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Understanding, quantifying and predicting market fluctuation has become increasingly important in recent decades. Volatility and volume are the two commonly used quantities to study the market dynamics and the relationship between these two has been modeled and debated for years with several hypothesis been put forward. Using empirical data, we investigate the causality and correlation between volume and volatility and find new ways in which they interact, particularly when the levels of both are high. We find that the volume-conditional volatility distribution scales with volume as a power-law function with an exponential cutoff. We exploit the characteristics of a volume-volatility scatterplot and find a strong correlation between logarithmic volume and a quantity we define as local maximum volatility (LMV), the highest volatility observed in a given range of volume. This supports our empirical analysis, showing that volume is an effective parameter for prediction of the maximum value of volatility for both same-day and near-future time periods. The joint conditional probability of volume and volatility also indicates if we invoke both quantities, the prediction of the largest next-day volatility will be better than invoking either one alone. This approach is thus a greatly improved method of risk assessment.

We analyze the structural and functional robustness of networked critical infrastructure systems (CISs). We propose a structural and functional robustness model of a typical complex network and take into account the corresponding measuring metrics and cascading processes to assess the impact of different hazard modes on robustness. We analyze the robustness of the shanghai subway network and the central China power grid to demonstrate an application of the model. We find that both are strongly robust to random failure but extremely vulnerable to targeted attack. We identify the critical areas most likely to be attack targets and use a functional perspective to identify vulnerabilities. Our proposed method can be applied to other CISs and aid in understanding the mechanisms of network robustness.

The linkages between crude oil spot and futures markets are widely studied in previous literatures. Most of these conventional methods are static, linear and parametric. In order to overcome this difficulty, we employ the nonparametric and nonlinear symmetric thermal optimal path (TOPS) method to determine the time-dependent lead–lag relationship between two time series. Based on the daily, weekly, monthly spot and futures returns for maturities of one, two, three and four months of the West Texas Intermediate (WTI) crude oil benchmark, we apply the TOPS method together with self-consistent test to investigate the time-varying lead–lag dependences between spot and futures markets from 1987 to 2017. We find that, for total three data sets with different frequencies, the TOPS paths indicate an alternate lead–lag structure instead of a dominance between spot and futures markets in most of the time periods. For the daily data, the lead–lag relationship between the two markets exists within one day except on some specific days. Moreover, without considering the units of time, on average, the lead–lag steps between the two markets are diminishing with the increasing frequency. Roughly speaking, these periods with strong lead–lag signals coincide with major influential changes in the oil and stock markets and the geopolitics, which can reflect real economic, financial and geopolitical regime changes. All in all, the results show that the lead–lag relationship between the spot and futures oil markets exists only temporarily.

We construct the empirical information network (EIN) of traders using the order flow data of the constituent stocks of SZSE 100 Index in 2013. A statistical validation method is applied to the edges of the network to filter out noises and uncover the intrinsic interaction behaviors of traders. We investigate the correlation between topological structures and statistical properties for their largest connected components. We find that the statistical validated network shows an assortative mixing pattern while the original network exhibits a disassortative mixing pattern. We consider two definitions of edge weight for comparison but there is no significant difference in a same network. We also analyze the mutual relationships among node degree, edge weight and node strength.

Based on ultrahigh-frequency returns, this paper comprehensively revisits the weak-form efficiency of the euro to Swiss franc (EUR/CHF) exchange rate market from 2002 to 2017, including the efficiency of several long periods and intraday efficiency. To this end, we employ Hurst index as the indicator of the degree of efficiency. The Jarque–Bera test demonstrates that the high-frequency logarithm return of EUR/CHF does not accord with a normal distribution. Further, a strict statistical test in the spirit of bootstrapping is performed to validate the statistical significance of Hurst indices of the EUR/CHF exchange rate returns. The results indicate that the EUR/CHF exchange rate market possesses an extremely mild anti-persistence when the full sample is investigated. Similarly, a weak anti-persistence is also found in five fixed sub-samples which can been roughly split into “free float” periods and “intervention” periods owing to the SNB’s interventions. When it comes to the intraday efficiency tests on EUR/CHF exchange rate market, we find the majority of intraday Hurst indices follow a downward departure from 0.5, which roughly provides an evidence for intraday market inefficiency. Overall, at the level of long period returns, the Hurst values show an approach to 0.5, whereas most Hurst indices of intraday returns exhibit a relatively large deviation from 0.5. Besides, the intraday Hurst indices present that the announcement of lower bound can be approximatively regarded as a turning point of the market efficiency, which potentially indicates that the SNB’s interventions might reduce the efficiency of the studied market.

