Showing papers by "H. Eugene Stanley published in 2014"

Spontaneous recovery in dynamical networks

Abstract: Networks that fail can sometimes recover spontaneously—think of traffic jams suddenly easing or people waking from a coma. A model for such recoveries reveals spontaneous ‘phase flipping’ between high-activity and low-activity modes, in analogy with first-order phase transitions near a critical point.

Reputation and impact in academic careers

Abstract: Reputation is an important social construct in science, which enables informed quality assessments of both publications and careers of scientists in the absence of complete systemic information. However, the relation between reputation and career growth of an individual remains poorly understood, despite recent proliferation of quantitative research evaluation methods. Here, we develop an original framework for measuring how a publication's citation rate Δc depends on the reputation of its central author i, in addition to its net citation count c. To estimate the strength of the reputation effect, we perform a longitudinal analysis on the careers of 450 highly cited scientists, using the total citations Ci of each scientist as his/her reputation measure. We find a citation crossover c×, which distinguishes the strength of the reputation effect. For publications with c < c×, the author's reputation is found to dominate the annual citation rate. Hence, a new publication may gain a significant early advantage corresponding to roughly a 66% increase in the citation rate for each tenfold increase in Ci. However, the reputation effect becomes negligible for highly cited publications meaning that, for c ≥ c×, the citation rate measures scientific impact more transparently. In addition, we have developed a stochastic reputation model, which is found to reproduce numerous statistical observations for real careers, thus providing insight into the microscopic mechanisms underlying cumulative advantage in science.

Quantifying the semantics of search behavior before stock market moves

Abstract: Technology is becoming deeply interwoven into the fabric of society. The Internet has become a central source of information for many people when making day-to-day decisions. Here, we present a method to mine the vast data Internet users create when searching for information online, to identify topics of interest before stock market moves. In an analysis of historic data from 2004 until 2012, we draw on records from the search engine Google and online encyclopedia Wikipedia as well as judgments from the service Amazon Mechanical Turk. We find evidence of links between Internet searches relating to politics or business and subsequent stock market moves. In particular, we find that an increase in search volume for these topics tends to precede stock market falls. We suggest that extensions of these analyses could offer insight into large-scale information flow before a range of real-world events.

Partial correlation analysis: Applications for financial markets

Abstract: The presence of significant cross-correlations between the synchronous time evolution of a pair of equity returns is a well-known empirical fact. The Pearson correlation is commonly used to indicate the level of similarity in the price changes for a given pair of stocks, but it does not measure whether other stocks influence the relationship between them. To explore the influence of a third stock on the relationship between two stocks, we use a partial correlation measurement to determine the underlying relationships between financial assets. Building on previous work, we present a statistically robust approach to extract the underlying relationships between stocks from four different financial markets: the United States, the United Kingdom, Japan, and India. This methodology provides new insights into financial market dynamics and uncovers implicit influences in play between stocks. To demonstrate the capabilities of this methodology, we (i) quantify the influence of different companies and, by studying market similarity across time, present new insights into market structure and market stability, and (ii) we present a practical application, which provides information on the how a company is influenced by different economic sectors, and how the sectors interact with each other. These examples demonstrate the effectiveness of this methodology in uncovering information valuable for a range of individuals, including not only investors and traders but also regulators and policy makers.

Correlation between centrality metrics and their application to the opinion model

Abstract: In recent decades, a number of centrality metrics describing network properties of nodes have been proposed to rank the importance of nodes. In order to understand the correlations between centrality metrics and to approximate a high-complexity centrality metric by a strongly correlated low-complexity metric, we first study the correlation between centrality metrics in terms of their Pearson correlation coefficient and their similarity in ranking of nodes. In addition to considering the widely used centrality metrics, we introduce a new centrality measure, the degree mass. The m order degree mass of a node is the sum of the weighted degree of the node and its neighbors no further than m hops away. We find that the B_{n}, the closeness, and the components of x_{1} are strongly correlated with the degree, the 1st-order degree mass and the 2nd-order degree mass, respectively, in both network models and real-world networks. We then theoretically prove that the Pearson correlation coefficient between x_{1} and the 2nd-order degree mass is larger than that between x_{1} and a lower order degree mass. Finally, we investigate the effect of the inflexible antagonists selected based on different centrality metrics in helping one opinion to compete with another in the inflexible antagonists opinion model. Interestingly, we find that selecting the inflexible antagonists based on the leverage, the B_{n}, or the degree is more effective in opinion-competition than using other centrality metrics in all types of networks. This observation is supported by our previous observations, i.e., that there is a strong linear correlation between the degree and the B_{n}, as well as a high centrality similarity between the leverage and the degree.

