Dynamic Evolution of Cross-Correlations in the Chinese Stock Market

The analysis of cross-correlations is extensively applied for the understanding of interconnections in stock markets and the portfolio risk estimation. Current studies of correlations in Chinese market mainly focus on the static correlations between return series, and this calls for an urgent need to investigate their dynamic correlations. Our study aims to reveal the dynamic evolution of cross-correlations in the Chinese stock market, and offer an exact interpretation for the evolution behavior. The correlation matrices constructed from the return series of 367 A-share stocks traded on the Shanghai Stock Exchange from January 4, 1999 to December 30, 2011 are calculated over a moving window with a size of 400 days. The evolutions of the statistical properties of the correlation coefficients, eigenvalues, and eigenvectors of the correlation matrices are carefully analyzed. We find that the stock correlations are significantly increased in the periods of two market crashes in 2001 and 2008, during which only five eigenvalues significantly deviate from the random correlation matrix, and the systemic risk is higher in these volatile periods than calm periods. By investigating the significant contributors of the deviating eigenvectors in different time periods, we observe a dynamic evolution behavior in business sectors such as IT, electronics, and real estate, which lead the rise (drop) before (after) the crashes. Our results provide new perspectives for the understanding of the dynamic evolution of cross-correlations in the Chines stock markets, and the result of risk estimation is valuable for the application of risk management.     

Dominating clasp of the financial sector revealed by partial correlation analysis of the stock market

What are the dominant stocks which drive the correlations present among stocks traded in a stock market? Can a correlation analysis provide an answer to this question? In the past, correlation based networks have been proposed as a tool to uncover the underlying backbone of the market. Correlation based networks represent the stocks and their relationships, which are then investigated using different network theory methodologies. Here we introduce a new concept to tackle the above question—the partial correlation network. Partial correlation is a measure of how the correlation between two variables, e.g., stock returns, is affected by a third variable. By using it we define a proxy of stock influence, which is then used to construct partial correlation networks. The empirical part of this study is performed on a specific financial system, namely the set of 300 highly capitalized stocks traded at the New York Stock Exchange, in the time period 2001–2003. By constructing the partial correlation network, unlike the case of standard correlation based networks, we find that stocks belonging to the financial sector and, in particular, to the investment services sub-sector, are the most influential stocks affecting the correlation profile of the system. Using a moving window analysis, we find that the strong influence of the financial stocks is conserved across time for the investigated trading period. Our findings shed a new light on the underlying mechanisms and driving forces controlling the correlation profile observed in a financial market.     

Analyzing the Long Term Cohesive Effect of Sector Specific Driving Forces

Financial markets are partially composed of sectors dominated by external driving forces, such as commodity prices, infrastructure and other indices. We characterize the statistical properties of such sectors and present a novel model for the coupling of the stock prices and their dominating driving forces, inspired by mean reverting stochastic processes. Using the model we were able to explain the market sectors’ long term behavior and estimate the coupling strength between stocks in financial markets and the sector specific driving forces. Notably, the analysis was successfully applied to the shipping market, in which the Baltic dry index (BDI), an assessment of the price of transporting the major raw materials by sea, influences the shipping financial market. We also present the analysis of other sectors—the gold mining market and the food production market, for which the model was also successfully applied. The model can serve as a general tool for characterizing the coupling between external forces and affected financial variables and therefore for estimating the risk in sectors and their vulnerability to external stress.     

Protein Sectors: Statistical Coupling Analysis versus Conservation

Statistical coupling analysis (SCA) is a method for analyzing multiple sequence alignments that was used to identify groups of coevolving residues termed “sectors”. The method applies spectral analysis to a matrix obtained by combining correlation information with sequence conservation. It has been asserted that the protein sectors identified by SCA are functionally significant, with different sectors controlling different biochemical properties of the protein. Here we reconsider the available experimental data and note that it involves almost exclusively proteins with a single sector. We show that in this case sequence conservation is the dominating factor in SCA, and can alone be used to make statistically equivalent functional predictions. Therefore, we suggest shifting the experimental focus to proteins for which SCA identifies several sectors. Correlations in protein alignments, which have been shown to be informative in a number of independent studies, would then be less dominated by sequence conservation.     