The Beijing–Tianjin–Hebei (BTH) integration project in China is ambitious which offers great potential with its promotion of sustainable and inclusive development. This study investigates the impact of regional financial development on economic growth in the BTH region, with panel data collected from 2007 to 2016. Two indicators namely, CREDIT (denoted as regional financial development depth) and BRANCH (denoted as regional financial intermediaries accessibility) are used to construct an integrated regional financial development indicator through the spatial econometrics approach. The spatio-temporal distribution characteristics of regional financial development and economic growth are analyzed. Afterward, the global Moran’s I and local Getis–Ord Gi* statistics are applied to detect the presence of spatial autocorrelation. Finally, a spatial Durbin model (SDM) is utilized to examine spatial distribution and spatial association. The research findings of this study suggest that the CREDIT has a positive effect on regional economic growth, while the BRANCH has no impact on regional economic growth. Moreover, it is found that the spatial autocorrelation of CREDIT and BRANCH are statistically significant. The CREDIT of the neighboring areas has a negative spatial spillover effect on economic growth of one area, while the BRANCH in the neighboring areas has a positive effect on the one area. The results and research findings reported in this article highlight the role of regional financial development in improving the economic growth not only for Chinese policy makers but also for other countries’ researchers and practitioners in this field.

We use isonymic distance to measure the dissimilarity in surname structure between populations of Chinese provinces, and we employ the minimum spanning tree (MST) and the single linkage cluster analysis (SLCA) to investigate the hierarchical structure of Chinese provinces and present its corresponding geographical features. We find diverse discrepancies in the averaged isonymic distance among provinces that are attributed to the heterogeneous surname distributions. The MST displays a core–fringe structure with Henan, Anhui, and Hubei making up the core, and several border provinces on the fringe. The degree centrality list in the MST reveals some “local centers” that act as regional economic centers. On the other hand, the geographical layout of MST reflects the historical “Rush to Northeast” mass migration, as well as the blocking effect of the Qinling–Huaihe line that separates north and south China. The clustering results derived from the SLCA show nine groups of provinces in which each group is geographically continuous.

We use a heterogeneous agent-based two-state sociophysics model to simulate financial markets. Focusing on stock market trader dynamics, we propose a model with two kinds of individual – the contrarian agent and the noise trader – in which the dynamics of buying and selling investors are governed by local and global interactions. We define an antiferromagnetic coupling that relates the option of contrarian agents to global magnetization and a ferromagnetic interaction that connects noise traders to their local neighborhood. Our model presents such stylized facts of real financial markets as clustered volatility, power-law distributed returns, and the long-time correlation of the absolute returns with exponential decay. We also observe that the distribution of logarithmic returns can be fitted by the Student’s t distribution in which its degree of freedom changes with the percentage of contrarian agents in the market.

Examining the venture capital market as a representative case, we made the first empirical study about timing and rhythm in organizational behavior. We analyze inter-event time intervals and find that the fat-tailed property of organizational behavior is similar that found in individual behavior, but that its lower scaling exponent indicates it is more heterogeneous than individual behavior. We quantify the observable burst and memory effect in organizational behavior. The burst effect occurs when organizations make frequent business deals during a short time period. This is a potential factor affecting economic cycles and market turbulence. The memory effect occurs when future organizational behavior correlates with past behavior, which potentially allows the prediction of future behavior. We find fewer bursts and a stronger memory effect in organizational behavior than in individual behavior. This difference may be related to resource constraint, business routine, and market competition. These findings shed light on the changeable features of organizational behavior and economic system. We drew a necessity to extend study on human dynamics to organization dynamics by exploring real organizational data in a wide range and proposing a mechanism to better modeling organizational behaviors. Understanding the temporal characteristics of organizational behavior is essential to understanding economic complexities.

Using the theoretical framework of spatial econometrics, we estimate the impact of capital markets, i.e., stock markets, medium-and-long term bond markets, and medium-and-long term loan markets, on industrial structure upgrading (ISU) across 31 provinces in China from 1998 to 2015. We apply an explanatory spatial data analysis (ESDA) to detect the presence of spatial dependence and use a spatial Durbin model (SDM) to examine spatial distribution and spatial association. Although our overall estimation results from the 31 provinces indicate a degree of correlation between capital markets and industrial structure upgrading, the magnitude of spatial spillover effects in three sub-markets on ISU differ. We divide the 31 provinces into four economic zones, and explore the impact of three sub-markets on ISU in the eastern zone, central zone, western zone, and northeastern zone. Using our results, we provide policy suggestions that better facilitate ISU in China.

The dynamics of opinion formation in a society is a complex phenomenon where many variables play essential roles. Recently, the influence of algorithms to filter which content is fed to social networks users has come under scrutiny. Supposedly, the algorithms promote marketing strategies, but can also facilitate the formation of filters bubbles in which a user is most likely exposed to opinions that conform to their own. In the two-state majority-vote model, an individual adopts an opinion contrary to the majority of its neighbors with probability q , defined as the noise parameter. Here, we introduce a visibility parameter V in the dynamics of the majority-vote model, which equals the probability of an individual ignoring the opinion of each one of its neighbors. For V = 0 . 5 each individual will, on average, ignore the opinion of half of its neighboring nodes. We employ Monte Carlo simulations to calculate the critical noise parameter as a function of the visibility q c ( V ) and obtain the phase diagram of the model. We find that the critical noise is an increasing function of the visibility parameter, such that a lower value of V favors dissensus. Via finite-size scaling analysis we obtain the critical exponents of the model, which are visibility-independent, and show that the model belongs to the Ising universality class. We compare our results to the case of a network submitted to a static site dilution and find that the limited visibility model is a more subtle way of inducing opinion polarization in a social network.