Robustness of a partially interdependent network formed of clustered networks.

Abstract: Clustering, or transitivity, a behavior observed in real-world networks, affects network structure and function. This property has been studied extensively, but most of this research has been limited to clustering in single networks. The effect of clustering on the robustness of coupled networks, on the other hand, has received much less attention. Only the case of a pair of fully coupled networks with clustering has recently received study. Here we generalize the study of clustering of a fully coupled pair of networks and apply it to a partially interdependent network of networks with clustering within the network components. We show, both analytically and numerically, how clustering within networks affects the percolation properties of interdependent networks, including the percolation threshold, the size of the giant component, and the critical coupling point at which the first-order phase transition changes to a second-order phase transition as the coupling between the networks is reduced. We study two types of clustering, one proposed by Newman [Phys. Rev. Lett. 103, 058701 (2009)] in which the average degree is kept constant while the clustering is changed, and the other by Hackett et al. [Phys. Rev. E 83, 056107 (2011)] in which the degree distribution is kept constant. The first type of clustering is studied both analytically and numerically, and the second is studied numerically.

Network of Interdependent Networks: Overview of Theory and Applications

Abstract: Complex networks appear in almost every aspect of science and technology. Previous work in network theory has focused primarily on analyzing single networks that do not interact with other networks, despite the fact that many real-world networks interact with and depend on each other. Very recently an analytical framework for studying the percolation properties of interacting networks has been introduced. Here we review the analytical framework and the results for percolation laws for a network of networks (NON) formed by \(n\) interdependent random networks. The percolation properties of a network of networks differ greatly from those of single isolated networks. In particular, although networks with broad degree distributions, e.g., scale-free networks, are robust when analyzed as single networks, they become vulnerable in a NON. Moreover, because the constituent networks of a NON are connected by node dependencies, a NON is subject to cascading failure. When there is strong interdependent coupling between networks, the percolation transition is discontinuous (is a first-order transition), unlike the well-known continuous second-order transition in single isolated networks. We also review some possible real-world applications of NON theory.

Dynamical Macroprudential Stress Testing Using Network Theory

Abstract: The increasing frequency and scope of financial crises has made global financial stability one of the major concerns of economic policy and decision makers. Under this highly complex environment, financial and banking supervision has to be thought as a systemic task, focusing not only on the strength of the institutions but also on the interdependent relations among them, unraveling the structure and dynamic of the system under surveillance. Using network theory, we developed a dynamic model to reveal the systemic structure of a banking system, to analyze its sensibility to external shocks and to evaluate the presence of contagious underlying features of the system. As a case study, we make use of the Venezuelan banking system in the period of 1998-2013. The introduced model was able to capture, in a dynamic way, changes in the structure of the system and the sensibility of banks portfolio to external shocks. Results suggest the fruitfulness of this kind of approach to policy makers and supervision agencies to address macro-prudential dynamical stress testing and regulation.

Anomalous diffusion and multifractality enhance mating encounters in the ocean.

