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Due to globally uneven distribution of forest resources and timely difference of supply and demand, forest products are difficult to achieve market equilibrium among neighboring countries. The global forest product trade not only alleviates the contradiction of supply and demand, but also ensures the sustainable development of the economic construction for each country. Previous literature analyzed the structure and evolution mechanism of forest product trade from different perspectives, but ignored the competitive relationships among forest product importers. Based on complex network theory, this paper selected global wood forest product trade data from 2004 to 2016. Then we constructed global unweighted and weighted wood-forest-product competitive network from import perspective, and three indicators (density, average path length, and clustering coefficient), core–periphery model and two indicators of competitive intensity were introduced to study the spatial competitive evolution of global wood forest product trade among countries. The results showed that there was wide competition in global wood forest product trade among continents, and the tightness of competitive relationships gradually decreased with time. Secondly, core countries and periphery countries were clearly identified in the competitive network, and China, Japan, the U.S., were the main core countries influencing the evolution of core–periphery structure. Finally, the U.S. played an important role in the global competition system. But with the increment of demand for Asian market, China and other Asian countries gradually became the center of wood-forest-product competition. The research tried to provide some references for importers to follow the laws of global wood forest product trade, and formulate the corresponding policy under constructing sustainable society and facing climate change. • The paper considered the competitive relationship of global wood forest product trade. • TThe paper constructed global unweighted and weighted wood-forest-product competitive network, and studied spatial competitive evolution of global wood forest product trade. • The U.S. played an important role in the global competition system, but China and other Asian countries gradually became the center of global wood-forest-product competition. [ABSTRACT FROM AUTHOR]

This paper examines the dynamical impact of market wide liquidity on the volatility by using the Markov switching-regime approach on the Heterogeneous Autoregressive model of the realized range-based volatility (MS-HAR-RRV-LIQ). First, based on the simple partition of the liquidity measures by their historical means, we find the impact of the market liquidity on the realized range-based volatility (RRV) is non-linear with significantly stronger when the market is fluctuating. Second, by utilizing the method of Markov regimes switching, we find that in sample results, the impact of market liquidity on RRV is dynamics with much stronger when the market is fluctuating. In addition, the impact of liquidity on RRV are persistent for both the fluctuating state and the stable state, which indicate that the impact of liquidity on volatility would remain in its original state unless there are major economic events. Third, in order to eliminate the hidden information of volatility in liquidity measures, we further decompose the liquidity measures to obtain the volatility-adjusted liquidity, and find that the impact of liquidity driven by the component of liquidity uncorrelated with volatility on market volatility is also dynamics with much stronger when the market is fluctuating. Finally, out-of-sample results suggest that the models of HAR-RRV-LIQ and their corresponding volatility-adjusted liquidity models with regimes switching can increase the forecasting ability significantly than those without regime switching. • This paper examines the dynamical impact of market wide liquidity on volatility. • The impact of the market wide liquidity on RRV is non-linear. • The impact of market wide liquidity on RRV is dynamics. • Our proposed models can increase the forecasting ability significantly. [ABSTRACT FROM AUTHOR]

Abstract Due to the short mechanism of the commodity futures market, a dynamic weighted money flow model is proposed in this paper. The model proposed herein is based on the original money flow model but considers the impact of changes in both open interest and price on money flow. The proposed model aptly depicts the overall law of money flow in the Chinese commodity futures market. The results regarding correlation between current money flows and future futures prices show that there are 17 futures contracts with strong negative correlations and three futures contracts with strong positive correlations between 2011 and 2013. Then, the logistic regression, Bayesian discriminant, decision tree, random forest and support vector machine models are applied to validate the forecasting ability of the proposed money flow model with respect to price fluctuations. The average prediction accuracies of the above models exceed 55%, indicating that the money flow model proposed in this paper has a strong forecasting ability. Finally, a binary classification logistic regression strategy based on the dynamic weighted money flow model is established for back-testing and is compared to the double-moving average strategy and the buy-hold strategy. The back-testing results show that the cumulative annualized yield of the portfolio that uses the strategy proposed in this paper is 281.95%. Therefore, the proposed strategy is far superior to other strategies and exhibits better profitability. Highlights • A new dynamic weighted money flow model considering open interest is proposed. • Analysis of money flow between sectors and correlation with future commodity futures prices. • Evaluating the predictive ability of the money flow index by logistic model. • The back-testing results exhibit very robust performance and excellent risk control ability. [ABSTRACT FROM AUTHOR]

Abstract A new high-order statistical moment based on multi-scale (MSHOM) is proposed for researching traditional statistics in this paper. In addition, the indispensable theoretical basis and derivation are illustrated in detail. With the help of three simulated time series, two kinds of situations of MSHOM analysis are mainly discussed in this work. One is accomplished by Gaussian white noise (GWN) and the other is fulfilled with Logistic map and autoregressive fractionally integrated moving-average (ARFIMA). Due to the insufficient performance of MSHOM, we propose an improved MSHOM, which is called MSHOM with control (C-MSHOM). Meanwhile, its performance is tested by the data of US and Chinese stock markets. However, C-MSHOM also brings an extra preprocess stage of data and the uncertainty of selection. To solve these problems, a more generic method, that is, generalized multi-scale high-order moments (G-MSHOM) is given in this paper. Highlights • A new high-order statistical moment is proposed based on multi-scale (MSHOM). • Two improved methods are proposed to widen extent of applications. • The analytic properties of the MSHOM method are presented in detail. • Simulation results show the stability and precision of the MSHOM method. • Benefits of MSHOM and G-MSHOM are shown using US and Chinese stock markets data. [ABSTRACT FROM AUTHOR]

Abstract This paper offers a new approach to calibrating parameters of an agent-based model proposed by Ormerod (2002) and conducts a comparative analysis of the US and Chinese business cycles for the period 1993–2014 using annual percentage changes in real gross domestic product. Depending on the underlying conditions of the system, the agent-based model is transformed to a damped pendulum, or a forced damped pendulum, through which we get the analytical solutions. These analytical solutions stipulate the speed of recovery from external shocks and the length of a business cycle. After pinning down two parameters in the analytical solutions, we calibrate the model using the Bayesian estimation technique. Our calibration approach captures the fundamental features of the economic fluctuations reasonably well. In particular, we find that the Chinese economy is more volatile and less persistent than the US economy: China has a shorter average duration of business cycles and recovers faster from external shocks; and the two countries move synchronously. Highlights • Modeling GDP fluctuations with agent-based model is proposed. • Obtain the analytical solutions for the model using a (forced) damped pendulum. • The solutions stipulate the recovery speed from external shocks and business cycle duration. • Estimation technique employed in the paper is shown to be more efficient. • Key empirical features of the GDP growth in the US and China are well captured. [ABSTRACT FROM AUTHOR]

Complex network theory is a widely used tool in the empirical research of financial markets. Two-mode and multi-mode networks are new trends and represent new directions in that they can more accurately simulate relationships between entities. In this paper, we use data for Chinese listed companies holding non-listed financial companies over a ten-year period to construct two networks: a two-mode primitive network in which listed companies and non-listed financial companies are considered actors and events, respectively, and a one-mode network that is constructed based on the decreasing-mode method in which listed companies are considered nodes. We analyze the evolution of the listed company co-holding network from several perspectives, including that of the whole network, of information control ability, of implicit relationships, of community division and of small-world characteristics. The results of the analysis indicate that (1) China’s developing stock market affects the share-holding condition of listed companies holding non-listed financial companies; (2) the information control ability of co-holding networks is focused on a few listed companies and the implicit relationship of investment preference between listed companies is determined by the co-holding behavior; (3) the community division of the co-holding network is increasingly obvious, as determined by the investment preferences among listed companies; and (4) the small-world characteristics of the co-holding network are increasingly obvious, resulting in reduced communication costs. In this paper, we conduct an evolution analysis and develop an understanding of the factors that influence the listed companies co-holding network. This study will help illuminate research on evolution analysis. [ABSTRACT FROM AUTHOR]