We investigate via numerical simulations of the TIP4P/Ice model the isothermal decompression of high density amorphous ice (HDA) mirroring the experimental protocol followed by recent experiments [H. K. Kim, et al., Science 370, 978, 2020]. Taking advantage of the recent determination of the TIP4P/Ice liquid-liquid critical point, we decompress our samples at temperatures both below and above the critical temperature. We follow the crossover between high and low density, complementing the time evolution of the structure factor with the time evolution of microscopic descriptors. Our results support the interpretation of the experimental pathway as a structural change from high to low density states through a first-order transition line. In the simulation study we do not observe nucleation events, but rather a spinodal-like non-equilibrium transformation, which offers an explanation on why the decompression process even at sub-critical temperatures may result in a continuous time evolution of the density and of the microscopic descriptors.

Research on dynamical processes in interconnected spatial networks has expanded in recent years, but there has been little focus on social contagions. Using a general social contagion model, we numerically study how an interconnected spatial system composed of two interconnected planar lattices influences social contagion dynamics. When information is transmitted and allows for a probability of behavior adoption, strongly interconnected lattices stimulate the contagion process and significantly increase the final density of adopted individuals. We perform a finite-size analysis and confirm that the dependency of prevalence on the transmission rate is continuous regardless of the adoption probability. The prevalence grows discontinuously with the adoption probability even when the transmission rate is low. Although a high transmission rate or a high adoption probability increases the final adopted density in weak interconnected lattices, the prevalence always grows continuously in these networks. These findings help us understand social contagion dynamics in interconnected lattices.

As socioeconomic systems continue to develop, their critical infrastructure systems become more intricate and the interdependencies among systems more intensive. This cascading effect on critical infrastructure systems significantly impacts system performance. We develop an approach to quantitatively assess the complex cascading effect on critical infrastructure systems under four different types of attack: (i) random, (ii) malicious, (iii) shell-based and local, and (iv) orientated and local. In the context of these four we study three types of cascading effect – non-cascading, inner-system cascading, and inter-system cascading – in both independent systems and interdependent systems. We model both logical and geographical interdependency. We apply this approach to the Chinese road and railway system and find that (i) the damage done by different types of attack on critical infrastructures systems varies significantly, (ii) under the same type of attack the damage caused by cascading effects on different critical infrastructure systems varies significantly, and (iii) different cascading effects contribute to damage in critical infrastructure systems. These findings indicate that more theoretical and practical research on cascading effects in infrastructure systems under different attacks is needed, especially when the attacks are local and oriented.

The algorithm of limited penetrable horizontal visibility graphs (LPHVGs) including the limited penetrable horizontal visibility graph [LPHVG( ρ )], the directed limited penetrable horizontal visibility graph [DLPHVG( ρ )] and the image limited penetrable horizontal visibility graph [ILPHVG n ( ρ ) )] are used to map time series (or matrices) on graphs and are powerful tools for analyzing time series. We derive the degree distributions of LPHVGs using an iterative LPHVGs construction process. We propose a more intuitive method of reproducing the construction process of limited penetrable horizontal visibility graphs that is simple to calculate. We find that the results confirm the analytical results from previous methods. We then introduce the concept of sequential LPHVG( ρ ) motifs and present a theoretical way of computing the exact motif profiles associated with unrelated random series. We perform several numerical simulations to further check the accuracy of our theoretical results. Finally we use the analytical results of LPHVG( ρ ) motif profiles to distinguish among random, periodic, and chaotic signals and find that the frequency of the type-I motif captures sufficient information to easily distinguish among different processes.

One of the most interesting challenges in network science is to understand the relation between network structure and dynamics on it, and many topological properties, including degree distribution, community strength and clustering coefficient, have been proposed in the last decade. Prominent in this context is the centrality measures, which aim at quantifying the relative importance of individual nodes in the overall topology with regard to network organization and function. However, most of the previous centrality measures have been proposed based on different concepts and each of them focuses on a specific structural feature of networks. Thus, the straightforward and standard methods may lead to some bias against node importance measure. In this paper, we introduce two physical processes with potential complementarity between them. Then we propose to combine them as an elegant integration with the classic eigenvector centrality framework to improve the accuracy of node ranking. To test the produced power iteration ranking (PIRank) algorithm, we apply it to the selection of attack targets in network optimal attack problem. Extensive experimental results on synthetic networks and real-world networks suggest that the proposed centrality performs better than other well-known measures. Moreover, comparing with the eigenvector centrality, the PIRank algorithm can achieve about thirty percent performance improvement while keeping similar running time. Our experiment on random networks also shows that PIRank algorithm can avoid the localization phenomenon of eigenvector centrality, in particular for the networks with high-degree hubs.