Abstract: For millimeter-scale aquatic crustaceans such as copepods, ensuring reproductive success is a challenge as potential mates are often separated by hundreds of body lengths in a 3D environment. At the evolutionary scale, this led to the development of remote sensing abilities and behavioral strategies to locate, to track, and to capture a mate. Chemoreception plays a crucial role in increasing mate encounter rates through pheromone clouds and pheromone trails that can be followed over many body lengths. Empirical evidence of trail following behavior is, however, limited to laboratory experiments conducted in still water. An important open question concerns what happens in the turbulent waters of the surface ocean. We propose that copepods experience, and hence react to, a bulk-phase water pheromone concentration. Here we investigate the mating behavior of two key copepod species, Temora longicornis and Eurytemora affinis, to assess the role of background pheromone concentration and the relative roles played by males and females in mating encounters. We find that both males and females react to background pheromone concentration and exhibit both innate and acquired components in their mating strategies. The emerging swimming behaviors have stochastic properties that depend on pheromone concentration, sex, and species, are related to the level of reproductive experience of the individual tested, and significantly diverge from both the Levy and Brownian models identified in predators searching for low- and high-density prey. Our results are consistent with an adaptation to increase mate encounter rates and hence to optimize reproductive fitness and success.

Search for a liquid-liquid critical point in models of silica

Abstract: Previous research has indicated the possible existence of a liquid-liquid critical point (LLCP) in models of silica at high pressure. To clarify this interesting question we run extended molecular dynamics simulations of two different silica models (WAC and BKS) and perform a detailed analysis of the liquid at temperatures much lower than those previously simulated. We find no LLCP in either model within the accessible temperature range, although it is closely approached in the case of the WAC potential near 4000 K and 5 GPa. Comparing our results with those obtained for other tetrahedral liquids, and relating the average Si–O–Si bond angle and liquid density at the model glass temperature to those of the ice-like β-cristobalite structure, we conclude that the absence of a critical point can be attributed to insufficient “stiffness” in the bond angle. We hypothesize that a modification of the potential to mildly favor larger average bond angles will generate a LLCP in a temperature range that is accessible to simulation. The tendency to crystallize in these models is extremely weak in the pressure range studied, although this tendency will undoubtedly increase with increasing stiffness.

Behavior of the Widom line in critical phenomena.

Abstract: Using linear scaling theory, we study the behavior of response functions extrema in the vicinity of the critical point. We investigate how the speed of convergence of the loci of response function extrema to the Widom line depends on the parameters of the linear scaling theory. We find that when the slope of the coexistence line is near zero, the line of specific heat maxima does not follow the Widom line but instead follows the coexistence line. This has relevance for the detection of liquid-liquid critical points, which can exhibit a near-horizontal coexistence line. Our theoretical predictions are confirmed by computer simulations of a family of spherically symmetric potentials.

Systemic risk and causality dynamics of the world international shipping market

Abstract: Various studies have reported that many economic systems have been exhibiting an increase in the correlation between different market sectors, a factor that exacerbates the level of systemic risk. We measure this systemic risk of three major world shipping markets, (i) the new ship market, (ii) the second-hand ship market, and (iii) the freight market, as well as the shipping stock market. Based on correlation networks during three time periods, that prior to the financial crisis, during the crisis, and after the crisis, minimal spanning trees (MSTs) and hierarchical trees (HTs) both exhibit complex dynamics, i.e., different market sectors tend to be more closely linked during financial crisis. Brownian distance correlation and Granger causality test both can be used to explore the directional interconnectedness of market sectors, while Brownian distance correlation captures more dependent relationships, which are not observed in the Granger causality test. These two measures can also identify and quantify market regression periods, implying that they contain predictive power for the current crisis.

The influence of water on protein properties

Abstract: The “dynamic” or “glass” transition in biomolecules is as important to their functioning as the folding process. This transition occurs in the low temperature regime and has been related to the onset of biochemical activity that is dependent on the hydration level. This protein transition is believed to be triggered by the strong hydrogen bond coupling in the hydration water. We study the vibrational bending mode and measure it using Fourier Transform Infrared spectroscopy. We demonstrate that at the molecular level the hydration water bending mode bonds the C=O and N–H peptide groups, and find that the temperature of the “dynamic” protein transition is the same as the fragile-to-strong dynamic transition in confined water. The fragile-to-strong dynamic transition in water governs the nature of the H bonds between water and peptides and appears to be universal in supercooled glass-forming liquids.

