fisher information for a complex gaussian random variable beamforming applications for wave propagation in a random medium we derive a general expression for the fisher information for a special class of complex gaussian random variables whose mean and covariance may be expressed in terms of a specific parameterized linear transformation the deterministic unknown parameter case is considered this class is descriptive of signals that propagate in a homogeneous or inhomogeneous medium and also occurs in numerous beamforming applications the fisher information derived here encompasses a wide range of previously derived well known results for complex gaussian signals propagating in a homogeneous medium and is also applicable to more recent models for signals propagating in an inhomogenous medium the conditions for the estimates of the azimuth and elevation to decouple from the estimates of the nuisance parameters are studied for monochromatic plane wave and spherical wave propagation in a random medium for propagation in an anisotropic medium it is found that in general the cramer rao lower bounds crlbs of the entire unknown parameter set will be coupled for special cases of propagation in an isotropic medium it is found that for plane waves the crlbs of the angles of arrival will be approximately decoupled from the crlbs of the other unknowns and for spherical waves the crlbs of the angles of arrival will be always be coupled to the crlbs of all other unknowns although the couplings may be minimized under certain conditions

multifractal properties of the indian financial market abstract we investigate the multifractal properties of the logarithmic returns of the indian financial indices bse nse by applying the multifractal detrended fluctuation analysis the results are compared with that of the us s p 500 index numerically we find that q th order generalized hurst exponents h q and q change with the moments q the nonlinear dependence of these scaling exponents and the singularity spectrum f show that the returns possess multifractality by comparing the mf dfa results of the original series to those for the shuffled series we find that the multifractality is due to the contributions of long range correlations as well as the broad probability density function the financial markets studied here are compared with the binomial multifractal model bmfm and have a smaller multifractal strength than the bmfm

maximum likelihood estimation of the parameters of a plane wave tone at an equispaced linear array maximum likelihood estimation of the amplitude phase frequency and arrival angle of a plane wave tone at an m element linear array using equispaced discrete time observations is considered the estimator is obtained for the case when the signal amplitude at each element is equal and is extended to the case of arbitrary amplitudes the cramer rao bounds are obtained for both cases and a monte carlo performance study is presented

long range power law correlations in stock returns abstract this study investigates long range power law correlations in us uk japanese german french and spanish stock markets using daily data and applying a recently developed residual analysis termed detrended fluctuation analysis dfa we quantify correlations for the returns absolute value of returns and square of returns the results show that there is little evidence of long range correlations in returns but there is strong evidence of long range correlation in absolute and squared returns for the absolute returns a cross over of approximately 41 days is found

can one make any crash prediction in finance using the local hurst exponent idea abstract we apply the hurst exponent idea for investigation of djia index time series data the behavior of the local hurst exponent prior to drastic changes in financial series signal is analyzed the optimal length of the time window over which this exponent can be calculated in order to make some meaningful predictions is discussed our prediction hypothesis is verified with examples of 1929 and 1987 crashes as well as with more recent phenomena in stock market from the period 1995 to 2003 some interesting agreements are found

the predictive power of local properties of financial networks abstract the literature on analyzing the dynamics of financial networks has focused so far on the predictive power of global measures of networks like entropy or index cohesive force in this paper i show that the local network properties have similar predictive power i focus on key network measures like average path length average degree or cluster coefficient and also consider the diameter and the s metric using granger causality tests i show that some of these measures have statistically significant prediction power with respect to the dynamics of aggregate stock market average path length is most robust relative to the frequency of data used or specification index or growth rate most measures are found to have predictive power only for monthly frequency further evidences that support this view are provided through a simple regression model

distributions of singular values for some random matrices the singular value decomposition is a matrix decomposition technique widely used in the analysis of multivariate data such as complex space time images obtained in both physical and biological systems in this paper we examine the distribution of singular values of low rank matrices corrupted by additive noise past studies have been limited to uniform uncorrelated noise using diagrammatic and saddle point integration techniques we extend these results to heterogeneous and correlated noise sources we also provide perturbative estimates of error bars on the reconstructed low rank matrix obtained by truncating a singular value decomposition 1999 the american physical society