Decrypting Financial Markets through E-Joint Attention Efforts: On-Line Adaptive Networks of Investors in Periods of Market Uncertainty

This paper looks at 800,000 messages on the Unicredit stock, exchanged by 7,500 investors in the Finanzaonline.com forum, between 2005 and 2012 and measured collective interpretations of stock market trends. We examined the correlation patterns between market uncertainty, bad news and investors'' network structure by measuring the investors'' communication patterns. Our results showed that the investors'' network reacted to market trends in different ways: While less turbulent market phases implied less communication, higher market volatility generated more complex communication patterns. While the information content of messages was less technical in situations of uncertainty, bad news caused more informative messages only when market volatility was lower. This meant that bad news had a different impact on network behaviour, depending on market uncertainty. By measuring the investors'' expertise, we found that their behaviour could help predict changes in daily stock returns. We also found that expert investors were more influential in communication processes during high volatility market phases, whereas they had less influence on the real-time forum''s reaction after bad news. Our findings confirm the crucial role of e-communication platforms. However, they also show the need to reconsider the fragility of these collective intelligence systems when under external shocks.     

Bioprocessing in the Digital Age: The Role of Process Models

In this age of technology, the vision of manufacturing industries built of smart factories is not a farfetched future. As a prerequisite for Industry 4.0, industrial sectors are moving towards digitalization and automation. Despite its tremendous growth reaching a sales value of worth $188 billion in 2017, the biopharmaceutical sector distinctly lags in this transition. Currently, the challenges are innovative market disruptions such as personalized medicine as well as increasing commercial pressure for faster and cheaper product manufacturing. Improvements in digitalization and data analytics have been identified as key strategic activities for the next years to face these challenges. Alongside, there is an emphasis by the regulatory authorities on the use of advanced technologies, proclaimed through initiatives such as Quality by Design (QbD) and Process Analytical Technology (PAT). In the manufacturing sector, the biopharmaceutical domain features some of the most complex and least understood processes. Thereby, process models that can transform process data into more valuable information, guide decision‐making, and support the creation of digital and automated technologies are key enablers. This review summarizes the current state of model‐based methods in different bioprocess related applications and presents the corresponding future vision for the biopharmaceutical industry to achieve the goals of Industry 4.0 while meeting the regulatory requirements.     

Index cohesive force analysis reveals that the US market became prone to systemic collapses since 2002

Background

The 2007–2009 financial crisis, and its fallout, has strongly emphasized the need to define new ways and measures to study and assess the stock market dynamics.

Methodology/Principal Findings

The S&P500 dynamics during 4/1999–4/2010 is investigated in terms of the index cohesive force (ICF - the balance between the stock correlations and the partial correlations after subtraction of the index contribution), and the Eigenvalue entropy of the stock correlation matrices. We found a rapid market transition at the end of 2001 from a flexible state of low ICF into a stiff (nonflexible) state of high ICF that is prone to market systemic collapses. The stiff state is also marked by strong effect of the market index on the stock-stock correlations as well as bursts of high stock correlations reminiscence of epileptic brain activity.

Conclusions/Significance

The market dynamical states, stability and transition between economic states was studies using new quantitative measures. Doing so shed new light on the origin and nature of the current crisis. The new approach is likely to be applicable to other classes of complex systems from gene networks to the human brain.     

Early steps in assembly of the yeast vacuolar H+-ATPase.

Vacuolar proton-translocating ATPases are composed of a complex of integral membrane proteins, the Vo sector, attached to a complex of peripheral membrane proteins, the V1 sector. We have examined the early steps in biosynthesis of the yeast vacuolar ATPase by biosynthetically labeling wild-type and mutant cells for varied pulse and chase times and immunoprecipitating fully and partially assembled complexes under nondenaturing conditions. In wild-type cells, several V1 subunits and the 100-kDa Vo subunit associate within 3-5 min, followed by addition of other Vo subunits with time. Deletion mutants lacking single subunits of the enzyme show a variety of partial complexes, including both complexes that resemble intermediates in the assembly pathway of wild-type cells and independent V1 and Vo sectors that form without any apparent V1Vo subunit interaction. Two yeast sec mutants that show a temperature-conditional block in export from the endoplasmic reticulum accumulate a complex containing several V1 subunits and the 100-kDa Vo subunit during incubation at elevated temperature. This complex can assemble with the 17-kDa Vo subunit when the temperature block is reversed. We propose that assembly of the yeast V-ATPase can occur by two different pathways: a concerted assembly pathway involving early interactions between V1 and Vo subunits and an independent assembly pathway requiring full assembly of V1 and Vo sectors before combination of the two sectors. The data suggest that in wild-type cells, assembly occurs predominantly by the concerted assembly pathway, and V-ATPase complexes acquire the full complement of Vo subunits during or after exit from the endoplasmic reticulum.     