Thermodynamic properties of bulk and confined water

Abstract: The thermodynamic response functions of water display anomalous behaviors. We study these anomalous behaviors in bulk and confined water. We use nuclear magnetic resonance (NMR) to examine the configurational specific heat and the transport parameters in both the thermal stable and the metastable supercooled phases. The data we obtain suggest that there is a behavior common to both phases: that the dynamics of water exhibit two singular temperatures belonging to the supercooled and the stable phase, respectively. One is the dynamic fragile-to-strong crossover temperature (T(L) ≃ 225 K). The second, T* ∼ 315 ± 5 K, is a special locus of the isothermal compressibility K(T)(T, P) and the thermal expansion coefficient α(P)(T, P) in the P-T plane. In the case of water confined inside a protein, we observe that these two temperatures mark, respectively, the onset of protein flexibility from its low temperature glass state (T(L)) and the onset of the unfolding process (T*).

Percolation of localized attack on complex networks

Abstract: The robustness of complex networks against node failure and malicious attack has been of interest for decades, while most of the research has focused on random attack or hub-targeted attack. In many real-world scenarios, however, attacks are neither random nor hub-targeted, but localized, where a group of neighboring nodes in a network are attacked and fail. In this paper we develop a percolation framework to analytically and numerically study the robustness of complex networks against such localized attack. In particular, we investigate this robustness in Erdős-Renyi networks, random-regular networks, and scale-free networks. Our results provide insight into how to better protect networks, enhance cybersecurity, and facilitate the design of more robust infrastructures.

Realized volatility and absolute return volatility: a comparison indicating market risk.

Abstract: Measuring volatility in financial markets is a primary challenge in the theory and practice of risk management and is essential when developing investment strategies. Although the vast literature on the topic describes many different models, two nonparametric measurements have emerged and received wide use over the past decade: realized volatility and absolute return volatility. The former is strongly favored in the financial sector and the latter by econophysicists. We examine the memory and clustering features of these two methods and find that both enable strong predictions. We compare the two in detail and find that although realized volatility has a better short-term effect that allows predictions of near-future market behavior, absolute return volatility is easier to calculate and, as a risk indicator, has approximately the same sensitivity as realized volatility. Our detailed empirical analysis yields valuable guidelines for both researchers and market participants because it provides a significantly clearer comparison of the strengths and weaknesses of the two methods.

Analysis of percolation behaviors of clustered networks with partial support-dependence relations

Abstract: We carry out a study of percolation behaviors of clustered networks with partial support‐dependence relations by adopting two different attacking strategies, attacking only one network and both networks, which help to further understand real coupled networks. For two different attacking strategies we find that the system changes from a second-order phase transition to a first-order phase transition as coupling strengthq increases. We also notice that the first-order region becomes smaller and the secondorder region becomes larger as average degree or clustering coefficient increases. And, as the average supported degree approaches infinity, coupled clustered networks become independent and only the second-order transition is observed, which is similar toq=0. Furthermore, we find that clustering coefficient has a significant impact on robustness of the system for strong coupling strength, but for weak coupling strength it has little influence, especially for attacking both networks. The study implies that we can obtain a more robust network by reducing clustering coefficient and increasing average degree for strong coupling strength. However, for weak coupling strength, a more robust network is obtained only by increasing average degree for the same support average degree.

Dynamic motifs in socio-economic networks

Abstract: Socio-economic networks are of central importance in economic lifeWe develop a method of identifying and studying motifs in socio-economic networks by focusing on "dynamic motifs,"ie, evolutionary connection patterns that, because of "node acquaintances "i n the net- work, occur much more frequently than random patterns We examine two evolving bi-partite networks: i) the world-wide commercial ship chartering market and ii) the shipbuild-to-order market We find similar dynamic motifs in both bipartite networks, eventhough they describe different economic activities We also find that "influence" and "persistence" are strong factors in the interaction behavior of organizations When two companies are doing business with the same customer, it is highly probable that another customer who currently only has business relationship with one of these two companies, will become customer of the second inthe future This is the effect of influence Persistence means that companies with close business ties to customers tend to maintain their relationships over a long period of time