The paper measures monthly short-term international capital flows during Jan. 2000 to Jul. 2015 in China. It is shown that, since mid-2014 the amount of short-term international capital outflows in China has increased rapidly, reaching the historical peak at 65.8 billion dollars per month in July, 2015. By conducting multifractal analysis with MF-DFA method, it is proved that China’s short-term international capital flows exhibit significant non-linear multifractal properties. Further study indicates that the long-range correlations and fat-tailed distribution both contribute to the multifractal properties, while the former is the main reason. The non-linearity of capital flows calls for non-linear tools instead of conventional linear tools to further study features of China’s short-term international capital flows or to build early warning system of international capital flow risks. The paper measures monthly short-term international capital flows during Jan. 2000 to Jul. 2015 in China. It is shown that, since mid-2014 the amount of short-term international capital outflows in China has increased rapidly, reaching the historical peak at 65.8 billion dollars per month in July, 2015. By conducting multifractal analysis with MF-DFA method, it is proved that China’s short-term international capital flows exhibit significant non-linear multifractal properties. Further study indicates that the long-range correlations and fat-tailed distribution both contribute to the multifractal properties, while the former is the main reason. The non-linearity of capital flows calls for non-linear tools instead of conventional linear tools to further study features of China’s short-term international capital flows or to build early warning system of international capital flow risks. [ABSTRACT FROM AUTHOR]

In recent years, China’s stock market has experienced dramatic fluctuations; in particular, in the second half of 2014 and 2015, the market rose sharply and fell quickly. Many classical financial phenomena, such as stock plate linkage, appeared repeatedly during this period. In general, these phenomena have usually been studied using daily-level data or minute-level data. Our paper focuses on the linkage phenomenon in Chinese stock 5-second-level data during this extremely volatile period. The method used to select the linkage points and the arbitrage strategy are both based on multi-dimensional stationary processes. A new program method for testing the multi-dimensional stationary process is proposed in our paper, and the detailed program is presented in the paper’s appendix. Because of the existence of the stationary process, the strategy’s logarithmic cumulative average return will converge under the condition of the strong ergodic theorem, and this ensures the effectiveness of the stocks’ linkage points and the more stable statistical arbitrage strategy. [ABSTRACT FROM AUTHOR]

Considering the complexity of the urban rail transit system (URTS), it is necessary to systematically identify the critical factors and their relationships to avoid or prevent urban rail transit accidents. In response to the problems few previous studies have modeled networks for the whole system components of URTS and have focused too much on the identification of specific component states or behaviors without quantitative analysis of inter-component relationships. This paper proposed a risk network construction and analysis method based on complex network theory to identify the critical accident causative factors and their relationships of the URTS system. Firstly, the accident is defined as the combination of a sequence of risk incidents occurring in a certain order and their resulting consequences, and the definitions of risk point and risk incident were given. Secondly, a risk network construction and analysis method based on the combination of accident reports and complex network theory was proposed to obtain the critical factors and their relationships in the process of the accident. At last, this paper constructed the urban rail transit risk point set containing 39 risk points, built the risk network based on 201 accident reports of URTS in China and analyzed the critical risk points and risk relationships. The results show that this study can provide a new perspective for identifying the critical causative factors of URTS accidents and their relationships for practical application in risk analysis and accident prevention. • The new definitions of risk points and incidents are put forward. • A critical risk analysis framework are proposed by complex network theory. • Critical risk points and relationships are identified in the urban rail transit system by the risk network. [ABSTRACT FROM AUTHOR]

Accurate and robust fine-grained expressway traffic volume simulation is a critical issue in intelligent transportation systems and real-time traffic-related applications. However, the fine-grained expressway traffic volumes aggregated from vehicle trajectories heavily rely on individual heterogeneity, making it challenging for modeling and accurate simulation. In order to eliminate the influence of individual heterogeneity on the modeling and simulation of the fine-grained expressway traffic volume, this paper proposes a novel method named Quantum Harmonic Oscillator Model for Fine-grained Expressway Traffic Volume Simulation (FGTVS-QHO). FGTVS-QHO adopts the wave function of the quantum harmonic oscillator model to describe the irregular evolution of the location of the vehicle. Then several optimization strategies are applied to the numerical solution of the wave function. FGTVS-QHO is validated with the expressway traffic volumes of six exits along the Nanjing-Shanghai Expressway in China. The simulation performance of FGTVS-QHO is compared with the methods of Long and Short-Term Memory (LSTM) networks and Autoregressive Integrated Moving Average (ARIMA). FGTVS-QHO shows higher simulation accuracy, less time cost, and lower parameter complexity. Especially, the average simulation accuracy of FGTVS-QHO is improved by 24.80% and 58.05% compared with the above two methods, respectively. The scale effects of FGTVS-QHO also indicate that it is adaptive to simulate the expressway traffic volume at fine time granularity. This paper provides a new potential method for fine-grained expressway traffic volume simulation with strong individual heterogeneity. • A traffic volume simulation model considering individual heterogeneity is proposed. • The irregular evolution of the location of vehicle is expressed by a wave function. • Our model can well simulate the complex fluctuation of fine-grained traffic volume. • Our model has the potential to model traffic with unobservable driving behavior. [ABSTRACT FROM AUTHOR]

The transmission of the liquidity crisis across interbank lending markets has emerged as a primary concern for regulatory agencies. In this paper, we consider the nonlinear dynamic contagion and the regulatory scheme of liquidity crisis in the interbank lending network. We first propose a Susceptible–Infected–Susceptible epidemic network model to describe the dynamics of crisis contagion between banks. We find that complex interbank linkages and the heterogenous contagiousness and resilience of banks determine the equilibrium state of the system and the crisis outbreak trajectory of each group. We prove the positive effect of improving bank liquidity on mitigating the crisis outbreak. Next, we propose concrete intervention policies based on two widely used liquidity regulation approaches — cutting reserve requirements and repo operations. With the financial data of China's banks from 2007 to 2021, we find that the liquidity crisis can be eliminated without intervention in 2021 in China due to nearly a decade of continued decrease in the reserve requirement ratios. We finally show that an unhealthy structure of interbank network can lead to a system where infection outpaces recovery. In this case, the effect of liquidity regulation is weak. The empirical results find that liquidity regulation is generally effective from 2013 to 2017, but weak from 2007 to 2010. • We propose a SIS model to study the interbank liquidity crisis contagion. • We derive heterogeneous parameters of each node based on banks' financial statements. • We implement the model on the Chinese interbank network research. • We design two macroprudential instruments for liquidity regulation — RRR cuts and repos. • We find that liquidity regulation is effective from 2013–2017, but weak from 2007–2010. [ABSTRACT FROM AUTHOR]

