Network analysis of the e-MID overnight money market: the informational value of different aggregation levels for intrinsic dynamic processes

In this paper, we analyze the network properties of the Italian e-MID data based on overnight loans during the period 1999–2010. We show that the networks appear to be random at the daily level, but contain significant non-random structure for longer aggregation periods. In this sense, the daily networks cannot be considered as being representative for the underlying ‘latent’ network. Rather, the development of various network statistics under time aggregation points toward strong non-random determinants of link formation. We also identify the global financial crisis as a significant structural break for many network measures.     

股指极端波动下中国股市复杂网络结构及中心性分析

基于复杂网络理论,以2015年、2008年国内两次股灾为背景,分别构建股灾发生前、中、后的中国股市网络,通过度、度分布、平均路径长度等基本拓扑指标,分析中国股市网络特性及网络结构的变化,综合利用度中心性、介数中心性及接近度中心性,筛选出各时期网络中的核心股票、核心行业并分析其变化情况,基于网络特征向量中心性分析股市的系统性风险及变化情况,通过仿真实验分析股市网络的鲁棒性。研究表明:两次股灾背景下的中国股市复杂网络均具有小世界性和无标度性;与2008年国际金融危机相比,2015年国内股灾对中国股市的影响强度更大,且2015年中国股市对金融风险的弹性更大;股灾期间各行业版块具有明显的风险传染性,指出各行业板块对稳定股市、修复股指的作用;股指极端波动时,股灾的外生冲击会使股市的系统性风险加大,与2015年国内股灾时期相比,2008年国际金融危机时期的股市系统性风险更大;中国股市网络对随机攻击具有一定鲁棒性,但对蓄意攻击具有脆弱性,股灾的外生冲击会降低中国股市网络的鲁棒性。研究为把握股市极端波动风险下的市场结构特征、股市风险管理提供了参考。     

Assessing dynamic inefficiency of the Spanish construction sector pre- and post-financial crisis

This paper undertakes the full decomposition of dynamic cost inefficiency into technical, scale and allocative inefficiency based on the dynamic directional distance function. The empirical application estimates dynamic inefficiency in the Spanish construction industry before and during the current financial crisis over the period 2001–2009. Static inefficiency measures are biased in a context of a significant economic crisis with large investments and disinvestments as they do not account for costs in the adjustment of quasi-fixed factors. Allocative inefficiency is smaller, while technical inefficiency is larger when using the dynamic compared to the static framework. Results further indicate that overall dynamic cost inefficiency is very high with technical inefficiency being the largest component, followed by allocative and scale inefficiency. Moreover, overall dynamic cost inefficiency is significantly larger before the beginning of the financial crisis than during the financial crisis. Larger firms are less technically and scale inefficient than smaller firms on average, but have more problems in choosing the mix of inputs that minimizes their long-term costs. Firms that went bankrupt, on average, have a higher overall dynamic cost inefficiency and scale inefficiency than continuing firms.     

The network structure and systemic risk in the global non-life insurance market

This paper contributes to the literature on systemic risk by assessing the systemic importance of insurers in the global non-life insurance market. First, we estimate the bilateral reinsurance claims matrix using the aggregate outstanding reinsurance data from ISIS and theoretically analyze the interconnectedness in the global reinsurance network using network indicators. The robustness of the estimated matrix is fully assured by sensitivity analysis. Second, we theoretically analyze the contagious defaults introducing the Eisenberg–Noe framework. Reinsurers play a dominant role in the reinsurance network and most of them are included in our data sample. The network analysis finds that some reinsurers with large centrality measures are central in the hierarchical structure of the network. The default analysis shows the occurrences of many stand-alone defaults and only one contagious default via the global reinsurance network after the global financial crisis. In addition, one stress test based on a hypothetical severe stress scenario predicts a few occurrences of contagious defaults in the future. It follows from these analyses that systemic risk via the global reinsurance network is relatively restricted in the global non-life insurance market. In conclusion, our methodology would help supervisory authorities develop an assessment approach for interconnectedness in the global reinsurance network and aid the implementation of insurer stress tests for default contagion.     

Ecosystems perspective on financial networks: Diagnostic tools

The world economy consists of highly interconnected and interdependent commercial and financial networks. Here, we develop temporal and structural network tools to analyze the state of the economy and the financial markets. Our analysis indicates that a strong clustering can be a warning sign. Reduction in diversity, which was an essential aspect of the dynamics surrounding the financial markets crisis of 2008, is seen as a key emergent feature arising naturally from the evolutionary and adaptive dynamics inherent to the financial markets. Similarly, collusion amongst construction firms in a number of regions in Japan in the 2000s can be identified with the formation of clusters of anomalous highly connected companies. © 2013 Wiley Periodicals, Inc. Complexity 19: 22–36, 2013     

Exploring communication networks to understand organizational crisis using exponential random graph models

In recent social network studies, exponential random graph (ERG) models have been used comprehensively to model global social network structure as a function of their local features. In this study, we describe the ERG models and demonstrate its use in modelling the changing communication network structure at Enron Corporation during the period of its disintegration. We illustrate the modelling on communication networks, and provide a new way of classifying networks and their performance based on the occurrence of their local features. Among several micro-level structures of ERG models, we find significant variation in the appearance of A2P (Alternating k-two-paths) network structure in the communication network during crisis period and non-crisis period. We also notice that the attribute of hierarchical positions of actors (i.e., high rank versus low rank staff) have impact on the evolution process of networks during crisis. These findings could be used in analyzing communication networks of dynamic project groups and their adaptation process during crisis which could lead to an improved understanding how communications network evolve and adapt during crisis.     