The hybrid economy and anthropological engagements with policy discourse: A brief reflection1

This article advocates a crucial role for economic anthropology in the twenty‐first century. The use of anthropological techniques for primary data collection is essential for understanding the complexity of diverse local economies. This is demonstrated with reference to a remote Aboriginal economy in Arnhem Land, Northern Australia, using a ‘hybrid economy’ model that includes the customary sector as well as market and state sectors. This empirically grounded model is contrasted with a very different theoretical construct: the ‘real’ economy that is dominating Indigenous affairs policy discourse. Although the hybrid economy model is currently subordinated, mainly for ideological reasons, examples are provided to demonstrate its policy and legal influences.     

Complex interactions between pre‐spawning water level increase,trophic state and spawning stock biomass determine year‐class strength in a shallow‐water‐spawning fish

Pre‐spawning water level increase (PWLI) is a recently discovered parameter of water level dynamics affecting juvenile year‐class strength (YCS) in shallow‐water‐spawning fish. By analysing a time series of commercial common bream (Abramis brama) yields in Lake Constance from 1950 through 2007, this study showed that the differences in juvenile YCS are conserved until the adult life stage. Adult YCS was best explained by complex interactions of PWLI with both stock‐intrinsic and extrinsic environmental variables. The correlation between PWLI and YCS of adult bream became more pronounced as the trophic state of the lake increased. It is argued that this mediator effect of the trophic state results from increased growth of the algal biofilms during high trophic state periods. These biofilms are known to impair safe attachment of the eggs to the substratum and affect mortality rates of the eggs. Furthermore, reproductive stock size exhibited a positive effect on the resulting YCS. However, a marginally significant interaction between reproductive stock size and PWLI indicates that the two positive effects of PWLI and reproductive stock size on YCS were not fully additive, probably because the very large year‐classes resulting from the combined positive effects suffered from strong intra‐specific competition. This study demonstrates that anthropogenic water level regulation, e.g. for flood protection or for the generation of hydroelectric power, and climate change altering PWLI have the potential to affect YCS throughout the whole life cycle of bream, particularly in eutrophic water bodies. Similar effects of PWLI are anticipated in other shallow water spawning species.     

E‐Commerce Effects on Energy Consumption: A Multi‐Year Ecosystem‐Level Assessment

This research investigates the impact of e‐commerce on energy consumption in all four sectors of the U.S. economy (commercial, industrial, residential, and transportation), using macroeconomic data from 1992 to 2015. These data capture all the development phases of e‐commerce, as well as direct and rebound effects in and across sectors. Empirical dynamic models (EDMs), a novel methodology in industrial ecology, are applied to the e‐commerce/energy relationship to accommodate for complex system behavior and state‐dependent effects. The results of these data‐driven methods suggest that e‐commerce increases energy consumption mainly through increases in the residential and commercial sectors. These findings contrast with extant research that focuses on transportation effects, which appear less prominent in this investigation. E‐commerce effects also demonstrate state dependence, varying over the magnitude of e‐commerce as a percentage of the total retail sector, particularly in commercial and transportation realms. Assuming these effects will continue in the future, the findings imply that policy makers should focus on mitigating the environmentally deteriorating effects of e‐commerce in the residential sector. However, this investigation cannot provide root causes for the uncovered e‐commerce effects. Robustness of the empirical findings, limitations of the novel EDM methodology, and respective avenues for future methodological and substantial research are discussed.     

Considering land–sea interactions and trade‐offs for food and biodiversity

With the human population expected to near 10 billion by 2050, and diets shifting towards greater per‐capita consumption of animal protein, meeting future food demands will place ever‐growing burdens on natural resources and those dependent on them. Solutions proposed to increase the sustainability of agriculture, aquaculture, and capture fisheries have typically approached development from single sector perspectives. Recent work highlights the importance of recognising links among food sectors, and the challenge cross‐sector dependencies create for sustainable food production. Yet without understanding the full suite of interactions between food systems on land and sea, development in one sector may result in unanticipated trade‐offs in another. We review the interactions between terrestrial and aquatic food systems. We show that most of the studied land–sea interactions fall into at least one of four categories: ecosystem connectivity, feed interdependencies, livelihood interactions, and climate feedback. Critically, these interactions modify nutrient flows, and the partitioning of natural resource use between land and sea, amid a backdrop of climate variability and change that reaches across all sectors. Addressing counter‐productive trade‐offs resulting from land‐sea links will require simultaneous improvements in food production and consumption efficiency, while creating more sustainable feed products for fish and livestock. Food security research and policy also needs to better integrate aquatic and terrestrial production to anticipate how cross‐sector interactions could transmit change across ecosystem and governance boundaries into the future.     