With the gradual expansion of the university scale, it is extremely inconvenient for students to travel to and from campus and classrooms. Bikeshare and shared electric bike (e-bikeshare) are currently promising solutions for campus travel. Most literature has recently focused on bikeshare and e-bikeshare studies in the urban environment, with little attention on campus. However, campuses are significantly different from urban environments, which hinder the active promotion of campus bikeshare and e-bikeshare use. The subject of this paper is Henan Polytechnic University, which is one of the largest bikeshare and e-bikeshare universities in China. Based on the analysis and summary of travel characteristics of Chinese college travelers, this paper uses RP and SP survey data to construct a Nested Logit model to explore the potential factors influencing the use behavior of bikeshare and e-bikeshare in Chinese college environments. The results show that bikeshare and e-bikeshare are the most sensitive to trip time. The influence of trip purpose and weather conditions on the selection of bikeshare and e-bikeshare is smaller than that of time urgency. According to the estimated trip sharing rate of the model, we find that bikeshare and e-bikeshare are attractive. • Transportation characteristics of campuses environment differs from social environments greatly. • Unique choice influencing factor of trip tool selection in campus environment—time urgency. • The probability of travel mode choice for campus travelers is strongly influenced by trip time. • Improving shared travel pricing models, developing reasonable pricing models based on travel time, creating shared travel cards, etc. could help policy development. [ABSTRACT FROM AUTHOR]

This paper proposes an approach to extract the hub networks from urban rail transit networks, and analyzes the characteristics of the hub networks. Minsk metro and Shanghai metro networks are given to illustrate the feasibility and effectiveness of the presented method in this paper. By simulations, we discover that the hub networks of urban rail transit networks possess small-world property and scale-free property. Meanwhile, this paper shows that the hub networks are completely different from the corresponding metro networks. Moreover, we find that the hub network is a hierarchical network, and the root of hub network corresponds to the transfer station of metro network which is passed by the most lines in metro network, and the root controls the main characteristics of hub network. In other words, the transfer station corresponding to this root plays the most important role in the urban rail transit networks. [ABSTRACT FROM AUTHOR]

Accurate estimation of ride-hailing travel mobility is significant for demand management, and transportation planning. Although existing intervening opportunities models based on individual destination selection behavior can estimate travel mobility patterns (e.g., commuter flow, and migration flow), they usually ignore the substitutability of candidate destinations. In the context of ride-hailing travel, people tend to have strong destination preferences, and candidate destinations should be related to individual travel needs. Meanwhile, artificial intelligence offers powerful tools to extract complex nonlinear dependencies of mobility data, which are difficult to capture by traditional intervention opportunities models. This paper proposes a novel ensemble model with conditional intervening opportunities to improve the accuracy of ride-hailing travel mobility estimation by considering the substitutability of candidate destinations, that is, only the location related to people's trip purpose will likely affect people's travel behavior. The proposed ensemble model employs a stacking strategy to integrate six advanced machine learning and deep learning algorithms to extract complex nonlinear dependencies from ride-hailing travel mobility data, and achieve accurate mobility estimation. Furthermore, datasets from two major cities in China with more than 25 million ride-hailing trips are used for model training and experimental comparison. The results indicate that the proposed model outperforms other baseline models in ride-hailing travel mobility estimation tasks. It accurately predicts trip flows and the trip distance distribution, and can capture mobility patterns with strong interpretability. The proposed model can be applied to analyze the travel behavior of ride-hailing passengers, as well as the mobility patterns between different regions. • Propose a novel ensemble model for ride-hailing travel mobility estimation. • Develop conditional intervening opportunities model with substitutability of candidate locations. • Extract nonlinear dependencies from ride-hailing data using artificial intelligence algorithm. • Verify the proposed prediction model using over 25-million ride-hailing trips. • Interpret ride-hailing travel mobility patterns using Shapley additive explanations method. [ABSTRACT FROM AUTHOR]

This paper proposes a multilayer bank network structure based on three channels: interbank lending, cross-shareholding and holding common assets. By investigating the dynamics of systemic risk contagion across networks, we extend the DebtRank model to a three-layer network. The empirical results suggest that the multilayer structure of the banking system has a non-linear superimposed effect on systemic losses, with interbank lending and holdings common asset being the main sources of risk contagion. We also simulate systemic shocks such as individual bank shocks, industry shocks, and macroeconomic shocks with the aim of identifying systemically important banks and industries. The results found that China's four largest state-owned commercial banks are the systemically important banks, and the banks' loan balances in manufacturing, transportation and other industries and personal credit should be the focus of attention. This study can feedback more correlations in the banking system, emphasizing the importance of research on multilayer network contagion mechanisms, and the content and findings of the study can provide empirical corroboration for systemic risk regulation. • Multilayer networks: interbank lending, cross-shareholding, holding common assets. • The superimposed effect of the three-layer network amplifies the systemic risk. • The four state-owned commercial banks in China are systemically important banks. • Personal lending and manufacturing loans play an important risk transmission role. • Applying to Chinese finance, the three-layer network showcases its strengths. institutions. [ABSTRACT FROM AUTHOR]

Pedestrian traffic violation at intersections is the main cause of pedestrian traffic accidents, especially red-light crossing, which is common in China. This study uses evolutionary game theory to analyze the interaction mechanism of complex behavior between pedestrians and traffic enforcers when pedestrians face red traffic lights at intersections. This paper constructs the game matrix of pedestrian and traffic police under static and dynamic punishment mechanisms and analyzes the influence of key factors on the dynamic evolution process. It is found that there is no evolutionarily stable strategy for traffic police behavior and mechanism under static punishment mechanism. The dynamic punishment mechanism can effectively make up for the shortcomings of the static reward and punishment mechanism, so as to achieve an evolutionarily stable state. The impact of the probability of pedestrian traffic accidents in violation of traffic rules, the cost of strict management of traffic police, the probability of punishment for pedestrians under the loose management of traffic police, and the delay cost of pedestrians not violating traffic rules on ESS are analyzed. The research results have a guiding role for local traffic management departments to formulate traffic policies. • Describes the game relationship between pedestrians and traffic enforcers, and introduces more parameters. • The evolutionary game models of both parties were developed under the static penalty mechanism and dynamic penalty mechanism. • Sensitivity analysis is performed on important parameters that affect the decision-making of the player. • Rational government decisions are important for reducing traffic accidents and improving the traffic environment. [ABSTRACT FROM AUTHOR]

Education is not only a guarantee of personal knowledge, but also a topic of national importance. In this paper, we analyzed the scientific papers published by authors from China and the United States in 239 educational journals over a period of 20 years (2000–2019). We first extracted the topic of each paper via the Latent Dirichlet allocation model, which demonstrated significant differences in specific sub-fields between both countries. Next, we constructed two networks of scientific collaboration among the educators of each country. By analyzing these networks' topological properties, we show both countries' differences and common features. Regarding individuals, Chinese educators collaborate widely, whereas American educators are more likely to collaborate in certain groups. Regarding groups, there exists an optimal team size for producing highly popular papers in both countries. • Developing a topology-based comparison approach of the collaboration network in China and the USA. • Investigating the differences of collaborating phenomenon between education scientists in China and USA • Gap of paper popularity between China and the USA decreases by year. • Cross-community collaboration are more common in China, while scientists are more likely to collaborate in certain groups in the USA. [ABSTRACT FROM AUTHOR]