Nonconsensus opinion model on directed networks

Abstract: Dynamic social opinion models have been widely studied on undirected networks, and most of them are based on spin interaction models that produce a consensus In reality, however, many networks such as Twitter and the World Wide Web are directed and are composed of both unidirectional and bidirectional links Moreover, from choosing a coffee brand to deciding who to vote for in an election, two or more competing opinions often coexist In response to this ubiquity of directed networks and the coexistence of two or more opinions in decision-making situations, we study a nonconsensus opinion model introduced by Shao et al [Phys Rev Lett 103, 018701 (2009)] on directed networks We define directionality ? as the percentage of unidirectional links in a network, and we use the linear correlation coefficient ? between the in-degree and out-degree of a node to quantify the relation between the in-degree and out-degree We introduce two degree-preserving rewiring approaches which allow us to construct directed networks that can have a broad range of possible combinations of directionality ? and linear correlation coefficient ? and to study how ? and ? impact opinion competitions We find that, as the directionality ? or the in-degree and out-degree correlation ? increases, the majority opinion becomes more dominant and the minority opinion's ability to survive is lowered

Emergence of dynamical complexity related to human heart rate variability

Abstract: We apply the refined composite multiscale entropy (MSE) method to a one-dimensional directed small-world network composed of nodes whose states are binary and whose dynamics obey the majority rule. We find that the resulting fluctuating signal becomes dynamically complex. This dynamical complexity is caused (i) by the presence of both short-range connections and long-range shortcuts and (ii) by how well the system can adapt to the noisy environment. By tuning the adaptability of the environment and the long-range shortcuts we can increase or decrease the dynamical complexity, thereby modeling trends found in the MSE of a healthy human heart rate in different physiological states. When the shortcut and adaptability values increase, the complexity in the system dynamics becomes uncorrelated.

Nonlinear phenomena in complex systems : from nano to macro scale

Abstract: 1. Complexity in Earthquake Dynamics.- Geosystemics, Entropy and Criticality of Earthquakes: a Vision of our Planet and a Key of Access A. De Santis.- Aftershock Cascade of the 3.11 Earthquake (2011) in Fukushima-Miyagi Area Y. Aizawa, S. Tsugawa.- 2. Socio-, Econo- and Biophysics.- Is it Necessary to lie to Win a Controversial Public Debate: An Answer from Sociophysics S. Galam.- Anticipating Stock Market Movements with Google and Wikipedia H.S. Moat et al.- Nonequilibrium Quantum Dynamics of Biomolecular Excitons C.A. Mujica-Martinez et al.- Fractal Dimensions and Entropies of Meragi Songs A. Aydemir, G. Gunduz.- 3. Network Dynamics in Macroscale Systems.- Large-Scale Connectivity vs. Spreading Efficiency: Spectral Analysis on Explosive Percolation N.N. Chung et al.- Power Grids, Smart Grids and Complex Networks A. Scala et al.- A Spectral Approach to Synchronizability of Interdependent Networks G. D'Agostino.- Theoretical Approaches to the Susceptible-Infected-Susceptible Dynamics on Complex Networks: Mean-Field Theories and Beyond C. Castellano.- 4. Quantum Network Dynamics.- Physics on Graphs R. Schrader.- Resonances in Quantum Networks and their Generalizations P. Exner.- Quantum Graph and Quantum Filter T. Cheon.- From Continuous-Time Random Walks to Continuous-Time Quantum Walks: Disordered Networks O. Mulken, A. Blumen.- Excitations Transfer and Random Walks on Dynamic Contacts Networks R. Burioni et al.- Ballistic Soliton Transport in Networks Z.A. Sobirov et al.- 5. Complexity in Nanoscale Systems.- Symmetry Breaking in Open Quantum Nonlinear Systems A.F. Sadreev et al.- Charge Separation and Transport in Third Generation Hybrid Polymer-Fullerene Solar Cells B.L. Oksengendler et al.- Complex Antenna Optimization H. Liu et al.- Complex Nonlinear Riccati Equations as a Unifying Link in Fundamental Physics D. Schuch.- Index.