Discovering amino acid patterns on binding sites in protein complexes

Discovering amino acid (AA) patterns on protein binding sites has recently become popular. We propose a method to discover the association relationship among AAs on binding sites. Such knowledge of binding sites is very helpful in predicting protein-protein interactions. In this paper, we focus on protein complexes which have protein-protein recognition. The association rule mining technique is used to discover geographically adjacent amino acids on a binding site of a protein complex. When mining, instead of treating all AAs of binding sites as a transaction, we geographically partition AAs of binding sites in a protein complex. AAs in a partition are treated as a transaction. For the partition process, AAs on a binding site are projected from three-dimensional to two-dimensional. And then, assisted with a circular grid, AAs on the binding site are placed into grid cells. A circular grid has ten rings: a central ring, the second ring with 6 sectors, the third ring with 12 sectors, and later rings are added to four sectors in order. As for the radius of each ring, we examined the complexes and found that 10Å is a suitable range, which can be set by the user. After placing these recognition complexes on the circular grid, we obtain mining records (i.e. transactions) from each sector. A sector is regarded as a record. Finally, we use the association rule to mine these records for frequent AA patterns. If the support of an AA pattern is larger than the predetermined minimum support (i.e. threshold), it is called a frequent pattern. With these discovered patterns, we offer the biologists a novel point of view, which will improve the prediction accuracy of protein-protein recognition. In our experiments, we produced the AA patterns by data mining. As a result, we found that arginine (arg) most frequently appears on the binding sites of two proteins in the recognition protein complexes, while cysteine (cys) appears the fewest. In addition, if we discriminate the shape of binding sites between concave and convex further, we discover that patterns {arg, glu, asp} and {arg, ser, asp} on the concave shape of binding sites in a protein more frequently (i.e. higher probability) make contact with {lys} or {arg} on the convex shape of binding sites in another protein. Thus, we can confidently achieve a rate of at least 78%. On the other hand {val, gly, lys} on the convex surface of binding sites in proteins is more frequently in contact with {asp} on the concave site of another protein, and the confidence achieved is over 81%. Applying data mining in biology can reveal more facts that may otherwise be ignored or not easily discovered by the naked eye. Furthermore, we can discover more relationships among AAs on binding sites by appropriately rotating these residues on binding sites from a three-dimension to two-dimension perspective. We designed a circular grid to deposit the data, which total to 463 records consisting of AAs. Then we used the association rules to mine these records for discovering relationships. The proposed method in this paper provides an insight into the characteristics of binding sites for recognition complexes.     

Estimation of covariance matrix of multivariate longitudinal data using modified Choleksky and hypersphere decompositions

Linear models are typically used to analyze multivariate longitudinal data. With these models, estimating the covariance matrix is not easy because the covariance matrix should account for complex correlated structures: the correlation between responses at each time point, the correlation within separate responses over time, and the cross-correlation between different responses at different times. In addition, the estimated covariance matrix should satisfy the positive definiteness condition, and it may be heteroscedastic. However, in practice, the structure of the covariance matrix is assumed to be homoscedastic and highly parsimonious, such as exchangeable or autoregressive with order one. These assumptions are too strong and result in inefficient estimates of the effects of covariates. Several studies have been conducted to solve these restrictions using modified Cholesky decomposition (MCD) and linear covariance models. However, modeling the correlation between responses at each time point is not easy because there is no natural ordering of the responses. In this paper, we use MCD and hypersphere decomposition to model the complex correlation structures for multivariate longitudinal data. We observe that the estimated covariance matrix using the decompositions is positive-definite and can be heteroscedastic and that it is also interpretable. The proposed methods are illustrated using data from a nonalcoholic fatty liver disease study.     

Impact of Stock Market Structure on Intertrade Time and Price Dynamics

We analyse times between consecutive transactions for a diverse group of stocks registered on the NYSE and NASDAQ markets, and we relate the dynamical properties of the intertrade times with those of the corresponding price fluctuations. We report that market structure strongly impacts the scale-invariant temporal organisation in the transaction timing of stocks, which we have observed to have long-range power-law correlations. Specifically, we find that, compared to NYSE stocks, stocks registered on the NASDAQ exhibit significantly stronger correlations in their transaction timing on scales within a trading day. Further, we find that companies that transfer from the NASDAQ to the NYSE show a reduction in the correlation strength of transaction timing on scales within a trading day, indicating influences of market structure. We also report a persistent decrease in correlation strength of intertrade times with increasing average intertrade time and with corresponding decrease in companies'' market capitalization–a trend which is less pronounced for NASDAQ stocks. Surprisingly, we observe that stronger power-law correlations in intertrade times are coupled with stronger power-law correlations in absolute price returns and higher price volatility, suggesting a strong link between the dynamical properties of intertrade times and the corresponding price fluctuations over a broad range of time scales. Comparing the NYSE and NASDAQ markets, we demonstrate that the stronger correlations we find in intertrade times for NASDAQ stocks are associated with stronger correlations in absolute price returns and with higher volatility, suggesting that market structure may affect price behavior through information contained in transaction timing. These findings do not support the hypothesis of universal scaling behavior in stock dynamics that is independent of company characteristics and stock market structure. Further, our results have implications for utilising transaction timing patterns in price prediction and risk management optimization on different stock markets.     