Abstract: In a recent paper in this journal [Q. Guo, L. Gao, Distribution of individual incomes in China between 1992 and 2009, Physica A 391 (2012) 5139–5145], a new family of distributions for modeling individual incomes in China was proposed. This family is the so-called Modified Gaussian (MG) distribution, which depends on two parameters. The MG distribution shows a satisfactory fit for the individual income data between 1992 and 2009. However, for the practical use of this model with individual incomes, it is necessary to know its probabilistic and statistical properties, especially the corresponding inequality measures. In this paper, probabilistic functions and inequality measures of the MG distribution are obtained in closed form, including the normalizing constant, probability functions, moments, first-degree stochastic dominance conditions, relationships with other families of distributions and standard tools for inequality measurement (Lorenz and generalized Lorenz curves and Gini, Donaldson–Weymark–Kakwani and Pietra indices). Several methods for parameter estimation are also discussed. In order to illustrate all the previous formulations, we have fitted individual incomes of Spain for three years using the European community household panel survey, concluding a static pattern of inequality, since the Gini index and other inequality measures remain constant over the study period. [Copyright &y& Elsevier]

Abstract: In China, both the mountainous areas and the number of people who live in mountain areas occupy a significant proportion. When production accidents or natural disasters happen, the residents in mountain areas should be evacuated and the evacuation is of obvious importance to public safety. But it is a pity that there are few studies on safety evacuation in rough terrain. The particularity of the complex terrain in mountain areas, however, makes it difficult to study pedestrian evacuation. In this paper, a three-dimensional surface cellular automata model is proposed to numerically simulate the real time dynamic evacuation of residents. The model takes into account topographic characteristics (the slope gradient) of the environment and the biomechanics characteristics (weight and leg extensor power) of the residents to calculate the walking speed. This paper only focuses on the influence of topography and the physiological parameters are defined as constants according to a statistical report. Velocity varies with the topography. In order to simulate the behavior of a crowd with varying movement velocities, and a numerical algorithm is used to determine the time step of iteration. By doing so, a numerical simulation can be conducted in a 3D surface CA model. Moreover, considering residents evacuation around a gas well in a mountain area as a case, a visualization system for a three-dimensional simulation of pedestrian evacuation is developed. In the simulation process, population behaviors of congestion, queuing and collision avoidance can be observed. The simulation results are explained reasonably. Therefore, the model presented in this paper can realize a 3D dynamic simulation of pedestrian evacuation vividly in complex terrain and predict the evacuation procedure and evacuation time required, which can supply some valuable information for emergency management. [Copyright &y& Elsevier]

Abstract: The visibility graph approach and complex network theory provide a new insight into time series analysis. The inheritance of the visibility graph from the original time series was further explored in the paper. We found that degree distributions of visibility graphs extracted from Pseudo Brownian Motion series obtained by the Frequency Domain algorithm exhibit exponential behaviors, in which the exponential exponent is a binomial function of the Hurst index inherited in the time series. Our simulations presented that the quantitative relations between the Hurst indexes and the exponents of degree distribution function are different for different series and the visibility graph inherits some important features of the original time series. Further, we convert some quarterly macroeconomic series including the growth rates of value-added of three industry series and the growth rates of Gross Domestic Product series of China to graphs by the visibility algorithm and explore the topological properties of graphs associated from the four macroeconomic series, namely, the degree distribution and correlations, the clustering coefficient, the average path length, and community structure. Based on complex network analysis we find degree distributions of associated networks from the growth rates of value-added of three industry series are almost exponential and the degree distributions of associated networks from the growth rates of GDP series are scale free. We also discussed the assortativity and disassortativity of the four associated networks as they are related to the evolutionary process of the original macroeconomic series. All the constructed networks have “small-world” features. The community structures of associated networks suggest dynamic changes of the original macroeconomic series. We also detected the relationship among government policy changes, community structures of associated networks and macroeconomic dynamics. We find great influences of government policies in China on the changes of dynamics of GDP and the three industries adjustment. The work in our paper provides a new way to understand the dynamics of economic development. [Copyright &y& Elsevier]

Understanding the information interaction mechanism of the mobile communication networks is great significant for understanding the human communication pattern. In this paper, a mobile communication network is constructed from the mobile phone call records of one specific city in China. We assign one weight on each edge to reflect the strength of social tie, which is the cumulative number of calls placed between the individuals. The experimental results of the weight distribution follows a power-law. In the mobile communication network with strong tie, the degree distribution also follows a power-law. From the perspective of the information interaction between individuals, the evolution mechanism of the mobile communication networks is given to explain the logical relation between the information interaction and the topology structure. Then a novel model based on the evolution mechanism is proposed to reproduce the topology characteristics of the mobile communication network. The analysis solutions of the weight distribution and the degree distribution in the model are presented. The model can help us to understand the law of the human information interaction and has significant implications for dynamic simulation researches of social networks, especially in information diffusion through the social networks. • The weight distribution follows the power-law in the mobile communication network. • An information interaction model based on the node importance is proposed. • The analysis solution of the weight distribution in the model is presented. • The model reproduces the topology characteristics of the mobile communication network. [ABSTRACT FROM AUTHOR]

Reputation and fame, two measurements of social popularity, play an increasing important role in the modern society. Reputation is influenced by one's performance of success, while often divorced from her/his professional achievements. Trying to figure out the relationship between the two measures is crucial for objectively rewarding excellence in all fields, such as entertainment, sports and sciences. Baidu popularity, an index developed by the dominating searching engine company in China, is designed to reflect the reputation of a person in Chinese media and publications. In this paper, Baidu popularity is utilized to quantify the relationship of a person's social popularity and her/his occupation parameters, such as the career length, the values of tournaments, rankings, etc. Taking table tennis athletes as an example, we quantify the relationship between the success and the Baidu popularity. The data collected from web sites is used to measure the Baidu popularity of athletes. A social popularity model (SPM) is constructed by combining the logarithm of the Baidu popularity and the linear combination of the logarithms of the four observed attributes. Experiments show that the model is effective and robust, and also reveals that performance indicators are the root cause of popularity in many areas of human achievement. The real-world data confirms that the SPM model is a timely and appropriate framework to measure the reputation of table tennis athletes. • The social popularity is quantified by the achievement. • Baidu index is introduced to measure the social popularity. • Achievements associate with the ranking, the career length, the matches' number, and the tournament's level. • The social popularity is a timely indices relying on achievements in the match. [ABSTRACT FROM AUTHOR]

Recent years have witnessed amount of studies on characteristics of public transport network and passengers' flows, however, most of them are discussed independently. An important criterion for evaluating public transport network is whether it could satisfy travel demand well. Therefore, in this paper, we study the interaction between public transport network and passengers' flow. Firstly, a layered network model is constructed for depicting public transport network, passengers' Origin–Destination (OD) flows and transfer flows. Based on that, we propose layer operations to analyze interaction between them and to evaluate suitability between traffic supply (physical infrastructure) and travel demand (passengers' OD trips and transfers). The proposed model is validated against empirical data from Chengdu and Qingdao, two metropolis cities in China. Results reveal the two cities have significant difference in suitability between travel demand and traffic supply. Although in contrast with Chengdu, Qingdao with advantages on transporting passengers by more straight lines and non-walking transfers, the average transfer flows and OD flows on stations in Qingdao are much heavier. The study demonstrates the importance of layer operations on exploring the interaction study of public transport network and traffic dynamics. • Public transport network, people's OD and transfer are studied by layered network. • Layer operations are proposed to explore the interaction between layers. • GPS and IC card data from Chengdu and Qingdao are used to validate our methods. • Layer operation is useful to study suitability between traffic supply and demand. [ABSTRACT FROM AUTHOR]