multifractal behavior of the korean stock market index kospi abstract we investigate multifractality in the korean stock market index kospi the generalized qth order height height correlation function shows multiscaling properties there are two scaling regimes with a crossover time around t c 40 min we consider the original data sets and the modified data sets obtained by removing the daily jumps which occur due to the difference between the closing index and the opening index to clarify the origin of the multifractality we also smooth the data through convolution with a gaussian function after convolution we observe that the multifractality disappears in the short time scaling regime t t c but remains in the long time scaling regime t t c regardless of whether or not the daily jumps are removed we suggest that multifractality in the short time scaling regime is caused by the local fluctuations of the stock index but the multifractality in the long time scaling regime appears to be due to the intrinsic trading properties such as herding behavior information outside the market the long memory of the volatility and the nonlinear dynamics of the stock market

cross correlation dynamics in financial time series abstract the dynamics of the equal time cross correlation matrix of multivariate financial time series is explored by examination of the eigenvalue spectrum over sliding time windows empirical results for the s p 500 and the dow jones euro stoxx 50 indices reveal that the dynamics of the small eigenvalues of the cross correlation matrix over these time windows oppose those of the largest eigenvalue this behaviour is shown to be independent of the size of the time window and the number of stocks examined a basic one factor model is then proposed which captures the main dynamical features of the eigenvalue spectrum of the empirical data through the addition of perturbations to the one factor model leading to a market plus sectors model additional sectoral features are added resulting in an inverse participation ratio comparable to that found for empirical data by partitioning the eigenvalue time series we then show that negative index returns drawdowns are associated with periods where the largest eigenvalue is greatest while positive index returns drawups are associated with periods where the largest eigenvalue is smallest the study of correlation dynamics provides some insight on the collective behaviour of traders with varying strategies

volatility and correlation based systemic risk measures in the us market this paper deals with the problem of how to use simple systemic risk measures to assess portfolio risk characteristics using three simple examples taken from previous literature one based on raw and partial correlations another based on the eigenvalue decomposition of the covariance matrix and the last one based on an eigenvalue entropy a granger causation analysis revealed some of them are not always a good measure of risk in the sp therefore in the sense of risk as volatility the indicators are not always suitable nevertheless their results towards returns are similar to previous works that accept them a deeper analysis has shown that any symmetric measure based on eigenvalue decomposition of correlation matrices however is not useful as a measure of correlation risk the empirical counterpart analysis of this proposition stated that negative correlations are usually small and therefore do not heavily distort the behavior of the indicator

measuring multifractality of stock price fluctuation using multifractal detrended fluctuation analysis abstract analyzing the shanghai stock price index daily returns using mf dfa method it is found that there are two different types of sources for multifractality in time series namely fat tailed probability distributions and non linear temporal correlations based on that a sliding window of 240 frequency data in 5 trading days was used to study stock price index fluctuation it is found that when the stock price index fluctuates sharply a strong variability is clearly characterized by the generalized hurst exponents h q therefore two measures h and based on generalized hurst exponents were proposed to compare financial risks before and after price limits and reform of non tradable shares the empirical results verify the validity of the measures and this has led to a better understanding of complex stock markets

on the hopf algebraic origin of wick normal ordering a combinatorial formula of rota and stein is taken to perform wick reordering in quantum field theory wick s theorem becomes a hopf algebraic identity called cliffordization the combinatorial method relying on hopf algebras is highly efficient in computations and yields closed algebraic expressions

forbidden patterns permutation entropy and stock market inefficiency abstract in this paper we introduce two new quantifiers for the stock market inefficiency the number of forbidden patterns and the normalized permutation entropy they are model independent measures thus they have more general applicability we find robust evidence that degree of market inefficiency is positively correlated with the number of forbidden patterns and negatively correlated with the permutation entropy our empirical results suggest that these two physical tools are useful to discriminate the stage of stock market development and can be easily implemented