Being able to theoretically predict the outbreak threshold of an epidemic is essential in disease control. Real-world correlated networks are ubiquitous and their topological structure strongly affects the prediction accuracy of present-day theoretical methods. Quantifying their accuracy and fitness in predicting outbreak thresholds of correlated networks is thus essential. We use a susceptible–infected–removed (SIR) model to examine four widely-used theoretical methods – the heterogeneous mean-field ( H M F ), quenched mean-field ( Q M F ), dynamical message passing ( D M P ), and connectivity matrix ( C M ) methods – to predict the outbreak threshold of a correlated network. The potential topological structure of correlated network impacts on prediction accuracy of these four methods. We emphasize that the quantitative changes of degree correlation, degree distribution exponent and network size strongly affect the outbreak of SIR spreading dynamics, and compare the simulation results with the theoretical ones obtained from these four methods. The extensive experiments in synthetic networks show that (a) the increasing degree correlation coefficients reduce outbreak threshold and suppress outbreak size; (b) the increasing degree distribution exponents raise outbreak threshold but suppress outbreak size; (c) the increasing network sizes decrease outbreak threshold but do not affect outbreak size; (d) as for four theoretical methods, C M and D M P are more likely to precisely predict outbreak threshold because they to some extent incorporate network topology with dynamical correlations. The experimental results in 50 real-world networks also prove the above conclusions.

Although there have been many network-based attempts to discover disease-associated genes, most of them have not taken edge weight – which quantifies their relative strength – into consideration. We use connection weights in a protein–protein interaction (PPI) network to locate disease-related genes. We analyze the topological properties of both weighted and unweighted PPI networks and design an improved random forest classifier to distinguish disease genes from non-disease genes. We use a cross-validation test to confirm that weighted networks are better able to discover disease-associated genes than unweighted networks, which indicates that including link weight in the analysis of network properties provides a better model of complex genotype–phenotype associations.

It is proposed that the core–periphery structure of complex networks can be simulated by h -cores and fingerprint curves. While the features of core structure are characterized by h -core, the features of periphery structure are visualized by rose or spiral curve as the fingerprint curve linking to entire-network parameters. It is suggested that a complex network can be approached by h -core and rose curves as the first-order Fourier-approach, where the core–periphery structure is characterized by five parameters: network h-index, network radius, degree power, network density and average clustering coefficient. The simulation looks Fourier-like analysis.

Detecting disease-related genes is crucial in disease diagnosis and drug design. The accepted view is that neighbors of a disease-causing gene in a molecular network tend to cause the same or similar diseases, and network-based methods have been recently developed to identify novel hereditary disease-genes in available biomedical networks. Despite the steady increase in the discovery of disease-associated genes, there is still a large fraction of disease genes that remains under the tip of the iceberg. In this paper we exploit the topological properties of the protein–protein interaction (PPI) network to detect disease-related genes. We compute, analyze, and compare the topological properties of disease genes with non-disease genes in PPI networks. We also design an improved random forest classifier based on these network topological features, and a cross-validation test confirms that our method performs better than previous similar studies.

To investigate the characteristics of extreme events in financial markets and the corresponding return intervals among these events, we use a Potts dynamic system to construct a random financial time series model of the attitudes of market traders. We use multiscale multifractal detrended cross-correlation analysis (MM-DCCA) and Lempel–Ziv complexity (LZC) perform numerical research of the return intervals for two significant China’s stock market indices and for the proposed model. The new MM-DCCA method is based on the Hurst surface and provides more interpretable cross-correlations of the dynamic mechanism between different return interval series. We scale the LZC method with different exponents to illustrate the complexity of return intervals in different scales. Empirical studies indicate that the proposed return intervals from the Potts system and the real stock market indices hold similar statistical properties.

Using the detrended cross-correlation analysis (DCCA) coefficient and the detrended partial cross-correlation analysis (DPCCA) coefficient, we investigate cross-correlations and net cross-correlations among five major world gold markets (London, New York, Shanghai, Tokyo, and Mumbai) at different time scales. We propose multiscale influence measures for examining the influence of individual markets on other markets and on the entire system. We find (i) that the cross-correlations, net cross-correlations, and net influences among the five gold markets vary across time scales, (ii) that the cross-market correlation between London and New York at each time scale is intense and inherent, meaning that the influence of other gold markets on the London–New York market is negligible, (iii) that the remaining cross-market correlations (i.e., those other than London–New York) are greatly affected by other gold markets, and (iv) that the London gold market significantly affects the other four gold markets and dominates the world-wide gold market. Our multiscale findings give market participants and market regulators new information on cross-market linkages in the world-wide gold market.

We examine how real-world individuals and companies can either reach an agreement or fail to reach an agreement after several stages of negotiation. We use a modified prisoner’s dilemma game with two-sided players who can either cooperate or not cooperate with their neighbors. We find that the presence of even a small number of these two-sided players substantially promotes the cooperation because, unlike the rock–paper–scissors scenario, when the cooperators change to the non-cooperators to gain a payoff, they can turn to the two-sided players and continue negotiating. We find that the network structure influences the spread of strategies. Lattice and regular-random (RR) networks benefit the spread of both non-cooperation and two-sided strategies, but scale-free (SF) networks stop both strategies. We also find that the Erdos–R e nyi (ER) network promotes the two-sided strategy and blocks the spread of non-cooperation. As the ER network density decreases, and the network degree is lowered the lifetime of non-cooperators increases. Our results expand our understanding of the role played by the two-sided strategy in the growth of the cooperative behavior in networks.