The growth curves of fractal dimension of urban form take on squashing effect and can be described by sigmoid functions. The fractal dimension growth of urban form in western countries can be modeled by Boltzmann's equation and logistic function. However, these models cannot be well applied to the fractal dimension growth curve of Beijing city, the national capital of China. In this paper, the experimental method is employed to find parametric models for the growth curves of fractal dimension of Chinese urban form. By statistical analysis, numerical analysis, and comparative analysis, we find that the quadratic Boltzmann equation and quadratic logistic function can be used to characterize how the fractal dimension of the urban land-use pattern of Beijing increases in the course of time. The models are also suitable for many cities in the north of China. In order to convert the empirical models into theoretical models, we attempt to construct a model of spatial replacement dynamics of urban evolution, from which the logistic model of urban fractal dimension growth can be derived. The models can be utilized to predict the rate and upper limitation of Chinese urban growth. In particular, the models can be employed to reveal the similarities and differences between the fractal growth of Chinese cities and that of the cities in western countries. • The squashing effect of limit values of fractal dimension leads to sigmoid curves. • Urban fractal dimension growth curves can be modeled by quadratic Boltzmann equation. • Quadratic Boltzmann equation can be reduced to a quadratic logistic function. • Fractal dimension growth curves of urban form fall into two types. • Fractal dimension growth curves indicate two types of spatial dynamics of cities. [ABSTRACT FROM AUTHOR]

This paper uses an agent-based model to study the sentiment contagion of interacting investors in China. We use text mining techniques to identify investor sentiment in stock forum, and then estimate the parameters of the sentiment contagion model to analyze sentiment formation from individual perspective. The empirical results suggest that the intensities of sentiment contagion in the periods of rising and falling are higher than that of the storming period. Intensities of sentiment contagion in emerging industries are higher than basic industries. A favorable macroeconomic environment will promote investors' optimism of the stock market, and high degree of investors' attention and discussions will accelerate contagion of such optimism. Once the stock market tends to be overheated, it may cause investors to worry about the market bubble, which will restrain further rise of investor sentiment. • Sentiment contagion intensity in rise or down stage is higher than storming stage. • The industries not directly linked to entity economy has higher contagion intensity. • The industries with lower sentiment contagion intensity are more basic. • Good macroeconomy and high investor attention will promote optimism sentiment. • The overheated stock market will restrain further rise of investor sentiment. [ABSTRACT FROM AUTHOR]

In this paper, we developed a novel measure to quantify the asymmetries in volatility spillovers, which emerge due to negative and positive shocks. Using high-frequency data of ten CSI 300 sector indices from 2007 to 2016, we employ our method to analyze the asymmetries in volatility spillover and volatility interdependence on China's stock market at the sector level. We find that in general, the spillover from bad volatilities, which due to negative movements in returns, is considerably stronger than that from good volatilities due to positive returns. Except when the State Council of China announced the 4 trillion stimulus program in November 2008 and raised the total maximum QFII quota by $50 billion to $80 billion in April 2012, during which the good volatility dominated. Moreover, our empirical results reveal the asymmetric spillovers transmission mechanism. By decomposing the overall shock into macroeconomic shocks that affect all sectors and industrial shocks that only affect the specific sector, we found that rather than industrial shocks, the macroeconomic shocks dominate the asymmetries in spillover transmission. • A new spillover asymmetry measure is developed. • Connectedness on China's stock market at industry level is explored. • Asymmetric spillovers transmission mechanism is revealed. • The industrial and macroeconomic shocks on China's stock market are examined. • The spillover from bad volatilities is considerably stronger in general. [ABSTRACT FROM AUTHOR]

This paper evaluates the relationships between weather variables like humidity, temperature, air pressure, wind speed and status (clear, cloud, rain, haze and snow) with stock returns and volatility in the Greater China region's stock market. The study focuses on four stock exchanges; Shanghai and Shenzhen stock exchanges from mainland China, Hong Kong stock exchange and Taiwan stock exchange from Hong Kong and Taiwan respectively. Using the robust approach the best peer econometric model for each market was chosen. The results of the study depict that weather variables have a significant effect on Greater China's capital markets. We are able to analyze that in some cases the variable affects returns as well as volatility • Prior studies have proved that weather has emotional effects. • Finance scholars have shown that emotions related to investor decisions. • I provide evidence that local weather affects stock returns. • I also explore which weather variable most liable for said effect. • I find that said effect is at least fall in every stock market of Greater China region. [ABSTRACT FROM AUTHOR]

Pedestrian dynamics in emergent situations such as stampedes and fire hazards have been widely studied, while few studies are focusing on human behavior in terrorist attack events. The research is vitally important for guiding efficient evacuation of the attacked crowd and reducing casualties. However, the research is insufficient due to the lack of researching materials. Besides, it is unconvincing for researchers to carry out these types of experiments. In this paper, the recorded video of the 3 ⋅ 1 Kunming terrorist attack event in China is analyzed. The instantaneous velocity field of the attacked crowd in the ticket hall is extracted from the video material, which is based on an image processing method. By analyzing the statistical characteristics of crowd velocity, crowd movement frequency and crowd entropy, several phenomena are found including crowd oscillating phenomenon, self-organizing grouping phenomenon. These discoveries will benefit the understanding of crowd dynamic in close-range attacks which is essential for the evacuation guidance efficiency, better design of architecture layout, and better defending strategies against the terrorists. • Crowd velocity peak is oscillating back and forth within the attacked crowd. • The velocity entropy of crowd is shown oscillating around the mean value 6.7. • The characteristic frequency of crowd movement is about 0.4 Hz. • Crowd in different area who have different movement behavior patterns show grouping phenomenon. [ABSTRACT FROM AUTHOR]

Abstract Network-wide traffic control strategies (e.g. perimeter control and route guidance) in urban networks have recently been mainly studied to relieve or postpone congestion based on the theory of macroscopic fundamental diagram (MFD). Nevertheless, these studies are mostly applied to the statically partitioned networks or the dynamic networks that fail to fully consider traffic state prediction, conflicting with strongly spatiotemporal variability of traffic congestion or objective of active traffic management (ATM). This paper proposes a methodology to dynamically identify critical congestion warning areas from heterogeneous urban road networks, which aids to design efficient perimeter control approaches. In the methodology, a dynamic directed weighted network is built on the base of the link connectivity and the real-time traffic loads, and a link travel time prediction method based on Kalman filter is developed to calibrate directed weight values and undirected input values for links. With the undirected link input information, a dynamic congestion warning community detection method which consists of three consecutive steps is developed. Firstly, it could capture emergence of new congestion areas based on the definition of congestion seed intersection. Secondly, expansion and regression of each congested area could be achieved with the objectives of spatial compactness and traffic condition homogeneity. Thirdly, a two-level merging algorithm of adjacent different congestion areas is designed utilizing modularity model in community detection of complex networks. The proposed methodology is validated using ground truth data from downtown network of Jinan City in China. The results show that the proposed algorithms can efficiently track congestion evolutionary processes and effectively detect congestion warning areas from the test real network. Highlights • First, we use community detection theory to identify traffic congestion warning areas. • Second, three consecutive steps are proposed to describe the entire process of traffic congestion propagation. • Third, homogeneous sub-areas with different levels of congestion are extracted from heterogeneous urban network. • Fourth, our method performs more effectively in real-word urban road networks. [ABSTRACT FROM AUTHOR]