Rural Poor Economies and Foreign Investors: An Opportunity or a Risk?

In the current age of commercial and financial openness, remote and poor local economies are becoming increasingly exposed to inflows of external capital. The new investors - enjoying lower credit constraints than local dwellers - might play a propulsive role in local development. At the same time, inflows of external capital can have negative impacts on local natural resource-dependent activities. We analyze a two-sector model where both sectors damage the environment, but only that of domestic producers relies on natural resources. We assess under which conditions the coexistence of the two sectors is compatible with sustainability, defined as convergence to a stationary state characterized by a positive stock of the natural resource. Moreover, we find that capital inflows can be stimulated by an increase in the pollution intensity of incoming activities, but also in the pollution intensity of the domestic sector; in both cases, capital inflows generate environmental degradation and a decrease in welfare for the local population. Finally, we show that a reduction in the cost of capital for external investors and the consequent capital inflows have the effect to increase wages, local investments and welfare of the local populations only if the environmental impact of the external sector is relatively low with respect to that of local activities. Otherwise, an unexpected scenario characterized by a reduction in domestic capital accumulation and the impoverishment of local agents can occur.     

Influence of the Time Scale on the Construction of Financial Networks

Background

In this paper we investigate the definition and formation of financial networks. Specifically, we study the influence of the time scale on their construction.

Methodology/Principal Findings

For our analysis we use correlation-based networks obtained from the daily closing prices of stock market data. More precisely, we use the stocks that currently comprise the Dow Jones Industrial Average (DJIA) and estimate financial networks where nodes correspond to stocks and edges correspond to none vanishing correlation coefficients. That means only if a correlation coefficient is statistically significant different from zero, we include an edge in the network. This construction procedure results in unweighted, undirected networks. By separating the time series of stock prices in non-overlapping intervals, we obtain one network per interval. The length of these intervals corresponds to the time scale of the data, whose influence on the construction of the networks will be studied in this paper.

Conclusions/Significance

Numerical analysis of four different measures in dependence on the time scale for the construction of networks allows us to gain insights about the intrinsic time scale of the stock market with respect to a meaningful graph-theoretical analysis.     

基于假设抽取法的黑河流域中游行业用水关联分析

基于2000—2012年张掖市混合型水资源投入产出模型,运用改进的假设抽取法,分析了张掖市6部门水资源关联效应,揭示了水资源在行业间的消耗规律,为调整产业结构提供有利的参考。研究结果表明:(1)各部门水资源直接消耗量与满足自身所需的水资源量不对等,种植业的水资源直接消耗量和纵向集成消耗量均为最大,且其纵向集成消耗水量小于直接消耗量,是张掖市经济系统中真正的水资源净输出部门。(2)种植业的内部效应和复合效应均最大,对自身的依赖性极强。服务业的净后项关联最大,对其他部门的依赖程度最高。(3)水资源在各部门之间发生了转移,种植业是张掖市经济系统中最大的水资源供给者,服务业是各部门中最大的受水者,通过中间投入的方式,由种植业到服务业的路径是最大的水转移途径,而建筑业是"纯"输入部门。(4)2000—2012年间,各部门的内部效应、复合效应、净前项关联和净后项关联均变化显著,进一步反映了产业部门水资源利用的动态关联。     

Unraveling Hidden Order in the Dynamics of Developed and Emerging Markets

The characterization of asset price returns is an important subject in modern finance. Traditionally, the dynamics of stock returns are assumed to lack any temporal order. Here we present an analysis of the autocovariance of stock market indices and unravel temporal order in several major stock markets. We also demonstrate a fundamental difference between developed and emerging markets in the past decade - emerging markets are marked by positive order in contrast to developed markets whose dynamics are marked by weakly negative order. In addition, the reaction to financial crises was found to be reversed among developed and emerging markets, presenting large positive/negative autocovariance spikes following the onset of these crises. Notably, the Chinese market shows neutral or no order while being regarded as an emerging market. These findings show that despite the coupling between international markets and global trading, major differences exist between different markets, and demonstrate that the autocovariance of markets is correlated with their stability, as well as with their state of development.