In human resource management, employee performance is strongly affected by both formal and informal employee networks. Most previous research on employee performance has focused on monolayer networks that can represent only single categories of employee social relationships. We study employee performance by taking into account the entire multiplex structure of underlying employee social networks. We collect three datasets consisting of five different employee relationship categories in three firms, and predict employee performance using degree centrality and eigenvector centrality in a superimposed multiplex network (SMN) and an unfolded multiplex network (UMN). We use a quadratic assignment procedure (QAP) analysis and a regression analysis to demonstrate that the different categories of relationship are mutually embedded and that the strength of their impact on employee performance differs. We also use weighted/unweighted SMN/UMN to measure the predictive accuracy of this approach and find that employees with high centrality in a weighted UMN are more likely to perform well. Our results shed new light on how social structures affect employee performance.

A publication that reports a breakthrough discovery in a particular scientific field is referred to as a “black swan”, and the most highly-cited papers previously published in the same field “white swans”. Important scientific progress occurs when “white swans” meet a “black swan”, and the citation patterns of the “white swans” change. This metaphor combines scientific discoveries and scientometric data and suggests that breakthrough scientific discoveries are either “black swans” or “grey-black swans”.

Although the tools of complexity research have been applied to the phenomenon of labor division, its underlying mechanisms are still unclear. Researchers have used evolutionary models to study labor division in terms of global optimization, but focusing on individual optimization is a more realistic, real-world approach. We do this by first developing a multi-agent model that takes into account information-sharing and learning-by-doing and by using simulations to demonstrate the emergence of labor division. We then use a master equation method and find that the computational results are consistent with the results of the simulation. Finally we find that the core underlying mechanisms that cause labor division are learning-by-doing, information cost, and random fluctuation.

We study stochastic resonance (SR) phenomenon in the fractional oscillator with time delay and damping fluctuation, analyze the impact of time delay and fractional damping as two memory ingredients on SR, and put forward firstly the concept of the robustness of GSR. By moment method, we obtain the analytical expression of the output amplitude gain and find that fluctuations in the output amplitude gain are non-monotonic. Using numerical simulations we verify the accuracy of the analytical results. We find (i) that the length of time delay and system order are parameters related to memory; (ii) that the output amplitude gain could attain a maximum by increasing driving frequency close to system frequency, and small time delay and system order contribute to enhance the resonance intensity; (iii) that the evolution of the output amplitude gain versus the noise intensity exhibits one-peak resonance, and small time delay can enhance the resonance intensity and the robustness of SR regarding to driving frequency and system frequency; (iv) that the evolution of the output amplitude gain versus the noise correlation rate presents one-peak resonance in the presence of small time delay, critical time delay is bigger with the increasing system order when noise intensity is fixed and critical time delay is smaller with the increasing noise intensity when system order is fixed.

A supply chain network is a typical interdependent network composed of an undirected cyber-layer network and a directed physical-layer network. To analyze the robustness of this complex interdependent supply chain network when it suffers from disruption events that can cause nodes to fail, we use a cascading failure process that focuses on load propagation. We consider load propagation via connectivity links as node failure spreads through one layer of an interdependent network, and we develop a priority redistribution strategy for failed loads subject to flow constraint. Using a giant component function and a one-to-one directed interdependence relation between nodes in a cyber-layer network and physical-layer network, we construct time-varied functional equations to quantify the dynamic process of failed loads propagation in an interdependent network. Finally, we conduct a numerical simulation for two cases, i.e., single node removal and multiple node removal at the initial disruption. The simulation results show that when we increase the number of removed nodes in an interdependent supply chain network its robustness undergoes a first-order discontinuous phase transition, and that even removing a small number of nodes will cause it to crash.

Made available in DSpace on 2020-12-12T01:16:29Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-09-15 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) This paper studies the effects of overconfidence and loss aversion in an artificial stock exchange. When we model only fundamentalists we find results that are consistent with homogeneous agent models. Adding 5% of chartists increases the stock return rate but also increases other variables, including volatility and kurtosis. We find that the inclusion of confidence in 5% of chartists raises the trading volume as empirical evidences corroborate and price volatility increases considerably. On the other hand, loss aversion in 5% of chartists substantially decreases the trading volume, although chartist traders now have a higher percentage of stocks in their portfolios, and a buy and hold strategy is adopted to mitigate losses. Department of Economics Sao Paulo State University (UNESP) Swiss Federal Institute of Technology in Zurich (ETH Zurich) Center for Polymer Studies Boston University Department of Economics Sao Paulo State University (UNESP) FAPESP: 2018/22562-4 CNPq: 303986/2017-4 CNPq: 428433/2018-9