estimation of azimuth and elevation angles for a plane wave sine wave with a 3 d array provides expressions for the cramer rao lower bounds for the estimates of azimuth and elevation made with an arbitrary 3 d array of sensors these results are formulated for a plane wave sine wave signal in uncorrelated gaussian noise in addition sufficient conditions on the array geometry are given which ensure that the azimuth and elevation angle estimates are uncoupled

statistical properties of the volatility of price fluctuations we study the statistical properties of volatility measured by locally averaging over a time window t the absolute value of price changes over a short time interval ensuremath delta t we analyze the s 500 stock index for the 13 year period jan 1984 to dec 1996 we find that the cumulative distribution of the volatility is consistent with a power law asymptotic behavior characterized by an exponent ensuremath mu ensuremath approx 3 similar to what is found for the distribution of price changes the volatility distribution retains the same functional form for a range of values of t further we study the volatility correlations by using the power spectrum analysis both methods support a power law decay of the correlation function and give consistent estimates of the relevant scaling exponents also both methods show the presence of a crossover at approximately 1 5 days in addition we extend these results to the volatility of individual companies by analyzing a data base comprising all trades for the largest 500 u s companies over the two year period jan 1994 to dec 1995

evolution of worldwide stock markets correlation structure and correlation based graphs we investigate the daily correlation present among market indices of stock exchanges located all over the world in the time period january 1996 to july 2009 we discover that the correlation among market indices presents both a fast and a slow dynamics the slow dynamics reflects the development and consolidation of globalization the fast dynamics is associated with critical events that originate in a specific country or region of the world and rapidly affect the global system we provide evidence that the short term time scale of correlation among market indices is less than 3 trading months about 60 trading days the average values of the nondiagonal elements of the correlation matrix correlation based graphs and the spectral properties of the largest eigenvalues and eigenvectors of the correlation matrix are carrying information about the fast and slow dynamics of the correlation of market indices we introduce a measure of mutual information based on link co occurrence in networks in order to detect the fast dynamics of successive changes of correlation based graphs in a quantitative way

predictability of multifractal analysis of hang seng stock index in hong kong in this paper the daily hang seng index in hong kong stock market is studied by multifractal analysis the main parameter of multifractal spectra used is f which can be used to characterize the ratio of number of highest index moments to that of lowest ones the dependence of today s gain probability g and the day s index increase probability n with f of the previous 3 days are studied it is found that g and n can reach 70 80 at the large positive f region and can be very close to 20 at the big negative f region the predictability decreases with the increasing number of the previous days

modeling the predictive power of the singular value decomposition based entropy empirical evidence from the dow jones global titans 50 index abstract in this paper we are analyzing the predictive power of the singular value decomposition entropy for the dow jones global titans 50 djgt index of the new york stock exchange nyse market for the correlation matrix of the djgt index components we then calculate the entropy of the singular value decomposition granger causality test was used to prove that entropy has a predictive power for stock market dynamics of the djgt index the results of our paper are revealing the importance of considering structural breaks when analyzing financial stock markets our analysis is conducting to the conclusion that the singular value decomposition entropy has predictive power for the nyse djgt index

entropy and predictability of stock market returns abstract we examine the predictability of stock market returns by employing a new metric entropy measure of dependence with several desirable properties we compare our results with a number of traditional measures the metric entropy is capable of detecting nonlinear dependence within the returns series and is also capable of detecting nonlinear affinity between the returns and their predictions obtained from various models thereby serving as a measure of out of sample goodness of fit or model adequacy several models are investigated including the linear and neural network models as well as nonparametric and recursive unconditional mean models we find significant evidence of small nonlinear unconditional serial dependence within the returns series but fragile evidence of superior conditional predictability profit opportunity when using market switching versus buy and hold strategies

topological properties of stock networks based on minimal spanning tree and random matrix theory in financial time series abstract we investigated the topological properties of stock networks constructed by a minimal spanning tree we compared the original stock network with the estimated network the original network is obtained by the actual stock returns while the estimated network is the correlation matrix created by random matrix theory we found that the consistency between the two networks increases as more eigenvalues are considered in addition we suggested that the largest eigenvalue has a significant influence on the formation of stock networks

