Abstract
Fierce competition as well as the recent financial crisis in financial and banking industries made credit scoring gain importance. An accurate estimation of credit risk helps organizations to decide whether or not to grant credit to potential customers. Many classification methods have been suggested to handle this problem in the literature. This paper proposes a model for evaluating credit risk based on binary quantile regression, using Bayesian estimation. This paper points out the distinct advantages of the latter approach: that is (i) the method provides accurate predictions of which customers may default in the future, (ii) the approach provides detailed insight into the effects of the explanatory variables on the probability of default, and (iii) the methodology is ideally suited to build a segmentation scheme of the customers in terms of risk of default and the corresponding uncertainty about the prediction. An often studied dataset from a German bank is used to show the applicability of the method proposed. The results demonstrate that the methodology can be an important tool for credit companies that want to take the credit risk of their customer fully into account.

Abstract

Abstract
This paper proposes a new model to predict company failure in the construction industry. The model includes three major innovative aspects. The use of strategic variables reflecting the key specificities of construction companies, which are critical to explain company failure. The use of data mining techniques, i.e. support vector machine to predict company failure. The use of two different sampling methods (random undersampling and random oversampling with replacement) to balance class distributions. The model proposed was empirically tested using all Portuguese contractors that operated in 2009. It is concluded that support vector machine, with random oversampling and including strategic variables, is a very robust tool to predict company failure in the context of the construction industry. In particular, this model outperforms the results obtained with logistic regression.

Abstract
This paper presents an exploratory study to assess the efficiency level of construction companies worldwide, exploring in particular the effect of location and activity in the efficiency levels. This paper also provides insights concerning the convergence in efficiency across regions. The companies are divided in three regions (Europe, Asia and North America), and in the three main construction activities (Buildings, Heavy Civil and Specialty Trade). We analyze a sample of 118 companies worldwide between 1995 and 2003. Data envelopment analysis is used to estimate efficiency, and the Malmquist index is applied for the evaluation of productivity change. Both methods were complemented by bootstrapping to refine the estimates obtained. A panel data truncated regression with categorical regressors is used to explore the impact of location and activity in the efficiency levels. The results reveal that the efficiency of North American companies is higher than the European and Asian counterparts. Other important conclusion points to a convergence in efficiency levels across regions as in North America productivity remains stable, whereas in Asia and Europe productivity improves.

Abstract
This paper presents a framework to facilitate the selection of the most appropriate company to be contracted among competitive bids. This framework is intended to be integrated in e-marketplaces to comply with the major technological advances in the construction industry. A novel feature of the system is that it allows bilateral evaluations between companies to better understand general contractor-subcontractor relationships and to improve the level of transparency within the construction sector. The performance assessment system incorporates other innovative features, such as the ability to specify a set of performance indicators suitable for inclusion in e-marketplaces covering three different perspectives: company reliability, operation performance, and bid attributes. The system also allows the integration of the preferences of the decision maker concerning the selection of the best company for a given work. (C) 2013 American Society of Civil Engineers.

Abstract
Among the Portuguese artisanal fishing fleets, the bivalve dredge fleet is one of the most profitable. In the last decade, after the implementation of a quotas system, the management of this fishery has been largely focused on adjusting catch to the conservation status of the resources exploited. The present work aims to understand how changes in the amount of quota attributed to each vessel each year and shifts in the quota regime affected vessel productivity. Boostrapped Malmquist indices, complemented with an efficiency assessment using a directional distance function, were used to quantify productivity changes between 1999 and 2011 for the fleets operating in two areas along the Portuguese coast (northwest and southwest). The results showed that the implementation of a weekly quota, as opposed to a daily quota, led to a significant improvement in productivity. This was mainly due to the decrease in fishing days and fuel consumption. It is predicted that the implementation of weekly quotas in the south area would lead to an overall reduction of about 12% in fishing days and fuel consumption, even though the variation in fuel consumption may be affected by the status of the resources. The results achieved provide important insights for future management actions and showed the potential advantages of applying this type of management to other fisheries worldwide, mainly those using active gear.