When a text is translated does the complexity of its vocabulary change? Translations and target readerships

Abstract: In linguistic studies, the academic level of the vocabulary in a text can be described in terms of statistical physics by using a “temperature” concept related to the text's word-frequency distribution. We propose a “comparative thermo-linguistic” technique to analyze the vocabulary of a text to determine its academic level and its target readership in any given language. We apply this technique to a large number of books by several authors and examine how the vocabulary of a text changes when it is translated from one language to another. Unlike the uniform results produced using the Zipf law, using our “word energy” distribution technique we find variations in the power-law behavior. We also examine some common features that span across languages and identify some intriguing questions concerning how to determine when a text is suitable for its intended readership.

Anticipating Stock Market Movements with Google and Wikipedia

Abstract: Many of the trading decisions that have led to financial crises are captured by vast, detailed stock market datasets. Here, we summarize two of our recent studies which investigate whether Internet usage data contain traces of attempts to gather information before such trading decisions were taken. By analyzing changes in how often Internet users searched for financially related information on Google (Preis et al., Sci Rep 3:1684, 2013) and Wikipedia (Moat et al., Sci Rep 3:1801, 2013), patterns are found that may be interpreted as “early warning signs” of stock market moves. Our results suggest that online data may allow us to gain new insight into early information gathering stages of economic decision making.

Search for a liquid-liquid critical point in models of silica

Abstract: Previous research has indicated the possible existence of a liquid-liquid critical point (LLCP) in models of silica at high pressure. To clarify this interesting question we run extended molecular dynamics simulations of two different silica models (WAC and BKS) and perform a detailed analysis of the liquid at temperatures much lower than those previously simulated. We find no LLCP in either model within the accessible temperature range, although it is closely approached in the case of the WAC potential near 4000 K and 5 GPa. Comparing our results with those obtained for other tetrahedral liquids, and relating the average Si-O-Si bond angle and liquid density at the model glass temperature to those of the ice-like beta-cristobalite structure, we conclude that the absence of a critical point can be attributed to insufficient "stiffness" in the bond angle. We hypothesize that a modification of the potential function to mildly favor larger average bond angles will generate a LLCP in a temperature range that is accessible to simulation. The tendency to crystallize in these models is extremely weak in the pressure range studied, although this tendency will undoubtedly increase with increasing stiffness.

A Systemic Stress Test Model in Bank-Asset Networks

Abstract: Financial networks are dynamic. To assess their systemic importance to the world-wide economic network and avert losses we need models that take the time variations of the links and nodes into account. Using the methodology of classical mechanics and Laplacian determinism we develop a model that can predict the response of the financial network to a shock. We also propose a way of measuring the systemic importance of the banks, which we call BankRank. Using European Bank Authority 2011 stress test exposure data, we apply our model to the bipartite network connecting the largest institutional debt holders of the troubled European countries (Greece, Italy, Portugal, Spain, and Ireland). From simulating our model we can determine whether a network is in a "stable" state in which shocks do not cause major losses, or a "unstable" state in which devastating damages occur. Fitting the parameters of the model, which play the role of physical coupling constants, to Eurozone crisis data shows that before the Eurozone crisis the system was mostly in a "stable" regime, and that during the crisis it transitioned into an "unstable" regime. The numerical solutions produced by our model match closely the actual time-line of events of the crisis. We also find that, while the largest holders are usually more important, in the unstable regime smaller holders also exhibit systemic importance. Our model also proves useful for determining the vulnerability of banks and assets to shocks. This suggests that our model may be a useful tool for simulating the response dynamics of shared portfolio networks.

Discovering social events through online attention.

