformational Changes in an Isolated Escherichia coli DNA Polymerase III Clamp Loader Subunit
AbstractSliding clamps are loaded onto DNA by ATP-driven clamp loader complexes. The structure of the E. coli clamp loader in a nucleotide-free state has been determined previously. We now report crystal structures of a truncated form of the isolated γ-ATPase subunit, γ1–243, of the E. coli clamp loader, in nucleotide-free and bound forms. The γ subunit adopts a defined conformation when empty, in which the nucleotide binding site is blocked. The binding of either ATPγS or ADP, which are shown to bind with equal affinity to γ1–243, induces a change in the relative orientation of the two domains such that nucleotides can be accommodated. This change would break one of the γ:γ interfaces seen in the empty clamp loader complex, and may represent one step in the activation process
Anti-correlation and subsector structure in financial systems
With the random matrix theory, we study the spatial structure of the Chinese
stock market, American stock market and global market indices. After taking
into account the signs of the components in the eigenvectors of the
cross-correlation matrix, we detect the subsector structure of the financial
systems. The positive and negative subsectors are anti-correlated each other in
the corresponding eigenmode. The subsector structure is strong in the Chinese
stock market, while somewhat weaker in the American stock market and global
market indices. Characteristics of the subsector structures in different
markets are revealed.Comment: 6 pages, 2 figures, 4 table
Analysis of cross-correlations in electroencephalogram signals as an approach to proactive diagnosis of schizophrenia
We apply flicker-noise spectroscopy (FNS), a time series analysis method
operating on structure functions and power spectrum estimates, to study the
clinical electroencephalogram (EEG) signals recorded in children/adolescents
(11 to 14 years of age) with diagnosed schizophrenia-spectrum symptoms at the
National Center for Psychiatric Health (NCPH) of the Russian Academy of Medical
Sciences. The EEG signals for these subjects were compared with the signals for
a control sample of chronically depressed children/adolescents. The purpose of
the study is to look for diagnostic signs of subjects' susceptibility to
schizophrenia in the FNS parameters for specific electrodes and
cross-correlations between the signals simultaneously measured at different
points on the scalp. Our analysis of EEG signals from scalp-mounted electrodes
at locations F3 and F4, which are symmetrically positioned in the left and
right frontal areas of cerebral cortex, respectively, demonstrates an essential
role of frequency-phase synchronization, a phenomenon representing specific
correlations between the characteristic frequencies and phases of excitations
in the brain. We introduce quantitative measures of frequency-phase
synchronization and systematize the values of FNS parameters for the EEG data.
The comparison of our results with the medical diagnoses for 84 subjects
performed at NCPH makes it possible to group the EEG signals into 4 categories
corresponding to different risk levels of subjects' susceptibility to
schizophrenia. We suggest that the introduced quantitative characteristics and
classification of cross-correlations may be used for the diagnosis of
schizophrenia at the early stages of its development.Comment: 36 pages, 6 figures, 2 tables; to be published in "Physica A
Common Scaling Patterns in Intertrade Times of U. S. Stocks
We analyze the sequence of time intervals between consecutive stock trades of
thirty companies representing eight sectors of the U. S. economy over a period
of four years. For all companies we find that: (i) the probability density
function of intertrade times may be fit by a Weibull distribution; (ii) when
appropriately rescaled the probability densities of all companies collapse onto
a single curve implying a universal functional form; (iii) the intertrade times
exhibit power-law correlated behavior within a trading day and a consistently
greater degree of correlation over larger time scales, in agreement with the
correlation behavior of the absolute price returns for the corresponding
company, and (iv) the magnitude series of intertrade time increments is
characterized by long-range power-law correlations suggesting the presence of
nonlinear features in the trading dynamics, while the sign series is
anti-correlated at small scales. Our results suggest that independent of
industry sector, market capitalization and average level of trading activity,
the series of intertrade times exhibit possibly universal scaling patterns,
which may relate to a common mechanism underlying the trading dynamics of
