Synergetic Application of Multi-Criteria Decision-Making Models to Credit Granting Decision Problems

Although various algorithms have widely been studied for bankruptcy and credit risk prediction, conclusions regarding the best performing method are divergent when using different performance assessment metrics. As a solution to this problem, the present paper suggests the employment of two well-known multiple-criteria decision-making (MCDM) techniques by integrating their preference scores, which can constitute a valuable tool for decision-makers and analysts to choose the prediction model(s) more properly. Thus, selection of the most suitable algorithm will be designed as an MCDM problem that consists of a finite number of performance metrics (criteria) and a finite number of classifiers (alternatives). An experimental study will be performed to provide a more comprehensive assessment regarding the behavior of ten classifiers over credit data evaluated with seven different measures, whereas the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Preference Ranking Organization METHod for Enrichment of Evaluations (PROMETHEE) techniques will be applied to rank the classifiers. The results demonstrate that evaluating the performance with a unique measure may lead to wrong conclusions, while the MCDM methods may give rise to a more consistent analysis. Furthermore, the use of MCDM methods allows the analysts to weight the significance of each performance metric based on the intrinsic characteristics of a given credit granting decision problem

Prácticas organizativas saludables frente a la violencia en el trabajo. Estudio de su incidencia

Violence at work is a growing problem for organizations. It involves significant costs for the organization, its members and the community. In its various dimensions, organized violence is one of the least investigated. This study provides evidence of the relevance of this dimension has on the development of violent behavior in the workplace. The results indicate that practices an organization implements an impact on levels of violence that occur at work. For the development of healthy organizations, free of violence, the company management must take a holistic approach and look at best practices related to human resource management, with leadership factors or job design

A literature review on the application of evolutionary computing to credit scoring

The last years have seen the development of many credit scoring models for assessing the creditworthiness of loan applicants. Traditional credit scoring methodology has involved the use of statistical and mathematical programming techniques such as discriminant analysis, linear and logistic regression, linear and quadratic programming, or decision trees. However, the importance of credit grant decisions for financial institutions has caused growing interest in using a variety of computational intelligence techniques. This paper concentrates on evolutionary computing, which is viewed as one of the most promising paradigms of computational intelligence. Taking into account the synergistic relationship between the communities of Economics and Computer Science, the aim of this paper is to summarize the most recent developments in the application of evolutionary algorithms to credit scoring by means of a thorough review of scientific articles published during the period 2000–2012.This work has partially been supported by the Spanish Ministry of Education and Science under grant TIN2009-14205 and the Generalitat Valenciana under grant PROMETEO/2010/028

An insight into the experimental design for credit risk and corporate bankruptcy prediction systems

Over the last years, it has been observed an increasing interest of the finance and business communities in any application tool related to the prediction of credit and bankruptcy risk, probably due to the need of more robust decision-making systems capable of managing and analyzing complex data. As a result, plentiful techniques have been developed with the aim of producing accurate prediction models that are able to tackle these issues. However, the design of experiments to assess and compare these models has attracted little attention so far, even though it plays an important role in validating and supporting the theoretical evidence of performance. The experimental design should be done carefully for the results to hold significance; otherwise, it might be a potential source of misleading and contradictory conclusions about the benefits of using a particular prediction system. In this work, we review more than 140 papers published in refereed journals within the period 2000–2013, putting the emphasis on the bases of the experimental design in credit scoring and bankruptcy prediction applications. We provide some caveats and guidelines for the usage of databases, data splitting methods, performance evaluation metrics and hypothesis testing procedures in order to converge on a systematic, consistent validation standard.This work has partially been supported by the Mexican Science and Technology Council (CONACYT-Mexico) through a Postdoctoral Fellowship [223351], the Spanish Ministry of Economy under grant TIN2013-46522-P and the Generalitat Valenciana under grant PROMETEOII/2014/062