Abstract: Twitter is a major social media platform in which users send and read messages (“tweets”) of up to 140 characters. In recent years this communication medium has been used by those affected by crises to organize demonstrations or find relief. Because traffic on this media platform is extremely heavy, with hundreds of millions of tweets sent every day, it is difficult to differentiate between times of turmoil and times of typical discussion. In this work we present a new approach to addressing this problem. We first assess several possible “thermostats” of activity on social media for their effectiveness in finding important time periods. We compare methods commonly found in the literature with a method from economics. By combining methods from computational social science with methods from economics, we introduce an approach that can effectively locate crisis events in the mountains of data generated on Twitter. We demonstrate the strength of this method by using it to locate the social events relating to the Occupy Wall Street movement protests at the end of 2011.

NetSci High: Bringing Network Science Research to High Schools

Abstract: We present NetSci High, our NSF-funded educational outreach program that connects high school students who are underrepresented in STEM (Science Technology Engineering and Mathematics), and their teachers, with regional university research labs and provides them with the opportunity to work with researchers and graduate students on team-based, year-long network science research projects, culminating in a formal presentation at a network science conference. This short paper reports the content and materials that we have developed to date, including lesson plans and tools for introducing high school students and teachers to network science; empirical evaluation data on the effect of participation on students' motivation and interest in pursuing STEM careers; the application of professional development materials for teachers that are intended to encourage them to use network science concepts in their lesson plans and curriculum; promoting district-level interest and engagement; best practices gained from our experiences; and the future goals for this project and its subsequent outgrowth.

Structure and Dynamics of the Brazilian Stock Market: A Correlation Analysis

Abstract: The impacts of the Subprime Financial crisis resulted in generally similar decline and volatility in both developed and emerging stock markets. However, in the aftermath of the crisis, the performance of emerging economies was stronger. To investigate this phenomena, this paper seeks to gauge the structure of one of the BRICS economies, the Brazilian stock market, for the period 2003 -- 2013, to determine possible determinants of this behavior.Using a Pearson and partial correlation analysis we find that the IBOVESPA index has little affect on the magnitude of the correlations of its components. This finding differs from results obtained recently for developed markets. Finally, we introduce a novel method to investigate the cohesion and dynamics of financial markets that provide new insights into the structure and dynamics of the stock market and its relation to the real economy.

Capital death in the world market.

Abstract: We study the gross domestic product (GDP) per capita together with the market capitalization (MCAP) per capita as two indicators of the effect of globalization. We find that $g$, the GDP per capita, as a function of $m$, the MCAP per capita, follows a power law with average exponent close to 1/3. In addition, the Zipf ranking approach confirms that the $m$ for countries with initially lower values of $m$ tends to grow more rapidly than for countries with initially larger values of $m$. If the trends over the past 20 years continue to hold in the future, then the Zipf ranking approach leads to the prediction that in about 50 years, all countries participating in globalization will have comparable values of their MCAP per capita. We call this economic state ``capital death,'' in analogy to the physics state of ``heat death'' predicted by thermodynamic arguments.

Multifractal Properties of a Closed Contour: A Peek beyond the Shape Analysis

Abstract: In recent decades multifractal analysis has been successfully applied to characterize the complex temporal and spatial organization of such diverse natural phenomena as heartbeat dynamics, the dendritic shape of neurons, retinal vessels, rock fractures, and intricately shaped volcanic ash particles. The characterization of multifractal properties of closed contours has remained elusive because applying traditional methods to their quasi-one-dimensional nature yields ambiguous answers. Here we show that multifractal analysis can reveal meaningful and sometimes unexpected information about natural structures with a perimeter well-defined by a closed contour. To this end, we demonstrate how to apply multifractal detrended fluctuation analysis, originally developed for the analysis of time series, to an arbitrary shape of a given study object. In particular, we show the application of the method to fish otoliths, calcareous concretions located in fish's inner ear. Frequently referred to as the fish's "black box", they contain a wealth of information about the fish's life history and thus have recently attracted increasing attention. As an illustrative example, we show that a multifractal approach can uncover unexpected relationships between otolith contours and size and age of fish at maturity.