diverse companies. Further, our observation of long-range power-law
correlations and a parallel with the crossover in the scaling of absolute price
returns for each individual stock, support the hypothesis that the dynamics of
transaction times may play a role in the process of price formation.Comment: 8 pages, 5 figures. Presented at The Second Nikkei Econophysics
Workshop, Tokyo, 11-14 Nov. 2002. A subset appears in "The Application of
Econophysics: Proceedings of the Second Nikkei Econophysics Symposium",
editor H. Takayasu (Springer-Verlag, Tokyo, 2003) pp.51-57. Submitted to
Phys. Rev. E on 25 June 200
Comprehensive Analysis of Market Conditions in the Foreign Exchange Market: Fluctuation Scaling and Variance-Covariance Matrix
We investigate quotation and transaction activities in the foreign exchange
market for every week during the period of June 2007 to December 2010. A
scaling relationship between the mean values of number of quotations (or number
of transactions) for various currency pairs and the corresponding standard
deviations holds for a majority of the weeks. However, the scaling breaks in
some time intervals, which is related to the emergence of market shocks. There
is a monotonous relationship between values of scaling indices and global
averages of currency pair cross-correlations when both quantities are observed
for various window lengths .Comment: 13 pages, 10 figure
Quantifying trading behavior in financial markets using Google Trends
Crises in financial markets affect humans worldwide. Detailed market data on trading decisions reflect some of the complex human behavior that has led to these crises. We suggest that massive new data sources resulting from human interaction with the Internet may offer a new perspective on the behavior of market participants in periods of large market movements. By analyzing changes in Google query volumes for search terms related to finance, we find patterns that may be interpreted as “early warning signs” of stock market moves. Our results illustrate the potential that combining extensive behavioral data sets offers for a better understanding of collective human behavior
Index Cohesive Force Analysis Reveals That the US Market Became Prone to Systemic Collapses Since 2002
BACKGROUND: The 2007-2009 financial crisis, and its fallout, has strongly emphasized the need to define new ways and measures to study and assess the stock market dynamics. METHODOLOGY/PRINCIPAL FINDINGS: The S&P500 dynamics during 4/1999-4/2010 is investigated in terms of the index cohesive force (ICF--the balance between the stock correlations and the partial correlations after subtraction of the index contribution), and the Eigenvalue entropy of the stock correlation matrices. We found a rapid market transition at the end of 2001 from a flexible state of low ICF into a stiff (nonflexible) state of high ICF that is prone to market systemic collapses. The stiff state is also marked by strong effect of the market index on the stock-stock correlations as well as bursts of high stock correlations reminiscence of epileptic brain activity. CONCLUSIONS/SIGNIFICANCE: The market dynamical states, stability and transition between economic states was studies using new quantitative measures. Doing so shed new light on the origin and nature of the current crisis. The new approach is likely to be applicable to other classes of complex systems from gene networks to the human brain
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
Minding impacting events in a model of stochastic variance
We introduce a generalisation of the well-known ARCH process, widely used for
generating uncorrelated stochastic time series with long-term non-Gaussian
distributions and long-lasting correlations in the (instantaneous) standard
deviation exhibiting a clustering profile. Specifically, inspired by the fact
that in a variety of systems impacting events are hardly forgot, we split the
process into two different regimes: a first one for regular periods where the
average volatility of the fluctuations within a certain period of time is below
a certain threshold and another one when the local standard deviation
outnumbers it. In the former situation we use standard rules for
heteroscedastic processes whereas in the latter case the system starts
recalling past values that surpassed the threshold. Our results show that for
appropriate parameter values the model is able to provide fat tailed
probability density functions and strong persistence of the instantaneous
variance characterised by large values of the Hurst exponent is greater than
0.8, which are ubiquitous features in complex systems.Comment: 18 pages, 5 figures, 1 table. To published in PLoS on
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