long term memory in stock market prices a test for long term memory that is robust to short range dependence is developed it is a modification of the r s statistic and the relevant asymptotic sampling theory is derived via functional central limit theory contrary to previous findings when applied to daily and monthly stock returns indexes over several time periods this test yields no evidence of long range dependence once short range dependence is accounted for monte carlo experiments show that the modified r s test has power against at least two specific models of long term memory suggesting that models with short range dependence may adequately capture the behavior of historical stock returns copyright 1991 by the econometric society

dynamic asset trees and black monday abstract the minimum spanning tree based on the concept of ultrametricity is constructed from the correlation matrix of stock returns the dynamics of this asset tree can be characterised by its normalised length and the mean occupation layer as measured from an appropriately chosen centre called the central node we show how the tree length shrinks during a stock market crisis black monday in this case and how a strong reconfiguration takes place resulting in topological shrinking of the tree

renormalizing rectangles and other topics in random matrix theory we consider random hermitian matrices made of complex or realm n rectangular blocks where the blocks are drawn from various ensembles these matrices haven pairs of opposite real nonvanishing eigenvalues as well asm n zero eigenvalues form n these zero eigenvalues are kinematical in the sense that they are independent of randomness we study the eigenvalue distribution of these matrices to leading order in the large n m limit in which the rectangularity r m n is held fixed we apply a variety of methods in our study we study gaussian ensembles by a simple diagrammatic method by the dyson gas approach and by a generalization of the kazakov method these methods make use of the invariance of such ensembles under the action of symmetry groups the more complicated wigner ensemble which does not enjoy such symmetry properties is studied by large n renormalization techniques in addition to the kinematical function spike in the eigenvalue density which corresponds to zero eigenvalues we find for both types of ensembles that if r 1 is held fixed asn then nonzero eigenvalues give rise to two separated lobes that are located symmetrically with respect to the origin this separation arises because the nonzero eigenvalues are repelled macroscopically from the origin finally we study the oscillatory behavior of the eigenvalue distribution near the endpoints of the lobes a behavior governed by airy functions asr 1 the lobes come closer and the airy oscillatory behavior near the endpoints that are close to zero breaks down we interpret this breakdown as a signal thatr 1 drives a crossover to the oscillation governed by bessel functions near the origin for matrices made of square blocks

equivalence of tba and qtm the traditional thermodynamic bethe ansatz equations for the xxz model at 1 are derived within the quantum transfer matrix method this provides further evidence of the equivalence of both methods most importantly we derive an integral equation for the free energy formulated for just one unknown function this integral equation differs in physical and mathematical aspects to the established ones the single integral equation is analytically continued to the regime 1

the predictive power of singular value decomposition entropy for stock market dynamics abstract we use a correlation based approach to analyze financial data from the us stock market both daily and monthly observations from the dow jones we compute the entropy based on the singular value decomposition of the correlation matrix for the components of the dow jones industrial index based on a moving window we derive time varying measures of entropy for both daily and monthly data we find that the entropy has a predictive ability with respect to stock market dynamics as indicated by the granger causality tests

multidimensional minimal spanning tree the dow jones case abstract this paper introduces a new methodology in order to construct minimal spanning trees mst and hierarchical trees ht using the information provided by more than one variable in fact the symbolic time series analysis stsa approach is applied to the dow jones companies using information not only from asset returns but also for trading volume the us stock market structure is obtained showing eight clusters of companies and general electric as a central node in the tree we use different partitions showing that the results do not depend on the particular partition in addition we apply monte carlo simulations suggesting that the tree is not the result of random connections

probability distribution function and multiscaling properties in the korean stock market abstract we consider the probability distribution function pdf and the multiscaling properties of the index and the traded volume in the korean stock market we observed the power law of the pdf at the fat tail region for the return volatility the traded volume and changes of the traded volume we also investigate the multifractality in the korean stock market we consider the multifractality by the detrended fluctuation analysis mfdfa we observed the multiscaling behaviors for index return traded volume and the changes of the traded volume we apply mfdfa method for the randomly shuffled time series to observe the effects of the autocorrelations the multifractality is strongly originated from the long time correlations of the time series