Flight delays have negative impacts on passengers, carriers, and airports. To reduce these unpopular influence, we need to find the statistical law of the collective behavior of passenger flights. We use a mean-field approach to analyze big data listing the departure and arrival records of all American domestic passenger flights in 20 years. We treat passenger flights as particle gases and define their dimensionless velocity, quasi-thermodynamic quantities — pressure, volume, temperature, and mole number, respectively. By introducing phenomenological parameters a and b to set up van der Waals-like state equations, we erect a universal gaseous constant R for actually operated passenger flights, their counterparts on schedule, and ”delayor gases” defined as the difference between them. We find that the attractive coefficient of ”delayor gases” positively correlates with the average delay per flight on airports. Rescaling state equations for passenger flights across all 20 years, we find a universal function. This is a significant step toward understanding flight delays and dealing with temporal big data with the tools of statistical physics.

We use the recently-proposed \emph{compressible cell} Ising-like model [Phys. Rev. Lett. \textbf{120}, 120603 (2018)] to estimate the ratio between thermal expansivity and specific heat (the Gr\"uneisen parameter $\Gamma$) in supercooled water. Near the critical pressure and temperature, $\Gamma$ increases. The $\Gamma$ value diverges near the pressure-induced finite-$T$ critical end-point [Phys. Rev. Lett. \textbf{104}, 245701 (2010)] and quantum critical points [Phys. Rev. Lett. \textbf{91}, 066404 (2003)], which indicates that two energy scales are governing the system. This enhanced behavior of $\Gamma$ is caused by the coexistence of high- and low-density liquids [Science \textbf{358}, 1543 (2017)]. Our findings support the proposed liquid-liquid critical point in supercooled water in the No-Man's Land regime, and indicates possible applications of this model to other systems., Comment: 5 pages, 4 figures

Interdependencies are ubiquitous throughout the world. Every real-world system interacts with and is dependent on other systems, and this interdependency affects their performance. In particular, interdependencies among networks make them vulnerable to failure cascades, the effects of which are often catastrophic. Failure propagation fragments network components, disconnects them, and may cause complete systemic failure. We propose a strategy of avoiding or at least mitigating the complete destruction of a system of interdependent networks experiencing a failure cascade. Starting with a fraction $1-p$ of failing nodes in one network, we reconnect with a probability $\gamma$ every isolated component to a functional giant component (GC), the largest connected cluster. We find that as $\gamma$ increases the resilience of the system to cascading failure also increases. We also find that our strategy is more effective when it is applied in a network of low average degree. We solve the problem theoretically using percolation theory, and we find that the solution agrees with simulation results., Comment: 13 pages, 4 figures

We consider a regular assembly of singly occupied cells with two accessible volumes. Coupled to cell volumes are interaction energies between nearest neighbors that lead to a phase transition with a critical point. We find that these compressible cell models can serve as Ising-like prototypes of the one-component liquid-liquid and isostructural solid-solid phase transitions that originate in the short-range features of the intermolecular potential. The mean-field solutions provide hints concerning the analytical form of the equation of state of liquid water.

Margin trading in which investors purchase shares with money borrowed from brokers is blamed to be a major cause of the 2015 Chinese stock market crash. We propose a cascading failure model and examine how an increase in margin trading increases share price vulnerability. The model is based on a bipartite graph of investors and shares that includes four margin trading factors, (i) initial margin $k$, (ii) minimum maintenance $r$, (iii) volatility $v$, and (iv) diversity $s$. We use our model to simulate margin trading and observe how the share prices are affected by these four factors. The experimental results indicate that a stock market can be either vulnerable or stable. A stock market is vulnerable when an external shock can cause a cascading failure of its share prices. It is stable when its share prices are resilient to external shocks. Furthermore, we investigate how the cascading failure of share price is affected by these four factors, and find that by increasing $v$ and $r$ or decreasing $k$ we increase the probability that the stock market will experience a phase transition from stable to vulnerable. It is also found that increasing $s$ decreases resilience and increases systematic risk. These findings could be useful to regulators supervising margin trading activities., Comment: 14 pages, 9 figures

Detection of power-law behavior and studies of scaling exponents uncover the characteristics of complexity in many real world phenomena. The complexity of financial markets has always presented challenging issues and provided interesting findings, such as the inverse cubic law in the tails of stock price fluctuation distributions. Motivated by the rise of novel digital assets based on blockchain technology, we study the distributions of cryptocurrency price fluctuations. We consider Bitcoin returns over various time intervals and from multiple digital exchanges, in order to investigate the existence of universal scaling behavior in the tails, and ascertain whether the scaling exponent supports the presence of a finite second moment. We provide empirical evidence on slowly decaying tails in the distributions of returns over multiple time intervals and different exchanges, corresponding to a power-law. We estimate the scaling exponent and find an asymptotic power-law behavior with 2 α 2 . 5 suggesting that Bitcoin returns, in addition to being more volatile, also exhibit heavier tails than stocks, which are known to be around 3. Our results also imply the existence of a finite second moment, thus providing a fundamental basis for the usage of standard financial theories and covariance-based techniques in risk management and portfolio optimization scenarios.

