Chinese stock market integration with developed world: A portfolio diversification analysis.

This study investigates integration dynamics between the Chinese stock market and major developed counterparts-Australia, Germany, Japan, the UK, and the US-focusing on portfolio diversification. Using a comprehensive analytical approach from 2012 to 2022, encompassing events like the Belt and Road Initiative, the Shanghai market crash, US-China trade tensions, and the COVID-19 pandemic, the research employs descriptive statistics, unit root tests, cointegration analysis, and VECM-based Granger Causality Tests. Findings indicate modest integration, endorsing diversified portfolios for developed country investors due to higher returns in China with acceptable risk.

Predicting multiple observations in complex systems through low-dimensional embeddings.

Forecasting all components in complex systems is an open and challenging task, possibly due to high dimensionality and undesirable predictors. We bridge this gap by proposing a data-driven and model-free framework, namely, feature-and-reconstructed manifold mapping (FRMM), which is a combination of feature embedding and delay embedding. For a high-dimensional dynamical system, FRMM finds its topologically equivalent manifolds with low dimensions from feature embedding and delay embedding and then sets the low-dimensional feature manifold as a generalized predictor to achieve predictions of all components.

Research on the crisis propagation in the global coal trade under the Russia-Ukraine conflict.

The outbreak of the 2022 Russia-Ukraine conflict exacerbated the natural gas supply shortage in European countries. European countries restarted coal-fired power plants to maintain economic and social operations. The uneven distribution of coal resources in the world makes coal international trade inevitable.

Adjusting the global industrial structure for minimizing global carbon emissions: A network-based multi-objective optimization approach.

Although many scholars have focused on industrial structure adjustment to find the optimal balance between carbon emission reduction and economic growth, few studies have considered the comprehensive influence of the supply chain structure on carbon emissions. Based previous studies, we proposed a novel network-based optimization model. The results showed that carbon emissions would decrease by 4.

Structural decomposition analysis of global carbon emissions: The contributions of domestic and international input changes.

Finding the essential factors driving carbon emissions is vital for the carbon reduction policy-making. Different from the existing research, this paper studied the separate influence of internal and external input structural changes on global carbon emissions. We applied structural decomposition analysis (SDA) to decompose the global carbon emission change into six factors: namely, the carbon emission intensity, the domestic input structure, the international input structure, consumption pattern, consumption volume and population.

Identifying key sectors based on cascading effect along paths in the embodied CO emission flow network in Beijing-Tianjin-Hebei region, China.

The emission of carbon dioxide (CO) is a serious environmental issue, especially in Beijing-Tianjin-Hebei region. Unlike previous studies that mainly consider the bilateral and direct connection between two sectors, this study identifies path-based key sectors by considering the cascading effect of a sector on other sectors on paths of the entire economic system. We first construct an embodied CO emission flow network of Beijing-Tianjin-Hebei region, combining environmental input-output analysis and complex network theory.

What Induces the Energy-Water Nexus in China's Supply Chains?

Given energy and water scarcity, it is necessary to develop an in-depth understanding of the energy-water nexus in China for its sustainable development. Previous studies have focused on nexus accounting, synergy conservation, and system optimization, but its induction mechanism along the supply chains has not been uncovered. This paper proposes a top-down structural path analysis (SPA) and combines it with an environmental input-output model (EIOM) to identify the critical final demand, consumption sectors, and supply chain paths inducing the energy-water nexus.

Information Connections among Multiple Investors: Evolutionary Local Patterns Revealed by Motifs.

The concept of motifs provides a fresh perspective for studying local patterns, which is useful for understanding the essence of a network structure. However, few previous studies have focused on the evolutionary characteristics of weighted motifs while further considering participants' differences. We study how information connections differ among multiple investors.

Reconstructing complex network for characterizing the time-varying causality evolution behavior of multivariate time series.

In order to explore the characteristics of the evolution behavior of the time-varying relationships between multivariate time series, this paper proposes an algorithm to transfer this evolution process to a complex network. We take the causality patterns as nodes and the succeeding sequence relations between patterns as edges. We used four time series as sample data.

Sentiment Diffusion of Public Opinions about Hot Events: Based on Complex Network.

To study the sentiment diffusion of online public opinions about hot events, we collected people's posts through web data mining techniques. We calculated the sentiment value of each post based on a sentiment dictionary. Next, we divided those posts into five different orientations of sentiments: strongly positive (P), weakly positive (p), neutral (o), weakly negative (n), and strongly negative (N).

Words analysis of online Chinese news headlines about trending events: a complex network perspective.

Because the volume of information available online is growing at breakneck speed, keeping up with meaning and information communicated by the media and netizens is a new challenge both for scholars and for companies who must address public relations crises. Most current theories and tools are directed at identifying one website or one piece of online news and do not attempt to develop a rapid understanding of all websites and all news covering one topic. This paper represents an effort to integrate statistics, word segmentation, complex networks and visualization to analyze headlines' keywords and words relationships in online Chinese news using two samples: the 2011 Bohai Bay oil spill and the 2010 Gulf of Mexico oil spill.

Characteristics of the transmission of autoregressive sub-patterns in financial time series.

There are many types of autoregressive patterns in financial time series, and they form a transmission process. Here, we define autoregressive patterns quantitatively through an econometrical regression model. We present a computational algorithm that sets the autoregressive patterns as nodes and transmissions between patterns as edges, and then converts the transmission process of autoregressive patterns in a time series into a network.

Analyzing the international exergy flow network of ferrous metal ores.

This paper employs an un-weighted and weighted exergy network to study the properties of ferrous metal ores in countries worldwide and their evolution from 2002 to 2012. We find that there are few countries controlling most of the ferrous metal ore exports in terms of exergy and that the entire exergy flow network is becoming more heterogeneous though the addition of new nodes. The increasing of the average clustering coefficient indicates that the formation of an international exergy flow system and regional integration is improving.

Transmission of linear regression patterns between time series: from relationship in time series to complex networks.

The linear regression parameters between two time series can be different under different lengths of observation period. If we study the whole period by the sliding window of a short period, the change of the linear regression parameters is a process of dynamic transmission over time. We tackle fundamental research that presents a simple and efficient computational scheme: a linear regression patterns transmission algorithm, which transforms linear regression patterns into directed and weighted networks.

Features of the correlation structure of price indices.

What are the features of the correlation structure of price indices? To answer this question, 5 types of price indices, including 195 specific price indices from 2003 to 2011, were selected as sample data. To build a weighted network of price indices each price index is represented by a vertex, and a positive correlation between two price indices is represented by an edge. We studied the features of the weighted network structure by applying economic theory to the analysis of complex network parameters.