pruning a minimum spanning tree abstract this work employs various techniques in order to filter random noise from the information provided by minimum spanning trees obtained from the correlation matrices of international stock market indices prior to and during times of crisis the first technique establishes a threshold above which connections are considered affected by noise based on the study of random networks with the same probability density distribution of the original data the second technique is to judge the strength of a connection by its survival rate which is the amount of time a connection between two stock market indices endures the idea is that true connections will survive for longer periods of time and that random connections will not that information is then combined with the information obtained from the first technique in order to create a smaller network in which most of the connections are either strong or enduring in time

interacting individuals leading to zipf s law institut de physique th eorique universit e de fribourg ch 1700 february 1 2008 we present a general approach to explain the zipf s law of city distribution if the simplestinteraction pairwise is assumed individuals tend to form cities in agreement with the well knownstatisticspacs numbers 89 50 r 05 20 y 05 40 j

correlation and network analysis of global financial indices random matrix theory rmt and network methods are applied to investigate the correlation and network properties of 20 financial indices the results are compared before and during the financial crisis of 2008 in the rmt method the components of eigenvectors corresponding to the second largest eigenvalue form two clusters of indices in the positive and negative directions the components of these two clusters switch in opposite directions during the crisis the network analysis uses the fruchterman reingold layout to find clusters in the network of indices at different thresholds at a threshold of 0 6 before the crisis financial indices corresponding to the americas europe and asia pacific form separate clusters on the other hand during the crisis at the same threshold the american and european indices combine together to form a strongly linked cluster while the asia pacific indices form a separate weakly linked cluster if the value of the threshold is further increased to 0 9 then the european indices france germany and the united kingdom are found to be the most tightly linked indices the structure of the minimum spanning tree of financial indices is more starlike before the crisis and it changes to become more chainlike during the crisis the average linkage hierarchical clustering algorithm is used to find a clearer cluster structure in the network of financial indices the cophenetic correlation coefficients are calculated and found to increase significantly which indicates that the hierarchy increases during the financial crisis these results show that there is substantial change in the structure of the organization of financial indices during a financial crisis

efficiency and multifractality analysis of csi 300 based on multifractal detrending moving average algorithm abstract we apply the multifractal detrending moving average mfdma to investigate and compare the efficiency and multifractality of 5 min high frequency china securities index 300 csi 300 the results show that the csi 300 market becomes closer to weak form efficiency after the introduction of csi 300 future we find that the csi 300 is featured by multifractality and there are less complexity and risk after the csi 300 index future was introduced with the shuffling surrogating and removing extreme values procedures we unveil that extreme events and fat distribution are the main origin of multifractality besides we discuss the knotting phenomena in multifractality and find that the scaling range and the irregular fluctuations for large scales in the f q s vs s plot can cause a knot

does the singular value decomposition entropy have predictive power for stock market evidence from the shenzhen stock market abstract this paper analyzes the predictive ability of the singular value decomposition entropy for the shenzhen component index based on different scales it is found that the predictive ability of the entropy for the index is affected by the width of moving time windows and the structural break in stock market by moving time windows with one year the predictive power of singular value decomposition entropy of shenzhen stock market for its component index is found after the reform of non tradable shares

the effect of a market factor on information flow between stocks using the minimal spanning tree abstract we empirically investigated the effects of market factors on the information flow created from n n 1 2 linkage relationships among stocks we also examined the possibility of employing the minimal spanning tree mst method which is capable of reducing the number of links to n 1 we determined that market factors carry important information value regarding information flow among stocks moreover the information flow among stocks showed time varying properties according to the changes in market status in particular we noted that the information flow increased dramatically during periods of market crises finally we confirmed via the mst method that the information flow among stocks could be assessed effectively with the reduced linkage relationships among all links among stocks from the perspective of the overall market

