Assessing bank soundness with classification techniques

The recent crisis highlighted, once again, the importance of early warning models to assess the soundness of individual banks. In the present study, we use six quantitative techniques originating from various disciplines to classify banks in three groups. The first group includes very strong and strong banks; the second one includes adequate banks, while the third group includes banks with weaknesses or serious problems. We compare models developed with financial variables only, with models that incorporate additional information in relation to the regulatory environment, institutional development, and macroeconomic conditions. The accuracy of classification of the models that include only financial variables is rather poor. We observe a substantial improvement in accuracy when we consider the country-level variables, with five out of the six models achieving out-of-sample classification accuracy above 70% on average. The models developed with multi-criteria decision aid and artificial neural networks achieve the highest accuracies. We also explore the development of stacked models that combine the predictions of the individual models at a higher level. While the stacked models outperform the corresponding individual models in most cases, we found no evidence that the best stacked model can outperform the best individual model.     

Big Data and Predictive Analytics and Manufacturing Performance: Integrating Institutional Theory,Resource‐Based View and Big Data Culture

The importance of big data and predictive analytics has been at the forefront of research for operations and manufacturing management. The literature has reported the influence of big data and predictive analytics for improved supply chain and operational performance, but there has been a paucity of literature regarding the role of external institutional pressures on the resources of the organization to build big data capability. To address this gap, this paper draws on the resource‐based view of the firm, institutional theory and organizational culture to develop and test a model that describes the importance of resources for building capabilities, skills and big data culture and subsequently improving cost and operational performance. We test our research hypotheses using 195 surveys, gathered using a pre‐tested questionnaire. Our contribution lies in providing insights regarding the role of external pressures on the selection of resources under the moderating effect of big data culture and their utilization for capability building, and how this capability affects cost and operational performance.     

Excluded by crisis management? Legislative hyperactivity in post-2015 Germany

This article examines the legislative measures taken by Germany since 2014 to manage the “refugee crisis” and analyses the potential effects on the rights and the well-being of asylum seekers and persons with protection needs at large. By taking a closer look at the reasoning given in the respective legislative documents we will show the link between the recent changes in the national asylum and migration law and the underlying migration management framework. The article concludes that Germany has seen a shift from a management to a securitization approach, the latter entailing a special emphasis on deportation (“return turn”) rather than focusing on accommodation and integration of those seeking protection. Acting in a mere “crisis mode”, the German legislator has implemented an exclusion paradigm that incrementally substituted the prior inclusive policies over time.     

Marginal and Conditional Distribution Estimation from Double‐sampled Semi‐competing Risks Data

Informative dropout is a vexing problem for any biomedical study. Most existing statistical methods attempt to correct estimation bias related to this phenomenon by specifying unverifiable assumptions about the dropout mechanism. We consider a cohort study in Africa that uses an outreach programme to ascertain the vital status for dropout subjects. These data can be used to identify a number of relevant distributions. However, as only a subset of dropout subjects were followed, vital status ascertainment was incomplete. We use semi‐competing risk methods as our analysis framework to address this specific case where the terminal event is incompletely ascertained and consider various procedures for estimating the marginal distribution of dropout and the marginal and conditional distributions of survival. We also consider model selection and estimation efficiency in our setting. Performance of the proposed methods is demonstrated via simulations, asymptotic study and analysis of the study data.     

An application of multicriteria decision aid models in the prediction of open market share repurchases

This study presents the first attempt to develop classification models for the prediction of share repurchase announcements using multicriteria decision aid (MCDA) techniques. We use three samples consisting of 434 UK firms, 330 French firms, and 296 German firms, to develop country-specific models. The MCDA techniques that are applied for the development of the models are the UTilités Additives DIScriminantes (UTADIS) and the ELimination and Choice Expressing REality (ELECTRE) TRI. We adopt a 10-fold cross validation approach, a re-sampling technique that allows us to split the datasets in training and validation sub-samples. Thus, at the first stage of the analysis the aim is the development of a model capable of reproducing the classification of the firms considered in the training samples. Once this stage is completed, the model can be used for the classification of new firms not included in the training samples (i.e. validation stage). The results show that both MCDA models achieve quite satisfactory classification accuracies in the validation sample and they outperform both logistic regression and chance predictions. The developed models could provide the basis for a decision tool for various stakeholders such as managers, shareholders, and investment analysts.     

Interculturalism in Romania: the metamorphosis of a post‐totalitarian society

This paper provides a succinct presentation of the cultural diversity present in Romania today. The author shall point out some of the causes and crises that have led to the emergence of various tensions relating to: rediscovering identity, dealing with the inertia of the past, clinging to former identities, exotic identity issues, and extensive cultural and political fragmentation. This paper also presents a discussion of the origins of diversity in Romania from historical, geographical, cultural‐religious, sociological, economic and political vantage points. Finally, the paper suggests several ways in which intercultural education can be promoted and developed in the Romanian context.     

What really works?! Evaluating the effectiveness of practices to increase the managerial diversity of women and minorities

Many studies have investigated barriers for women and minorities on their way to the top, but very few studies have examined which organizational practices indeed increase the diversity of the top management in organizations. This research analyzes whether various diversity practices increase the proportion of women and minorities at the top. The results show that the more diversity management and family-friendly practices that are implemented, the higher is the proportion of women in management positions. However, the results do not show any effect for diversity training or that people with a migration background benefit from diversity practices.