We study auto-correlations and cross-correlations of daily price changes and daily volume changes of thirteen global stock market indices, using multifractal detrended fluctuation analysis (MF-DFA) and multifractal detrended cross-correlation analysis (MF-DXA). We find rather distinct multifractal behavior of price and volume changes. Our results indicate that the time series of price changes are more complex than those of volume changes, and that large fluctuations dominate multifractal behavior of price changes, while small fluctuations dominate multifractal behavior of volume changes. We also find that there is an absence of correlations in price changes, there are anti-persistent long-term correlations in volume changes, and there are anti-persistent long-term cross-correlations between price and volume changes. Shuffling the series reveals that multifractality of both price changes and volume changes arises from a broad probability density function.

We investigate multifractal properties of daily price changes in currency rates using the multifractal detrended fluctuation analysis (MF-DFA). We analyze managed and independent floating currency rates in eight countries, and determine the changes in multifractal spectrum when transitioning between the two regimes. We find that after the transition from managed to independent float regime the changes in multifractal spectrum (position of maximum and width) indicate an increase in market efficiency. The observed changes are more pronounced for developed countries that have a well established trading market. After shuffling the series, we find that the multifractality is due to both probability density function and long term correlations for managed float regime, while for independent float regime multifractality is in most cases caused by broad probability density function.

The cascading failure can bring a huge loss for most real-world networks; but, we cannot uncover fully the mechanism and law of the cascading events occurrence. Most networks in which the cascading failure occurred are based on the various ‘flows’, such as power, oils, and information; moreover, the same link degree of the different nodes likely contain the different meanings, where some are large pivotal nodes and some are mini switching centers. Thus, these networks must be described by the directed and weighted network model. Besides, the ‘over-loading’ cascading failures were more analyzed and studied; but the cascading failures caused by ‘short-loading’ were less studied relatively. However, for some directed networks, such as power grids, oil pipe nets, gas pipe nets and information networks, the large-scale failures of network nodes substantially could be induced by ‘short-loading’ in a such similar way as ‘over-loading’. Based on the above reasons, in this paper, we first built the ‘load-capacity’ model of the directed and weighted network. Afterwards the ‘over-loading’ cascading failure model and the ‘short-loading’ cascading failure model based on the directed and weighted network were built. Meanwhile, applying the models to two typical real networks–Poisson distribution network and power law distribution network–intensive study and numerical analysis were carried out. Lastly, two classical networks simulation experiment results are provided. After the numerical and simulation analyses, we gained the following conclusions. For the power law network, the power exponent β of ‘load-capacity’ function should be taken value (0, 1) for a good robustness, and the minimum in-degree and out-degree should be increased respectively, meanwhile, the weight and the scaling exponents of the in-degree and the out-degree distributions should be increased synchronously in the interval (2, 3) for enhancing the resistibility of ‘over-loading’ and ‘short-loading’ failures. For the Poisson network, the power exponent β of loading function should be taken value (0, 3) for a good robustness, and the average weight and the average in-degree should be increased respectively restricting 2 β 3 for enhancing the resistibility of ‘over-loading’ and ‘short-loading’ failures.

In this paper we present a new measure to investigate the functional structure of financial markets, the Sector Dominance Ratio (SDR). We study the information embedded in raw and partial correlations using random matrix theory (RMT) and examine the evolution of economic sectoral makeup on a yearly and monthly basis for four stock markets, those of the US, UK, Germany and Japan, during the period from January 2000 to December 2010. We investigate the information contained in raw and partial correlations using the sector dominance ratio and its variation over time. The evolution of economic sectoral activities can be discerned through the largest eigenvectors of both raw correlation and partial correlation matrices. We find a characteristic change of the largest eigenvalue from raw and partial correlations and the SDR that coincides with sharp breaks in asset valuations. Finally, we propose the SDR as an indicator for changes in VIX indexes.

Financial networks have become extremely useful in characterizing the structures of complex financial systems. Meanwhile, the time evolution property of the stock markets can be described by temporal networks. We utilize the temporal network framework to characterize the time-evolving correlation-based networks of stock markets. The market instability can be detected by the evolution of the topology structure of the financial networks. We then employ the temporal centrality as a portfolio selection tool. Those portfolios, which are composed of peripheral stocks with low temporal centrality scores, have consistently better performance under different portfolio optimization frameworks, suggesting that the temporal centrality measure can be used as new portfolio optimization and risk management tool. Our results reveal the importance of the temporal attributes of the stock markets, which should be taken serious consideration in real life applications.

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.

Using the empirical data from the Norwegian Tax Administration, we analyze the wealth and income of the richest individuals in Norway during the period 2010–2013. We find that both annual income and wealth level of the richest individuals are describable using the Pareto law. We find that the robust mean Pareto exponent over the four-year period to be ≈2.3 for income and ≈1.5 for wealth.