scaling in the norwegian stock market the main objective of this paper is to investigate the validity of the much used assumptions that stock market returns follow a random walk and are normally distributed for this purpose the concepts of chaos theory and fractals are applied two independent models are used to examine price variations in the norwegian and us stock markets the first model used is the range over standard deviation or r s statistic which tests for persistence or antipersistence in the time series both the norwegian and us stock markets show significant persistence caused by long run memory components in the series in addition an average non periodic cycle of four years is found for the us stock market these results are not consistent with the random walk assumption the second model investigates the distributional scaling behaviour of the high frequency price variations in the norwegian stock market the results show a remarkable constant scaling behaviour between different time intervals this means that there is no intrinsic time scale for the dynamics of stock price variations the relationship can be expressed through a scaling exponent describing the development of the distributions as the time scale changes this description may be important when constructing or improving pricing models such that they coincide more closely with the observed market behaviour the empirical distributions of high frequency price variations for the norwegian stock market is then compared to the levy stable distribution with the relevant scaling exponent found by using the r s and distributional scaling analysis good agreement is found between the levy profile and the empirical distribution for price variations less than 6 standard deviations covering almost three orders of magnitude in the data for probabilities larger than 6 standard deviations there seem to be an exponential fall off from the levy profile in the tails which indicates that the second moment may be finite

multiscale shannon entropy and its application in the stock market abstract in this paper we perform a multiscale entropy analysis on the dow jones industrial average index using the shannon entropy the stock index shows the characteristic of multi scale entropy that caused by noise in the market the entropy is demonstrated to have significant predictive ability for the stock index in both long term and short term and empirical results verify that noise does exist in the market and can affect stock price it has important implications on market participants such as noise traders

the predictive power of japanese candlestick charting in chinese stock market abstract this paper studies the predictive power of 4 popular pairs of two day bullish and bearish japanese candlestick patterns in chinese stock market based on morris study we give the quantitative details of definition of long candlestick which is important in two day candlestick pattern recognition but ignored by several previous researches and we further give the quantitative definitions of these four pairs of two day candlestick patterns to test the predictive power of candlestick patterns on short term price movement we propose the definition of daily average return to alleviate the impact of correlation among stocks overlap time returns in statistical tests to show the robustness of our result two methods of trend definition are used for both the medium market value and large market value sample sets we use step spa test to correct for data snooping bias statistical results show that the predictive power differs from pattern to pattern three of the eight patterns provide both short term and relatively long term prediction another one pair only provide significant forecasting power within very short term period while the rest three patterns present contradictory results for different market value groups for all the four pairs the predictive power drops as predicting time increases and forecasting power is stronger for stocks with medium market value than those with large market value

using renyi parameter to improve the predictive power of singular value decomposition entropy on stock market abstract this paper generalizes the method of traditional singular value decomposition entropy by incorporating orders q of renyi entropy we analyze the predictive power of the entropy based on trajectory matrix using shanghai composite index and dow jones index data in both static test and dynamic test in the static test on sci results of global granger causality tests all turn out to be significant regardless of orders selected but this entropy fails to show much predictability in american stock market in the dynamic test we find that the predictive power can be significantly improved in sci by our generalized method but not in dji this suggests that noises and errors affect sci more frequently than dji in the end results obtained using different length of sliding window also corroborate this finding

from the quantum jacobi trudi and giambelli formula to a nonlinear integral equation for thermodynamics of the higher spin heisenberg model we propose a nonlinear integral equation nlie with only one unknown function which gives the free energy of the integrable one dimensional heisenberg model with arbitrary spin in deriving the nlie the quantum jacobi trudi and giambelli formula bazhanov reshetikhin formula which gives the solution of the t system plays an important role in addition we also calculate the high temperature expansion of the specific heat and the magnetic susceptibility

how long the singular value decomposed entropy predicts the stock market evidence from the dow jones industrial average index in this paper a new concept of multi scales singular value decomposition entropy based on dcca cross correlation analysis is proposed and its predictive power for the dow jones industrial average index is studied using granger causality analysis with different time scales it is found that the singular value decomposition entropy has predictive power for the dow jones industrial average index for period less than one month but not for more than one month this shows how long the singular value decomposition entropy predicts the stock market that extends caraiani s result obtained in caraiani 2014 on the other hand the result also shows an essential characteristic of stock market as a chaotic dynamic system