Abstract Predictability is an important topic in weather and climate studies. Model-free and model-based quantification of diurnal temperature range (DTR) time series predictability is provided in this paper. Both intrinsic predictability quantified by the permutation entropy and realizable predictability defined by the model's forecasting error are studied on DTR fluctuations. The intrinsic predictability tells us whether there are more predictive structures in DTR fluctuations and to what extent the maximal predictability can be reached. The realizable predictability measures the degree of prediction to which adopted model reaches. Results show that there is a well defined regime-dependent pattern between the intrinsic predictability and the realizable predictability in DTR fluctuations, and both the intrinsic predictability and the realizable predictability in DTR fluctuations over China are overall South–North (with the dividing line around the latitude of 37°N and almost along the Yellow River, and south and north to this line are called southern China and northern China in this study) asymmetric with higher predictability in southern China and weaker predictability in northern China. This South–North contrasting predictability behavior is closely related to the DTR's South–North asymmetric multi-fractal behavior. Highlights • Both intrinsic and realizable predictability are studied on DTR fluctuations. • Well defined regime-dependent relation between two predictabilities exists. • Overall South–North asymmetric predictability is revealed in DTR over China. [ABSTRACT FROM AUTHOR]

Abstract This paper examines the impact of cross-shareholding and network positions in the cross-shareholding network on the financing constraints of private firms in China. Two indices are constructed to measure the position characteristics: network centrality and structural holes. Based on the data of listed private firms for 2008-2014, we find that the cross-shareholding relationship can significantly relieve the financing constraints of private firms. However, the role of cross-shareholding in reducing financing constraints is weakened as the level of financial marketization increases. Further, the results suggest that the higher the centralities or the more structural holes of private firms there are in the cross-shareholding networks, the lower the financing constraints of private firms. Highlights • The impact of cross-shareholding on financing constraints of private firms is examined. • The impact is examined from the perspective of social network. • The cross-shareholding relation can relieve financing constraints of private firms. • The higher centralities in the social networks means lower financing constraints. [ABSTRACT FROM AUTHOR]

Abstract This paper studies the systemic risk of China's stock market during crashes in 2008 and 2015 using the 5-minute intraday transaction data. The results show that liquidity contracted significantly after the downtrend. The systemic risk was magnified during the crash in 2008 while the system risk increased to an abnormal level before the crash in 2015. The volatility of systemic risk rose in 2015 compared to the one in 2008. Moreover, the Johansen co-integration test proves that there is a long-run equilibrium relationship between security margin trading and systemic risk volatility. Granger causality test indicates that margin financing is the Granger cause of the volatility of systemic risk in a bear market. This shows that the government response may impose negative effects on the systemic risk of China's stock market. It helps us better to understand features of systemic risk in China's stock market, and offer new ideas on how to reduce and stabilize the systemic risk. Highlights • The 5-minute intraday transaction data is used to study the systemic risk of China's stock market. • The system risk increased to an abnormal level before the crash in 2015. • There is a long-run equilibrium relationship between systemic risk volatility and margin trading. • The most risky industry changed from manufacturing to finance & insurance. • Government response imposes negative effects on the systemic risk of China's stock market. [ABSTRACT FROM AUTHOR]

Abstract Is idiosyncratic volatility priced? The existing literature finds conflicting results on the cross-sectional relation between expected returns and idiosyncratic volatility. This paper examines the relation between idiosyncratic volatility and expected returns in Chinese Stock Market. We find there is a significantly positive relation between idiosyncratic volatility and expected returns when we use Fama–French five-factor model to estimate idiosyncratic volatility. However, the positive relation disappears when we use GARCH (1,1) model to estimate idiosyncratic volatility. This result indicates that in Chinese Stock Market, the idiosyncratic volatility premium is just an apparent phenomenon, whether the idiosyncratic volatility matter or not depend on the way we estimate the idiosyncratic volatility. The result is robust after controlling for investors' lottery preference, investors' sentiment and other factors. Highlights • A new method is applied to estimate idiosyncratic volatility. • The idiosyncratic volatility is positively related to expected return. • Positive relation disappears when using alternate estimate method. • In China, the idiosyncratic volatility premium is just an apparent phenomenon. [ABSTRACT FROM AUTHOR]

Abstract The complexity of time series has become necessary to understand the dynamics of the control system. In this paper, the multivariate multiscale distribution entropy (MMSDE) is introduced as a new method to assess the complexity of dynamical systems such as financial systems, physiological systems, etc. Distribution entropy (DE) takes full advantage of the information hidden in the state–space by the estimation of the probability density of distances among vectors. Based on this, MMSDE can quantify the complexity of multivariate time series from multiple time scales. We test the performance of this method with simulated data. Results show that MMSDE has less dependence on parameters and the test of short time series is very effective. As for the real data, we explore the high dimensional series that are composed of opening price, closing price, volume of stocks data including US stock indices and Chinese stock indices. MMSDE can quantify the change in the complexity of the stock market data. In addition, we get richer information from MMSDE and gain some features about the difference between the U.S. and Chinese stock indexes. Highlights • Multivariate multiscale distribution entropy of financial time series is introduced as a new way is proposed. • The new model can be used to assess complexity of a complex dynamical system. • The experiments show this method is more sensitive to the change of short time series and describes the trends of complex systems clearly. [ABSTRACT FROM AUTHOR]

Abstract This paper adopts multifractal methods to analyze the multifractal characteristics of polluted time series in Beijing, Zhengzhou, and Jinan. The results of the multifractal detrended fluctuation analysis (MF-DFA) show that each of three air quality index (AQI) time series has multifractal characteristics, which provides evidence that the multifractal method but the traditional linear or monofractal method is feasible. The sources of multifractality in three AQI time series are all from long-range correlations and fat tail distributions, although the highlighted sources are different in three cities. The results of the coupling detrended fluctuation analysis (CDFA) show that SO 2 has a vital impact on air quality in each of three cities. O 3 has an important impact on air quality in Beijing and Jinan city and NO 2 has an important impact on air quality in Zhengzhou city. It is noted that PM 2.5 has not as large an impact as we imagine in three cities. It has a relatively large impact on air quality of Zhengzhou city, while it has a small impact on air quality of Beijing and Jinan city. The chi square χ 2 test further confirms the above results. Highlights • Each AQI time series has multifractal rather than monofractal characteristics. • There are two sources of multifractality. • The coupled correlations of six air polluted series have multifractal features. • SO 2 , O 3 and NO 2 have different impacts on air quality in each of three cities. [ABSTRACT FROM AUTHOR]

Abstract Recent literature draws attention to the relationship of some financial markets, in particular, both the co-movement and the lead–lag effect. This paper examines the weekly frequency market indices of Japan, Singapore, Hong Kong and China over the period 2000–2013 using wavelet analysis. The Morlet wavelet coherence model is employed since it allows the simultaneous examination of co-movement and lead–lag effect between the two markets in both the time and frequency domains. Our results show there exist a strong co-movement between stock markets of Japan, Singapore, Hong Kong and China in the long run and Japan leads the other markets in the long term. Highlights • The weekly frequency market indices of Japan, Singapore, Hong Kong and China are used. • Both the co-movement and the lead–lag effect are investigated. • The Morlet wavelet coherence model is employed. • There exist a strong co-movement between stock markets of Japan, Singapore. [ABSTRACT FROM AUTHOR]