Supply chain network equilibrium with strategic financial hedging using futures

In this paper, we develop a network equilibrium model for supply chain networks with strategic financial hedging. We consider multiple competing firms that purchase multiple materials and parts to manufacture their products. The supply chain firms’ procurement activities are exposed to commodity price risk and exchange rate risk. The firms can use futures contracts to hedge the risks. Our research studies the equilibrium of the entire network where each firm optimizes its own operation and hedging decisions. We use variational inequality theory to formulate the equilibrium model, and provide qualitative properties. We provide analytical results for a special case with duopolistic competition, and use simulations to study an oligopolistic case. The analytical and simulation studies reveals interesting managerial insights.     

尾部风险网络视角下的金融机构系统性风险贡献研究

金融机构的尾部风险关联模式及结构在金融系统性风险的形成演化中发挥重要作用。利用CoVaR指标及分位数回归方法,衡量金融机构之间的尾部风险传染强度,进而建立金融机构尾部风险动态网络。分析全连接网络及阈值法下过滤网络的全局和局部拓扑结构特征及其动态演化规律。建立面板数据回归模型,研究网络拓扑结构特征对金融机构系统性风险贡献的影响。实证研究发现,全连接网络的节点强度,能有效地衡量金融机构尾部风险传染强度及承受强度,并揭示其动态变化规律;各机构的尾部风险传染强度及承受强度排序匹配性存在差异;随着时间推进,各金融机构间的平均尾部风险传染路径缩短、系统性风险更易迅速累积和爆发;滞后一期的节点出度、节点入度及聚集系数越大,相应金融机构的系统性风险贡献越小;滞后一期的节点介数和节点接近中心度越大,相应金融机构的系统性风险贡献越大。研究结果对于金融机构的宏观审慎监管及系统性风险管理,提供了一个全新的基于金融机构尾部风险网络的视角。     

金融风险在股票市场的传染效应及联动行为分析

近年来金融危机频发并表现出了易传染性,引起了众多学者的高度关注。以动态条件相关模型研究美欧股市与中、日、韩股市间的时变相关性,并结合内生多重结构突变模型划分危机传染阶段,选用溢出指数模型分析股市间的风险溢出特性;随后,定义股市间相互影响的联动模式并构建不同传染阶段的加权有向网络图分析股市间的联动行为。研究表明:美欧股市对中日韩股市有明显的传染效应,被传染的速度和持续时间均不相同;金融传染和风险溢出展现出一定的不一致性,危机期间日股的风险溢出效应强于美股;传染效应在联动网络中表现为联动模式的高聚类性和高联动性,相比欧债危机,次贷危机时期股市间展现出更强的联动行为;日股与美欧股市在两次危机中均表现出最强的联动性,其所受影响也最大。     

Structure and dynamics of the global financial network

In this paper, we study the evolution of the network topology for the global financial market. We evaluate the level of diversification and participation of developed and emerging economies in cross-border exposures and find that the gross exposure network is dense, the vulnerability matrix is sparse, and the network’s fragility changes over time. Prior to the financial crisis in 2008, the network was relatively fragile, whereas it became more resilient afterwards, showing a reduction in financial institutions’ risk appetite. Our results suggest that financial regulators should track down the network evolution in their systemic risk assessment.     

The co-evolution of integrated corporate financial networks and supply chain networks with insolvency risk

In this paper, we develop a modeling framework that integrates supply chain networks and corporate financial networks, and studies the co-evolution of the two types of networks. The framework allows one to investigate the following questions: (1) How do the physical product flows, demands and prices interplay with the corporate financial networks, and evolve over time with varying financial and economic conditions? (2) How do the financial flows, profits, and insolvency risks interplay with the supply chain networks, and evolve over time with varying financial and economic conditions? We illustrate the modeling framework with computational studies. In particular, the computational results demonstrate how the two types of networks interplay and evolve in a business environment similar to the one during the 2008–2009 financial crisis in which the credit markets tightened up and the product demands continued to decline.     

The Dynamics of Closeness and Betweenness

Although both betweenness and closeness centrality are claimed to be important for the effectiveness of someone's network position, it has not been comprehensively studied which networks emerge if actors strive to optimize their centrality in the network in terms of betweenness and closeness. We study each of these centrality measures separately, but we also analyze what happens if actors value betweenness and closeness simultaneously. Network dynamics differ considerably in a scenario with either betweenness or closeness incentives compared with a scenario in which closeness and betweenness incentives are combined. There are not only more stable networks if actors’ betweenness and closeness are combined, but also these stable networks are less stylized.     