a permutation entropy based test for causality the volume stock price relation abstract the purpose of this paper is to propose a newly developed non parametric test for linear and nonlinear causality based on permutation entropy and to show its usefulness in analyzing the potential causal relationship between trading volume and security prices most of the empirical applications and tests for causality rely on using granger causality based test for linear models although these tests have high power in uncovering linear causal relations their power against nonlinear causal relations can be low our test is designed to deal with the detection of linear and non linear causality we also compare our permutation entropy based test with other granger causality tests monte carlo simulations show excellent performance in terms of size and power of the new test for detecting linear and non linear causality under different scenarios our conclusions point that there is a bidirectional causal relation from volume to price returns not only in the mean but also in the variance

analysis of the efficiency of the shanghai stock market a volatility perspective abstract by applying the rolling window method we investigate the efficiency of the shanghai stock market through the dynamic changes of local hurst exponents based on multifractal detrended fluctuation analysis we decompose the realized volatility into continuous sample paths and jump components and analyze their long range correlations of decomposing components our results reveal that the efficiency of the shanghai stock market improved greatly based on the time varying hurst exponents

nonlinear integral equations for thermodynamics of the sl r 1 uimin sutherland model we derive traditional thermodynamic bethe ansatz tba equations for the sl r 1 uimin sutherland model from the t system of the quantum transfer matrix these tba equations are identical to the those from the string hypothesis next we derive a new family of nonlinear integral equations nlies in particular a subset of these nlies forms a system of nlies which contains only a finite number of unknown functions for r 1 this subset of nlies reduces to takahashi s nlie for the xxx spin chain a relation between the traditional tba equations and our new nlies is clarified based on our new nlies we also calculate the high temperature expansion of the free energy

cross correlations in an emerging market financial data abstract we report the results of a study of the spectral properties of the correlation matrix of price fluctuations in the stock prices in an emerging market istanbul stock exchange market by using random matrix theory although properties such as the distributions of correlation matrix elements eigenvalue spacings and components of the eigenvectors show similar trends to those observed for the other markets the eigenvalue distribution is found to be markedly different compared to similar studies

long range correlations and nonstationarity in the brazilian stock market abstract we report an empirical study of the ibovespa index of the sao paulo stock exchange in which we detect the existence of long range correlations to analyze our data we introduce a rescaled variant of the usual detrended fluctuation analysis that allows us to obtain the hurst exponent through a one parameter fitting we also compute a time dependent hurst exponent h t using 3 year moving time windows in particular we find that before the launch of the collor plan in 1990 the curve h t remains in general well above 1 2 while afterwards it stays close to 1 2 we thus argue that the structural reforms set off by the collor plan has lead to a more efficient stock market in brazil we also suggest that the time dependence of the ibovespa hurst exponent could be described in terms of a multifractional brownian motion

a multifractal detrended fluctuation analysis of trading behavior of individual and institutional traders in tehran stock market abstract employing the multifractal detrended fluctuation analysis mf dfa the multifractal properties of trading behavior of individual and institutional traders in the tehran stock exchange tse are numerically investigated using daily trading volume time series of these two categories of traders the scaling exponents generalized hurst exponents generalized fractal dimensions and singularity spectrum are derived furthermore two main sources of multifractality i e temporal correlations and fat tailed probability distributions are also examined we also compare our results with data of s p 500 results of this paper suggest that for both classes of investors in tse multifractality is mainly due to long range correlation while for s p 500 the fat tailed probability distribution is the main source of multifractality

multifractal characterization of energy stocks in china a multifractal detrended fluctuation analysis abstract in this paper we investigate the impacts of oil price changes on energy stocks in chinese stock market from the multifractal perspective the well known multifractal detrended fluctuation analysis mf dfa is applied to detect the multifractality we find that both returns and volatilities of energy industry index display apparent multifractal behavior oil market activity is an important source of multifractality in energy stocks index in addition to long range correlations and fat tail distributions