Abstract The stock, bond and fund markets are three important components of a financial market, and the volatility of the markets and correlations between the markets have been paid extensive attention by researchers and investors. In this paper, we devote our efforts to studying the Shanghai financial market, while the return series of Shanghai Composite Index, Shanghai Bond Index and Shanghai Fund Index are considered. Statistical tests are used to detect the nonlinear auto-correlated structures and long-range cross-correlations of the three time series. The multifractal detrended fluctuation analysis and multifractal spectrum analysis methods are applied, by which the existence of multifractality in these three return series are revealed and the sources of multifractality are explored. In particular, the multiscale multifractal detrended cross-correlation analysis method is employed for the first time to generate the Hurst surfaces, which can be used to visualize the dynamic behaviors of cross-correlations among the markets. Empirical results show that the cross-correlations among the markets present different fractal features at different time scales. Further, our study finds that the correlation between the stock and fund markets is stronger than that of the other two groups, and the correlation between the stock and bond markets is unstable. These findings can help to better understand the dynamic mechanisms that govern the volatility of security markets and aid in performing better financial risk assessment and management. Highlights • Auto-correlations and cross-correlations of Chinese security markets are verified. • Existence of the multifractality of the three return series is confirmed. • Sources of the multifractality are explored. • Dynamic behaviors of the cross-correlations among the three markets are investigated. [ABSTRACT FROM AUTHOR]

Abstract Collective attention of investors maps the interests and intention of investors directly in the stock market. However, the evolution mechanism of the collective attention from the viewpoint of complex system is missing. In this paper, we empirically investigate the investor collective attention mechanism based on a best-known stock trading platform between 2014 and 2016. Taking the global and recent popularity effects into account, we introduce a generative model for the collective attention of millions of investors who are deciding their trading behavior among thousands of stocks in Chinese stock market. The experimental results show that the investor attention is more closely affected by recent attention, with the optimal case, when the memory effect parameter T = 10 and the recent popularity parameter γ = 0. 1 , the model could regenerate the collective attention more accurately, say Kendall’s τ = 0. 92 for the Shanghai Stock Exchange(SSE) and Shenzhen Stock Exchange(SZSE) simultaneously. This work may shed some lights for deeply understanding the mechanism of the investor collective attention for the financial market. Highlights • Taking into account the cumulative and recent popularity, we introduce a generative model for the collective attention of investors. • The investor attention is more closely affected by recent attention. With the optimal case, the Kendall’s τ = 0. 92 for two different stock exchanges. • This work enrich the research of investor collective attention from the view of complex system. [ABSTRACT FROM AUTHOR]

Understanding the complex network response to nodes failure is important to protect them and guarantee their functioning. Most existing studies considered binary networks where the intensity (weight) of the links is not accounted for. However, in real world networks the intensity of the links widely differ so that a realistic description of complex networks is only possible if the heterogeneity present in edge weights is taken into account. Further, the vast majority of the existing studies investigates the efficacy of the attack strategies when all the nodes are removed. Despite it is important to study the network behavior in such a circumstance, real-world networks are more likely to experience local failures involving a small amount of nodes with a decrease in the system functioning not causing an overall collapse. This is the case of crossroad closing in complex road networks. For this reason it is interesting to investigate the networks response focusing to small node removals. In this paper we analyze the response of the real-world road networks of Beijing, the capital of China, to the removal of 1 and 2 nodes and accounting the weighted structure of the network. We use both classic binary node properties and network functioning measurement, introduced a weighted rank for node importance (node strength or page rank), and used a measurement for network functioning that accounts for the weight of the links (weighted efficiency). We find that the weighted node attack strategy Str (i.e. strength attack), which removes nodes according to their strength, is the most efficient to blemish the system when evaluating the damage with a weighted efficiency. Differently, betweenness attack strategy Bet , which removes nodes according to their centrality is the most efficient when using the binary efficiency to compute the network damage. These results indicate that adopting weighted or binary efficiency to measure networks functioning changed the efficacy of the attack strategies to damage the system. Last, we found a non-negligible fraction of random removals to be more efficient than the attack strategies; given that the main goal of the attack strategies is to individuate important nodes in the system, i.e. nodes which removal triggers highest damage, our results would indicate how in order to find the most important nodes may be more efficient to simulate an higher number of random nodes removal. [ABSTRACT FROM AUTHOR]

The freeway system of China is the longest in the world and planned to expand in the following years. However, the safety conditions of freeways are drawing increasing concerns both from the authorities and the public in the country. The safety design and management for freeways are urgently needed considering the current safety situation and great demands of new freeways. The study presented in this paper thoroughly investigated factors affecting safety using detailed data of crashes, traffic characteristics and freeway geometry. A random effects negative binomial (RENB) model was applied to account for spatial variations within groups together with a negative binomial (NB) model. The results indicated a better goodness of fit of RENB model than the NB model. In addition, a good number of factors significantly contributed to crash, such as truck proportion, presence of climbing lane, median barrier offset, curvature and longitudinal grade, were identified. This study was expected to provide a better understanding of how traffic condition and freeway design affect safety and should be useful to freeway engineers to design safe freeways, or develop effective safety countermeasures. [ABSTRACT FROM AUTHOR]

In this paper, different from the previous studies starting from the micro perspective, we address the key question on the neighborhood effects of provincial-level stock market participation in China. By choosing two different measures of participation behaviors, we have found that there exists provincial imbalance in China. Further, by using the spatial panel data models, we investigate the spatial spillover effect of stock market participation between neighboring provinces, and find that participation behaviors of bordering provinces can affect each other directly and indirectly. We also consider some effects from the explanatory variables and find that, (1) per capita GDP has a significantly positive effect on stock market participation; (2) net population inflow has a significantly negative effect. [ABSTRACT FROM AUTHOR]

The price limit trading rule is one of the most widely adopted measures on restricting stock price volatilities in some stock markets. It is expected to stabilize the stock markets and enhance the efficiency of the market allocations. The existence of the cooling-off effect or the magnet effect, induced by the price limit trading rule, is the main controversy of this policy. In this paper, we investigate the cooling-off effect (opposite to the magnet effect) from two aspects. Firstly, from the viewpoint of dynamics, we study the existence of the cooling-off effect by following the dynamical evolution of some financial variables over a period of time before the stock price hits its limit. Secondly, from the probability perspective, we investigate, with the logit model, the existence of the cooling-off effect through analyzing the high-frequency data of all A-share common stocks traded on the Shanghai Stock Exchange and the Shenzhen Stock Exchange from 2000 to 2011 and inspecting the trading period from the opening phase prior to the moment that the stock price hits its limits. A comparison is made of the properties between up-limit hits and down-limit hits, and the possible difference will also be compared between bullish and bearish market state by dividing the whole period into three alternating bullish periods and three bearish periods. We find that the cooling-off effect emerges for both up-limit hits and down-limit hits, and the cooling-off effect of the down-limit hits is stronger than that of the up-limit hits. The difference of the cooling-off effect between bullish period and bearish period is quite modest. Moreover, we examine the sub-optimal orders effect, and infer that the professional individual investors and institutional investors play a positive role in the cooling-off effects. All these findings indicate that the price limit trading rule exerts a positive effect on maintaining the stability of the Chinese stock markets. [ABSTRACT FROM AUTHOR]

