Cluster analysis to validate the sustainability label of stock indices: An analysis of the inclusion and exclusion processes in terms of size and ESG ratings

Sustainability stock indices play an important role in guiding socially responsible funds to their constituents. Thus, to find out whether the term sustainability is more than just a label, we analyze the inclusion and exclusion criteria applied by sustainability indices, and we compare them with those applied by conventional indices. We analyze the level of sustainability and size of the companies included in and excluded from five sustainability indices compared to a control group of 11 conventional indices. Our results show that the level of sustainability influences the inclusion process and, to a lesser extent, the exclusion process of the five FTSE4Good indices. However, we find similar results for several conventional indices. In addition, the size criterion dominates the sustainability criterion in the inclusion and exclusion processes of sustainability indices like in conventional indices. Further, we use different cluster algorithms to determine that the inclusion and exclusion processes of four of the five sustainability indices are different from those of the conventional indices. Our results validate the use of the “sustainability” label for four of five sustainability indices but also show that further differentiation between sustainability and conventional indices is needed

Credit risk prediction for small and medium enterprises utilizing adjacent enterprise data and a relational graph attention network

Credit risk prediction for small and medium enterprises (SMEs) has long posed a complex research challenge. Traditional approaches have primarily focused on enterprise-specific variables, but these models often prove inadequate when applied to SMEs with incomplete data. In this innovative study, we push the theoretical boundaries by leveraging data from adjacent enterprises to address the issue of data deficiency. Our strategy involves constructing an intricate network that interconnects enterprises based on shared managerial teams and business interactions. Within this network, we propose a novel relational graph attention network (RGAT) algorithm capable of capturing the inherent complexity in its topological information. By doing so, our model enhances financial service providers' ability to predict credit risk even in the face of incomplete data from target SMEs. Empirical experiments conducted using China's SMEs highlight the predictive proficiency and potential economic benefits of our proposed model. Our approach offers a comprehensive and nuanced perspective on credit risk while demonstrating the advantages of incorporating network-wide data in credit risk prediction

Patrones de comportamiento de clientes con tarjetas de crédito de consumo con deterioro de calificación por riesgo utilizando K-means

This document presents the analysis of the behavior of cardholders of a Colombian financial institution based on their credit risk rating through the application of the unsupervised machine learning model called K-means. Clusters of clients are obtained that allow identifying their behavior.En este documento se presenta el análisis del comportamiento de los clientes con tarjetas de crédito de una institución financiera colombiana con base en su calificación de riesgo de crédito, a través de la aplicación del modelo de ma­chine learning no supervisado denominado K-means. Se obtienen clústeres de clientes que permiten identificar sus patrones de comportamiento

Identification of credit risk based on cluster analysis of account behaviours

Assessment of risk levels for existing credit accounts isimportant to the implementation of bank policies and offeringfinancial products.This paper uses cluster analysis of be-haviour of credit card accounts to help assess credit risk level.Account behaviour is modelled parametrically and we thenimplement the behavioural cluster analysis using a recentlyproposed dissimilarity measure of statistical model parameters.The advantage of this new measure is the explicit exploitationof uncertainty associated with parameters estimated fromstatistical models.Interesting clusters of real credit cardbehaviours data are obtained, in addition to superior predictionand forecasting of account default based on the clusteringoutcomes