El cambiante mundo de las organizaciones. Hacia una organización saludable

Nuestro modelo actual de sociedad se halla inmerso en un profundo cambio (re-evolución), que nos está conduciendo hacia un mundo «enredado». Las organizaciones, como subsistemas fundamentales de esa sociedad, tienen el reto de digerir el complejo conjunto de transformaciones que estamos viviendo. Uno de los patrones que están emergiendo en las organizaciones, como respuesta a este proceso de cambio, es el de la «salud en la organización». El desarrollo de este patrón se concreta en el constructo «organizaciones saludables», que se caracterizan por promover y potenciar la sinergia positiva entre su propio crecimiento, el de sus miembros y el de la comunidad. La construcción de este tipo de organizaciones se ha convertido en uno de los desafíos del mundo organizacional y, por ende, de la propia sociedad. Su viabilidad y concreción pueden determinar, en buena medida, el modelo social del futuro a medio y largo plazo. El estado de la cuestión es aún demasiado disperso. Se requiere formular un marco referencial que permita orientar el desarrollo de este tipo de organizaciones; un marco que, a su vez, nos ofrezca una conceptualización adecuada para, posteriormente, plantear el desarrollo progresivo de las configuraciones organizativas que posibiliten la evolución íntegra y holística de la salud de la organización, de sus miembros y de la comunidad. Partiendo de la revisión sistemática de información, el presente trabajo ofrece una primera aproximación configuracional en este camino.Our present model of society is engaged in a profound change (re-evolution) that is directing us towards a «networked world». Organizations, as fundamental subsystems of this society, have to cope with the challenge of managing the complex transformation frameset we are experiencing.One of the patterns that is emerging in response to this changing process is health in organization. The development of this pattern draws closer to the construct of healthy organizations, which promote and empower the positive synergy between their own growth, that of their members and that of the community. Building this type of organization has become one of the challenges of the organizational world and, therefore, of society itself. A great deal of the future social model in the mid and long term may depend on their feasibility and concreteness. The state of the art is still too fragmented. A reference framework needs to be formulated that enables these kinds of organization to be developed. The framework should also conceptualize the organization and then progressively develop organizational configurations that lead to the comprehensive and holistic evolution of the health of the organization, its members and the community. On the basis of a systematic review of information, the present paper uses a configurational approach to make an initial proposal

Financial distress prediction using the hybrid associative memory with translation

This paper presents an alternative technique for financial distress prediction systems. The method is based on a type of neural network, which is called hybrid associative memory with translation. While many different neural network architectures have successfully been used to predict credit risk and corporate failure, the power of associative memories for financial decision-making has not been explored in any depth as yet. The performance of the hybrid associative memory with translation is compared to four traditional neural networks, a support vector machine and a logistic regression model in terms of their prediction capabilities. The experimental results over nine real-life data sets show that the associative memory here proposed constitutes an appropriate solution for bankruptcy and credit risk prediction, performing significantly better than the rest of models under class imbalance and data overlapping conditions in terms of the true positive rate and the geometric mean of true positive and true negative rates.This work has partially been supported by the Mexican CONACYT through the Postdoctoral Fellowship Program [232167], the Spanish Ministry of Economy [TIN2013-46522-P], the Generalitat Valenciana [PROMETEOII/2014/062] and the Mexican PRODEP [DSA/103.5/15/7004]. We would like to thank the Reviewers for their valuable comments and suggestions, which have helped to improve the quality of this paper substantially

Model Selection for Financial Distress Prediction by Aggregating TOPSIS and PROMETHEE Rankings

Ponencia presentada al 11th International Conference, HAIS 2016, Seville, Spain, April 18-20, 2016Many models have been explored for financial distress prediction, but no consistent conclusions have been drawn on which method shows the best behavior when different performance evaluation measures are employed. Accordingly, this paper proposes the integration of the ranking scores given by two popular multiple-criteria decision-making tools as an important step to help decision makers in selecting the model(s) properly. Selection of the most appropriate prediction method is here shaped as a multiple-criteria decision-making problem that involves a number of performance measures (criteria) and a set of techniques (alternatives). An empirical study is carried out to assess the performance of ten algorithms over six real-life bankruptcy and credit risk databases. The results reveal that the use of a unique performance measure often leads to contradictory conclusions, while the multiple-criteria decision-making techniques may yield a more reliable analysis. Besides, these allow the decision makers to weight the relevance of the individual performance metrics as a function of each particular problem.This work has partially been supported by the Spanish Ministry of Economy [TIN2013-46522-P], the Generalitat Valenciana [PROMETEOII/2014/062], the Mexican PRODEP [DSA/103.5/15/7004] and the Mexican Science and Technology Council through the Postdoctoral Fellowship Program [232167]

Understanding the apparent superiority of over-sampling through an analysis of local information for class-imbalanced data