The crowd panic and its contagion play non-negligible roles at the time of the stock crash, especially for China where inexperienced investors dominate the market. However, existing models rarely consider investors in networking stocks and accordingly miss the exact knowledge of how panic contagion leads to abrupt crash. In this paper, by networking stocks of sharing common mutual funds, a new methodology of investigating the market crash is presented. It is surprisingly revealed that the herding, which origins in the mimic of seeking for high diversity across investment strategies to lower individual risk, will produce too-connected-to-fail stocks and reluctantly boosts the systemic risk of the entire market. Though too-connected stocks might be relatively stable during the crisis, they are so influential that a small downward fluctuation will cascade to trigger severe drops of massive successor stocks, implying that their falls might be unexpectedly amplified by the collective panic and result in the market crash. Our findings suggest that the whole picture of portfolio strategy has to be carefully supervised to reshape the stock network. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of global economic policy uncertainty (GEPU) on the volatility of the Chinese stock market and whether GEPU has predictive power for the volatility of the Chinese stock market. We apply the generalized autoregressive conditional heteroscedastic mixed data sampling (GARCH–MIDAS) model to evaluate the impact of the monthly GEPU index on the daily Shanghai Composite index. Our empirical results show that GEPU has a positive and significant influence on the volatility of the Chinese stock market, which reflects that Chinese stock market has been gradually integrated into the global economy. In addition, the forecasts generated by the GARCH–MIDAS model with GEPU and RV (Realized Volatility) produce substantially smaller errors than the GARCH (1,1) model and the GARCH–MIDAS model with RV (measured by RMSE, RMAE, RMSD, RMAD loss functions and DM test), which confirms the important role of GEPU for predicting the volatility of the Chinese stock market. Overall, our findings imply that GEPU is an additional driving factor of long-term volatility in the Chinese stock market. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we apply MF-X-DMA method to investigate the multifractal features between Chinese stock index and three stock indexes in "The Belt and Road Initiative" in both global and local periods, and find the source of multifractality. The cross-correlation coefficients ρ D M A show that these markets are positively and significantly correlated for different window intervals. The cross-correlations between Chinese stock market and the other three stock markets are multifractal and there exists more long-term trade behavior than short-term behavior. Compared with the pre-crisis period 2004–2008, the multifractal characteristics are much more persistent after "The Belt and Road Initiative" proposed. Finite-size effect, which contributes 55%–87% of the singularity width, is the most important component of multifractality between Chinese stock market and the stock markets in "The Belt and Road Initiative", while both non-linear long-range correlations and fat-tail distributions are much less important components in multifractality. Highlights • The cross-correlation coefficients between these markets are positively and significantly correlated for different window intervals. • There exists more long-term trade behaviors than short-term behaviors. • Compared with the pre-crisis period 2004–2008, the multifractal characteristics are much more persisent after "The Belt and Road Initiative" proposed. • Finite-size effect is the most important component of multifractality between these stock markets. Non-linear long-range correlations and fat-tail distributions are much less important components in multifractality. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we study the multifractal scaling behaviour in Shanghai and Hong Kong stock markets by means of multifractal detrended fluctuation analysis (MF-DFA) and multifractal detrended cross-correlation analysis (MF-DCCA). The results show that the multifractal degrees of each stock market are larger after the Shanghai–Hong Kong Stock Connect Program (SHSCP) than before. Scaling analysis demonstrates that multifractality exists in cross-correlations, and the cross-correlation coefficients after the SHSCP are larger than those before the SHSCP. Moreover, an analysis of the origin of multifractality indicates that long-range correlation and fat-tailed distribution play important roles in the contributions of multifractality. Finally, the results via the sliding window procedure indicate that the multifractal degrees after the SHSCP are not significantly affected by the stock market turbulence in 2015. Highlights • Two stock returns are multifractal before and after the SHSCP. • Multifractality exists in cross-correlations between the two stock markets. • The multifractal degrees become larger after the SHSCP than before. • Long-range correlation and fat-tailed distribution are the sources of multifractality. • The multifractal degrees are not significantly affected by the stock market crash. [ABSTRACT FROM AUTHOR]

Abstract Using stock market returns of two stock exchanges in China, this paper employs MF-DCCA to investigate the non-linear cross-correlation between individual investor sentimentand Chinese stock market return. We find that there exists a power-law cross-correlation between individual investor sentiment and Chinese stock market return, and the cross-correlations are significantly multifractal. In addition, the cross-correlation between individual investor sentiment and Shenzhen Component Index (SZSE) return is more anti-persistent than that between individual investor sentiment and Shanghai Composite Index (SSEC) return, implying that individual investor sentiment has a stronger impact on small stocks. Besides, long-range correlations, fat-tailed distribution and extreme value all contribute to the multifractality for cross-correlation between individual investor sentiment and SSEC return, while long-range correlation is the main source of multifractality for the cross-correlation between individual investor sentiment and SZSE return. Highlights • Cross-correlation between investor sentiment and stock return is investigated. • Yu'ebao Sentiment Index and Shenzhen stock market return is more anti-persistent. • The sources of multifractal features are investigated. [ABSTRACT FROM AUTHOR]

Abstract: In the literature, studies on real estate market were mainly concentrating on the relation between property price and some key factors. The trend of the real estate market is a major concern. It is believed that changes in trend are signified by some jump points in the property price series. Identifying such jump points reveals important findings that enable policy-makers to look forward. However, not all jump points are observable from the plot of the series. This paper looks into the trend and introduces a new approach to the framework for real estate investment success. The main purpose of this paper is to detect jump points in the time series of some housing price indices and stock price index in Hong Kong by applying the wavelet analysis. The detected jump points reflect to some significant political issues and economic collapse. Moreover, the relations among properties of different classes and between stocks and properties are examined. It can be shown from the empirical result that a lead-lag effect happened between the prices of large-size property and those of small/medium-size property. However, there is no apparent relation or consistent lead in terms of change point measure between property price and stock price. This may be due to the fact that globalization effect has more impact on the stock price than the property price. [Copyright &y& Elsevier]

Research on the evolution mechanism of population distribution has practical applications in transportation science, spatial economics and many other fields. However, the evolution of population distribution is influenced by numerous factors, among which transportation is a very important one. This paper proposed a dynamic model of population distribution that considers the impact of the transportation network. In the model, the human prefers to move to areas with good transportation. By considering the highway and railway network, we investigate the effects of them on the population spatial distribution. The parameters of the model can govern how many people can immigrate and emigrate in each area. It can be determined by the nonlinear least square method. Furthermore, we use the data of china's population distribution to demonstrate our model's ability. Moreover, this model can help us better understand the underlying mechanism of population distribution evolution. • This paper considers the impact of transportation network on the evolution of population distribution. • The population distribution change rate depends on the mechanism of emigration demand, immigration attraction and the utility of transportation network. • We use the nonlinear least square method to calibrate the parameters of the dynamic model. • The changes of population spatial distribution in 31 provinces in China from 1995 to 2019 is studied. [ABSTRACT FROM AUTHOR]

In this paper, guided by macroscopic cell transmission models, a cell transmission model of aircraft traffic flow on runways, taxiways and aprons was established to deduce and analyze the evolution rules of an airport surface traffic. The model was simulated by adopting data from a large domestic airport in China. The relationship between three parameters of traffic flow on taxiways was evaluated, and the airport surface traffic flow characteristics were analyzed. Moreover, this paper macroscopically analyzed the characteristics of spatial–temporal distribution of airport surface recurrent and non-recurrent traffic congestion. Simulation results showed that arrival rates and pushback rates had significant influence on the formation and dissipation of recurrent traffic congestion in different phases of the surface aircraft traffic flow. When the arrival rate was 0.3 aircraft/minute and the pushback rate was 0.23 aircraft/minute, the speed of aircraft on taxiways was high, the density and flow were large, and the aircraft had a high degree of obstacle-free taxiing. At this time, the airport ran smoothly and the surface network operation efficiency was high. Compared with the arrival rate, the outward expansion trend of each parameter was more obvious when the pushback rate changed. Parallel taxiways are less robust than apron taxiways and connect taxiways. • Establish a cell transmission model (CTM) of traffic flow on airport surface. • Analyze spatial–temporal features of recurrent and non-recurrent traffic congestion. • Arrival rates and pushback rates influence the recurrent traffic congestion greatly. [ABSTRACT FROM AUTHOR]