There is a long standing debate over how to objectively compare the career achievements of professional athletes from different historical eras. Developing an objective approach will be of particular importance over the next decade as Major League Baseball (MLB) players from the "steroids era" become eligible for Hall of Fame induction. Here we address this issue, as well as the general problem of comparing statistics from distinct eras, by detrending the seasonal statistics of professional baseball players. We detrend player statistics by normalizing achievements to seasonal averages, which accounts for changes in relative player ability resulting from both exogenous and endogenous factors, such as talent dilution from expansion, equipment and training improvements, as well as performance enhancing drugs (PED). In this paper we compare the probability density function (pdf) of detrended career statistics to the pdf of raw career statistics for five statistical categories -- hits (H), home runs (HR), runs batted in (RBI), wins (W) and strikeouts (K) -- over the 90-year period 1920-2009. We find that the functional form of these pdfs are stationary under detrending. This stationarity implies that the statistical regularity observed in the right-skewed distributions for longevity and success in professional baseball arises from both the wide range of intrinsic talent among athletes and the underlying nature of competition. Using this simple detrending technique, we examine the top 50 all-time careers for H, HR, RBI, W and K. We fit the pdfs for career success by the Gamma distribution in order to calculate objective benchmarks based on extreme statistics which can be used for the identification of extraordinary careers.

Phase transitions in water are normally classified as first or second order. But in confined quasi-one-dimensional films of water, simulations show that the solid–liquid transition can take place by means of a first-order transition or a continuous one without a distinction between solid and liquid.

We consider a “generalized Heisenberg model”, with a Hamiltonian given by ℋ(D) = –JΣijS(D)j, where the quantities Si are isotropically-interacting D-dimensional classical spins and the summation is restricted to nearest-neighbor pairs of sites . Thus when D = 1, 2, and 3, this model reduces to the S = 1/2 Ising, plane rotator, and classical Heisenberg models which are useful for describing phase transitions in, respectively, liquid-gas systems, Bose fluids, and magnetic materials. Unfortunately this Hamiltonian is exactly soluble only in the limit D ∞, in which case the solution corresponds to the spherical model of Berlin and Kac. For finite D, we must consider approximation procedures in order to obtain any useful information. In this paper we develop a diagrammatic representation, valid for arbitrary D, for successive terms in the high-temperature expansions of the free energy and the spin-spin correlation function—from which we can obtain series expansions for all the thermodynamic properties of the system.

The static scaling law hypothesis for thermodynamic functions is formulated by the statement that the singular part of any thermodynamic potential is a generalized homogeneous function (GHF), where by definition a function f(x, y) is a GHF if there exist two numbers a, b such that for all positive values of λ, f (λax, λby) = λ f(x, y). We show that all Legendre transforms and all partial derivatives of a GHF are also GHF'S. Since every thermodynamic function is related to a given thermodynamic potential by some combination of Legendre transforms and partial derivatives, it follows that all thermodynamic functions are GHF'S. We then observe that every such function has a simple power law singularity at the critical point, and that the value of every critical-point exponent can be written down by inspection in terms of the two initial numbers a, b. Consideration of different paths of approach to the critical point lead to an important class of direct tests of the scaling hypothesis; among these are the familiar relations among certain combinations of critical-point exponents. Finally, the problems of extension to functions of more than two variables are discussed.

The unique scaling behavior of financial time series have attracted the research interest of physicists. Variables such as stock returns, share volume, and number of trades have been found to display distributions that are consistent with a power-law tail. We present an overview of recent research joining practitioners of economic theory and statistical physics to try to understand better some puzzles regarding economic fluctuations. One of these puzzles is how to describe outliers, i.e. phenomena that lie outside of patterns of statistical regularity. We review recent research, which suggests that such outliers may not in fact exist and that the same laws seem to govern outliers as well as day-to-day fluctuations.

We calculate the Shannon entropy of a time series by using the probability density functions of the characteristic sizes of the long-range correlated clusters introduced in [A. Carbone, G. Castelli, H.E. Stanley, Phys. Rev. E 69 (2004) 026105]. We define three different measures of the entropy related, respectively, to the length, the duration and the area of the clusters. For all the three cases, the entropy increases as the logarithm of a power of the size with exponents equal to the fractal dimension of the cluster length, duration and area, respectively. r 2007 Elsevier B.V. All rights reserved.

We study the transport properties of model networks such as scale-free and Erdýos- Renyi networks as well as a real network. We consider the conductance G between two arbitrarily chosen nodes where each link has the same unit resistance. Our theoretical analysis for scale-free networks predicts a broad range of values of G, with a power-law tail distributionSF(G) ∼ G −gG, where gG = 2λ − 1, and λ is the decay exponent for the scale-free network degree distribution. We confirm our predictions by large scale simulations. The power-law tail inSF(G) leads to large values of G, thereby significantly improving the transport in scale-free networks, compared to Erdýos-Renyi networks where the tail of the conductivity distribution decays exponentially. We develop a simple physical picture of the transport to ac- count for the results. We study another model for transport, the max-flow model, where conductance is defined as the number of link-independent paths between the