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Although stock price co-movement has been examined extensively, its causes are not well understood. Using a decomposition method, we extract three information components from the turnover rate: market information, firm-specific information, and investors' opinion divergence. We find that market information strengthens stock price co-movement, whereas firm-specific information weakens it. Moreover, our analysis shows that divergence of investors' opinion increases stock price variations but weakens price co-movement.

Since the opening of China (aka, reform and opening-up), a great number of rural residents have migrated to large cities in the past 40 years. Such a one-way population inflow to urban areas introduces nontrivial social conflicts between urban natives and migrant workers. This study aims to investigate the most discussed topics about migrant workers on Sina Weibo along with the corresponding sentiment divergence.

An exploratory-descriptive-explanatory research methodology is employed. The study explores the main topics on migrant workers discussed in social media via manual annotation. Subsequently, guided LDA, a semi-supervised topic modeling approach, is applied to describe the overall topical landscape. Finally, the authors verify their theoretical predictions with respect to the sentiment divergence pattern for each topic, using regression analysis.

The study identifies three most discussed topics on migrant workers, namely wage default, employment support and urban/rural development. The regression analysis reveals different diffusion patterns contingent on the nature of each topic. In particular, this study finds a positive association between urban/rural development and the sentiment divergence, while wage default exhibits an opposite relationship with sentiment divergence.

The authors combine unique characteristics of social media with well-established theories of social identity and framing, which are applied more to off-line contexts, to study a unique phenomenon of migrant workers in China. From a practical perspective, the results provide implications for the governance of urbanization-related social conflicts.

How do new professional service firms strategically position themselves in fields where developing a favourable external reputation is critical to performance? Are certain positioning strategies more effective than others? This study reveals that most professional service firm start-ups attempt to establish themselves by pursuing a strategy of moderate divergence from a field's institutionalized practices. Those that do so, however, do not perform as well as those that either conform more closely to these institutional prescriptions or depart more radically from them. In other words, balance beguiles but purism pays.

Several coefficients, called divergences, have been suggested in the statistical literature to reflect the fact that some probability distributions are “closer together” than others and consequently that it may be easier to distinguish between the distributions of one pair than between those of another. When comparing three biological populations, it is often interesting to measure how two of them “move apart” from the third. Deals with the statistical analysis of this problem by means of bivariate divergence statistics. Provides a unified study, depicting the behaviour and relative merits of traditional divergences, by using the (h,ø), divergence family of statistics introduced by Menéndez et al.

Due to stern management policies and increased community attentiveness, sustainable supply chain management (SSCM) performs a vast component in endeavor operation and production management. Sustainable circular supplier selection (SCSS) and evaluation presented the environmental and social concerns in the fields of circular economy and sustainable supplier selection. Choosing the optimal SCSS is vital for organizations to persuade SSCM, as specified in various researches. Based on the subjectivity of human behavior, the selection of ideal SCSS often involves uncertain information, and the Pythagorean fuzzy sets (PFSs) have a huge capability to tackle strong vagueness, uncertainty and inaccuracy in the multi-criteria decision-making (MCDM) procedure. Here, a framework is developed to assess and establish suitable suppliers in the SSCM and the circular economy.

This paper introduced an extended framework using the evaluation based on distance from average solution (EDAS) with PFSs and implemented it to solve the SCSS in the manufacturing sector. Firstly, the PFSs to handle the uncertain information of decision experts (DEs) is employed. Secondly, a novel divergence measure and parametric score function for calculating the criteria weights are proposed. Thirdly, an extended decision-making approach, known as PF-EDAS, is introduced.

The outcomes and comparative discussion show that the developed method is efficient and capable of facilitating the DEs to choose desirable SCSS. Therefore, the proposed framework can be used by organizations to assess and establish suitable suppliers in the SCSS process in the circular economy.

Selecting the optimal sustainable circular supplier (SCS) in the manufacturing sector is important for organizations to persuade SSCM, as specified in various research. However, corresponding to the subjectivity of human behavior, the selection of the best SCS often involves uncertain information, and the PFSs have a huge capability to tackle strong vagueness, uncertainty and inaccuracy in the MCDM procedure. Hence, manufacturing companies' administrators can implement the developed method to assess and establish suitable suppliers in the SCSS process in the circular economy.

On the basis of the f*‐Divergence for fuzzy information systems, this article presents a sequential selection method for a fixed number of fuzzy systems. f*‐Divergence is a measure of the quantity of information concerning the state space provided by the fuzzy system when the a priori probability distribution is defined on the space. The method described by the author determines a procedure which maximises the “Terminal” f*‐Divergence.

This paper aims to offer a tutorial/introduction to new statistics arising from the theory of optimal transport to empirical researchers in econometrics and machine learning.

Presenting in a tutorial/survey lecture style to help practitioners with the theoretical material.

The tutorial survey of some main statistical tools (arising from optimal transport theory) should help practitioners to understand the theoretical background in order to conduct empirical research meaningfully.

This study is an original presentation useful for new comers to the field.

Proposes a test of goodness‐of‐fit with composite null hypotheses and weights in the classes based on weighted (h,φ)‐divergences.

The weighted (h,φ)‐divergence between an empirical distribution and the probability of the estimated model is here investigated for large simple random samples.

The unknown parameters of the model are estimated using minimum (h,φ)‐divergences estimators with weights as studied in previous works by the authors.

Research makes an important contribution to (h,φ)‐divergences and their applications in statistical and other areas.

Weighted (h,φ) – divergence statistics are obtained by either replacing both distributions involved in the argument by their nonparametric estimators or replacing one distribution and considering the other as given. Asymptotic properties of weighted (h,φ) – divergence statistics are obtained and some tests constructed on the basis of these results are presented.

The purpose of this paper is to analyze how monetary fundamentals affect exchange rate movements.

To develop this paper, a Bayesian Network modeling is applied to explore the causal interactions between monetary fundamentals and exchange rate fluctuations. Subsequently, a sensitivity analysis is performed to asses and estimate exchange rate behavior with uncertain monetary fundamentals. Furthermore, a Granger Causality test is used as suggested in the Econometric literature to determine the causality direction among factors.

The empirical findings show that money supply and interest rate have a significant positive effect on exchange rate, whereas inflation rate has a considerable negative effect on exchange rate. In addition, the authors deduce that real income has an indirect impact on exchange rate and a direct impact on inflation rate, interest rate and money supply. Furthermore, the sensitivity analysis shows that monetary uncertainty has a considerable effect on exchange rate fluctuations. Moreover, the Granger Causality test reveals that there is a unique unidirectional causality running from money supply to exchange rate.

The model can be considered as a vital management tool for international investors and financial analysts to explore the effect of monetary fundamentals on exchange rate behavior. It allows estimating exchange rate fluctuations with uncertain monetary factors.

This study is the first one which applied a Bayesian Network modeling to examine the exchange rate determination problem. Results of this research are presented under a clear graphical representation that can be easily useful by monetary policymakers and international traders to determine the influential monetary factors on exchange rate behavior. Also, the model will help them in estimating the effect of monetary uncertainty on exchange rate fluctuations.