Data plays a key role in the design of expert and intelligent systems and therefore, data preprocessing appears to be a critical step to produce high-quality data and build accurate machine learning models. Over the past decades, increasing attention has been paid towards the issue of class imbalance and this is now a research hotspot in a variety of fields. Although the resampling methods, either by under-sampling the majority class or by over-sampling the minority class, stand among the most powerful techniques to face this problem, their strengths and weaknesses have typically been discussed based only on the class imbalance ratio. However, several questions remain open and need further exploration. For instance, the subtle differences in performance between the over- and under-sampling algorithms are still under-comprehended, and we hypothesize that they could be better explained by analyzing the inner structure of the data sets. Consequently, this paper attempts to investigate and illustrate the effects of the resampling methods on the inner structure of a data set by exploiting local neighborhood information, identifying the sample types in both classes and analyzing their distribution in each resampled set. Experimental results indicate that the resampling methods that produce the highest proportion of safe samples and the lowest proportion of unsafe samples correspond to those with the highest overall performance. The significance of this paper lies in the fact that our findings may contribute to gain a better understanding of how these techniques perform on class-imbalanced data and why over-sampling has been reported to be usually more efficient than under-sampling. The outcomes in this study may have impact on both research and practice in the design of expert and intelligent systems since a priori knowledge about the internal structure of the imbalanced data sets could be incorporated to the learning algorithms

Implementation of a project-based learning to the coordination of subjects in the Agrifood and Rural Engineering Bachelor

The Bachelor’s Degree in Agrifood and Rural Engineering at Universitat Jaume I of Castelló has implemented in the second academic year a multidisciplinary project using a Project-Based Learning as the teaching method. Its final purpose is the acquisition of skills that should help the students to cope with their future career. This teaching-learning system has been used for three consecutive years since the degree was firstly implemented. Once a particular farm is assigned, the students are organized in groups and must fulfill their assigned tasks in a collaborative manner with the final goal of developing a project on that farm including viable improvements of the exploitation, taking into account the issues related to the different subjects involved. This work presents the results obtained along the three years, analyzed from two different points of view: student satisfaction and learning outcomes. Besides, the proposals for improvement of the weaknesses identified during the process are presented. The results show that the used method has promoted the acquisition of the competences proposed. Moreover the multidisciplinary approach has led to better results in the student performance than those obtained by students enrolled in an unidisciplinary project. Although improvement actions have solved some of the problems detected, there are still some weaknesses, mainly related to team working and tutorials that should be addressed in the future.El grado de Ingeniería Agroalimentaria y del Medio Rural de la Universitat Jaume I de Castelló viene aplicando en su segundo curso un proyecto multidisciplinar usando el Aprendizaje Basado en Proyectos como recurso docente en el que se pretende que los estudiantes adquieran competencias que les ayuden a enfrentarse a su futuro profesional. Este sistema de enseñanza-aprendizaje se ha llevado a cabo durante los tres cursos que está implantado el grado. Una vez asignado un tipo de explotación agrícola concreta y utilizando las herramientas del trabajo en equipo, los estudiantes deben ser capaces de desarrollar un proyecto sobre la explotación con propuestas de mejora que sean factibles y que abarquen aspectos relacionados con las diferentes disciplinas implicadas, aplicando los conocimientos adquiridos en éstas. El presente trabajo incluye los resultados obtenidos durante los tres años del proyecto desde dos puntos de vista importantes: la satisfacción del estudiante y los resultados de aprendizaje. Además se presentan las propuestas de mejora aplicadas en cada curso como respuesta a las debilidades detectadas durante el proceso. Los resultados demuestran que el método utilizado ha favorecido la adquisición de las competencias propuestas. Además, el enfoque multidisciplinar ha propiciado mejores resultados que los alcanzados por los estudiantes que realizaron trabajos unidisciplinares. Por otro lado, aunque las acciones de mejora han permitido solventar algunos de los problemas detectados, siguen persistiendo carencias, sobre todo a nivel de trabajo en equipo y de tutorización, que se proponen como mejoras para el futuro

Análisis de la estrategia logística en las redes ínterorganizativas. Una aplicación al distrito industrial cerámico

Este trabajo reúne dos aspectos fundamentales para la competitividad empresarial: Por un lado la gestión logística en la empresa y las implicaciones de la misma en términos de resultados y, por otro lado, la pertenencia de la empresa a los distritos o clusters industriales. La investigación está centrada en el distrito industrial cerámico de Castellón por ser, así lo entendemos, un marco adecuado para el estudio propuesto. El objetivo general de este trabajo es cubrir el vacío que existe en la literatura estratégica por lo que se refiere a estudios sobre las implicaciones estratégicas de la gestión logística. Éste objetivo general lo hemos concretado en dos objetivos más operativos y específicos. El primero de ellos se ha centrado en la relación entre bases de las elecciones estratégicas, como el benchmarking y el ajuste, y las estrategias logísticas de la empresa y sus prioridades competitivas. El segundo objetivo específico se refiera a la influencia de la elección de una u otra estrategia logística y prioridad competitiva en los resultados logísticos y empresariales. Para la confirmación de nuestros argumentos teóricos, los trabajos empíricos han tenido como objeto de estudio la población de empresas que forman parte del distrito cerámico de Castellón