Prediction of financial distress: An empirical study of listed Chinese companies using data mining

The deterioration in profitability of listed companies not only threatens the interests of the enterprise and internal staff, but also makes investors face significant financial loss. It is important to establish an effective early warning system for prediction of financial crisis for better corporate governance. This paper studies the phenomenon of financial distress for 107 Chinese companies that received the label ‘special treatment’ from 2001 to 2008 by the Shanghai Stock Exchange and the Shenzhen Stock Exchange. We use data mining techniques to build financial distress warning models based on 31 financial indicators and three different time windows by comparing these 107 firms to a control group of firms. We observe that the performance of neural networks is more accurate than other classifiers, such as decision trees and support vector machines, as well as an ensemble of multiple classifiers combined using majority voting. An important contribution of the paper is to discover that financial indicators, such as net profit margin of total assets, return on total assets, earnings per share, and cash flow per share, play an important role in prediction of deterioration in profitability. This paper provides a suitable method for prediction of financial distress for listed companies in China.     

Supply chain performance assessment and supplier and component importance identification in a general competitive multitiered supply chain network model

In this paper, we develop a multitiered competitive supply chain network game theory model, which includes the supplier tier. The firms are differentiated by brands and can produce their own components, as reflected by their capacities, and/or obtain components from one or more suppliers, who also are capacitated. The firms compete in a Cournot–Nash fashion, whereas the suppliers compete a la Bertrand since firms are sensitive to prices. All decision-makers seek to maximize their profits with consumers reflecting their preferences through the demand price functions associated with the demand markets for the firms’ products. We construct supply chain network performance measures for the full supply chain and the individual firm levels that assess the efficiency of the supply chain or firm, respectively, and also allow for the identification and ranking of the importance of suppliers as well as the components of suppliers with respect to the full supply chain or individual firm. The framework is illustrated through a series of numerical supply chain network examples.     

Communication Roles, Perceived Effectiveness, and Satisfaction in an Environmental Management Program

Earlier research has shown a relationship between various forms of structural centrality and perceived leadership and role satisfaction in small experimental groups. The limited amount of research on this topic in naturally occurring social networks has yielded results that often conflict with one another. Different results have generally been attributed to possible differences in task environments. This paper examines the relationship between two types of structural centrality and perceived influence, role satisfaction, and perceived effectiveness in an environmental resource management program. Findings in this paper suggest that the observed differences in relationships between the network and other variables is partly a function of global network properties (e.g., marginality of subgroups) and related task environments. This revised version was published online in August 2006 with corrections to the Cover Date.     

基于相空间重构技术的金融系统混沌识别

借助工程技术领域内识别非线性系统混沌现象的相空间重构技术研究了金融混沌的识别问题.以我国金融系统历经本轮全球金融危机这个金融史上影响最为严重的金融混沌为研究对象;研究发现:在全球金融危机的冲击下,我国金融系统在运行过程中发生了确定性的失稳,出现了金融混沌现象.从而为进一步防范与控制金融混沌奠定基础.     

Productivity and technical changes in the Indian pharmaceutical industry

We study the productivity change and factors driving this change in the Indian pharmaceutical industry during 1994–2003, in the backdrop of economic liberalization and change in regulatory norms. We use a non parametric Data Envelopment based-methodology to estimate productivity change and decompose it into technical and relative efficiency changes. We find that, the long-term strategic measures by a section of innovative firms that foresaw the implications from competitive forces of globalization and a change in the regulatory environment have sphereheaded the technical change. Consequently, few innovative firms, characterized by greater R&D investments, transition into higher value-added products and businesses as a step towards more technically sophisticated new drug development have pushed the production frontier, increasing the technical and productivity gains. The higher technical and R&D capabilities and wider new product portfolios of multinational companies also have contributed to the positive technical and productivity changes in the Indian pharmaceutical industry.     

基于复杂网络的金融风险跨市场传导机制研究——以金融危机时期(2007~2009年)数据为例

金融系统具有典型的非线性复杂系统的特征,其多层次和多重反馈特性使得金融风险跨市场传导效应更加复杂多变。选取2007~2009年金融危机时期的相关数据,构建金融网络,并采用最小生成树(MST)的方法对金融风险跨市场传导机制进行实证分析。结果表明:我国金融市场具有明显的小世界特征;金融危机期间金融市场内部各子市场间的关联程度显著加强;股票、债券、房地产和外汇市场是系统重要性市场,需要重点监控;对金融风险跨市场传导的潜在路径进行了识别,为宏观审慎监管提供了理论基础。     

基于遗传算法的上市公司财务危机预测模型研究

本文以我国沪深A股上市公司为研究对象,选取制造业公司376家,其中被特别处理的ST公司188家,与其配对的健康公司188家,使用遗传算法和21个财务比率建立了财务危机预测模型,并与Logistic回归和BP神经网络模型进行了比较,结果表明,使用遗传算法可以获得不受统计约束且预测准确率更高的模型。