Improving bankruptcy prediction in micro-entities by using nonlinear effects and non-financial variables

The use of non-parametric methodologies, the introduction of non-financial variables, and the development of models geared towards the homogeneous characteristics of corporate sub-populations have recently experienced a surge of interest in the bankruptcy literature. However, no research on default prediction has yet focused on micro-entities (MEs), despite such firms’ importance in the global economy. This paper builds the first bankruptcy model especially designed for MEs by using a wide set of accounts from 1999 to 2008 and applying artificial neural networks (ANNs). Our findings show that ANNs outperform the traditional logistic regression (LR) models. In addition, we also report that, thanks to the introduction of non-financial predictors related to age, the delay in filing accounts, legal action by creditors to recover unpaid debts, and the ownership features of the company, the improvement with respect to the use of solely financial information is 3.6%, which is even higher than the improvement that involves the use of the best ANN (2.6%)

Hybrid model using logit and nonparametric methods for predicting micro-entity failure

Following the calls from literature on bankruptcy, a parsimonious hybrid bankruptcy model is developed in this paper by combining parametric and non-parametric approaches.To this end, the variables with the highest predictive power to detect bankruptcy are selected using logistic regression (LR). Subsequently, alternative non-parametric methods (Multilayer Perceptron, Rough Set, and Classification-Regression Trees) are applied, in turn, to firms classified as either “bankrupt” or “not bankrupt”. Our findings show that hybrid models, particularly those combining LR and Multilayer Perceptron, offer better accuracy performance and interpretability and converge faster than each method implemented in isolation. Moreover, the authors demonstrate that the introduction of non-financial and macroeconomic variables complement financial ratios for bankruptcy prediction

Modelling self-sufficiency of microfinance institutions using logistic regression based on principal component analysis

Analizar los factores que influyen en la sostenibilidad es clave para llegar a alcanzarla. En base a la Teoría de los Recursos y las Capacidades (Grant, 1991), desarrollamos un modelo de gestión que determina los factores explicativos de la sostenibilidad de las instituciones microfinancieras (IMF). El modelo empírico se desarrolla aplicando análisis de componentes principales y regresión logística, sobre una muestra de 313 IMF, con 31 variables financieras agrupadas en seis componentes/factores que están teóricamente relacionadas con su autosuficiencia. Nuestros resultados muestran una relación significativa y positiva entre el tama˜no y la eficiencia-productividad de las IMF con su sostenibilidad, presentando el factor riesgo de crédito una relación inversa con respecto a dicha sostenibilidad. Por tanto, sugerimos que las IMF que quieran continuar desarrollando su actividad bajo un enfoque de autosuficiencia, deben fomentar una estrategia de gestión orientada hacia: (1) el aumento de la eficiencia-productividad, (2) el control exhaustivo del riesgo de crédito y (3) el incremento del tama˜no para la consecución de economías de escala. La capacidad predictiva del modelo es alta, con un área bajo la curva ROC (AUC) del 89.7%.The analysis of the factors that influence sustainability is the key to achieving it. Based on the Theory of Resources and Capabilities (Grant, 1991), a management model that determines the explanatory factors of the sustainability of microfinance institutions (MFI) is developed. The empirical model is constructed by applying a principal component and logistic regression analysis using a sample of 313 MFI, with 31 finance variables, grouped into 6 components/factors that are theoretically associated with selfsufficiency. The results obtained showed a significant and positive relationship between size and the efficiency-productivity of the MFI and their sustainability, with the credit risk factor having an inverse relationship as regards that sustainability. Thus, it may be suggested that the MFI that wish to continue developing their activity using a self-sufficiency approach must promote a management strategy oriented towards: (1) an increase in efficiency-productivity, (2) the exhaustive control of credit risk and, (3) the increase in size in order to achieve economies of scale. The predictive capacity of the model is high, with an area under the ROC curve of 89.7%

La metodología de los Rough Sets como técnica de preprocesamiento de datos: una aplicación a las quiebras de microempresas familiares

Micro enterprises (MEs) represent over 75 % of all enterprises in the EU, accounting for over 30 % of employment. However, since the onset of the economic crisis in 2008, this business segment has suffered high rates of bankruptcies and business closures, destroying many jobs. The construction of models that anticipate insolvency to allow sufficient time to take appropriate action is important to avoid bankruptcy of the MEs. However, it is difficult to obtain complete and relevant information for MEs, making it very difficult to be a good fit of the models for predicting corporate failure for this size of company. Applying Rough Sets technique as a method for pre - processing of the data, in the present study, we order the variables that best discriminate between solvent / insolvent in order to increase efficiency in predicting insolvency MEs. Additionally, we provide an application of the technique to family- MEs. Throughout this process, our results highlight the importance of considering non-financial variables to predict insolvency of MEs.Las microempresas (MEs) representan más del 75% del tejido empresarial de la Unión Europea, acaparando más del 30% del empleo. No obstante, desde el inicio de la crisis económica en el año 2008, este segmento empresarial viene sufriendo elevadas tasas de quiebras y cierres empresariales, destruyéndose numerosos puestos de trabajo. La construcción de modelos que anticipen situaciones de insolvencia que permitan adoptar con suficiente antelación las medidas oportunas, es clave para evitar la quiebra de las MEs. A pesar de ello, es difícil disponer de información completa y relevante de las MEs, lo que hace muy difícil un buen ajuste de los modelos de predicción de la insolvencia empresarial para este tamaño de empresas. Aplicando la técnica de los Rough Sets como un método de preprocesamiento de los datos, en el presente estudio, ordenamos las variables que mejor discriminan entre MEs solventes/insolventes en aras a incrementar la eficiencia en la predicción de su insolvencia. Además, ofrecemos una aplicación de la técnica a MEs de carácter familiar. En todo este proceso nuestros resultados realzan la relevancia de la consideración de variables no financieras para predecir la insolvencia de las MEs

Factores determinantes de las quiebras en microempresas

La pequeña y mediana empresa es uno de los principales motores de las economías europeas. De entre ellas, un alto porcentaje son microempresas las cuales generan la mayor parte del empleo. En la actualidad este segmento empresarial está sufriendo en mayor medida la situación de crisis financiera, con la consecuente elevación de la tasa de destrucción de las mismas. En este contexto, desarrollar modelos de quiebra específicos para este tamaño empresarial e identificar las variables con mayor poder explicativo constituye un reto. Aquí se aborda la cuestión llegando a ser un trabajo pionero en este campo, en tanto la metodología utilizada como en el sector al que se aplica, caracterizado por una elevada opacidad informativa. Partiendo de variables financieras y no financieras que han sido utilizadas con relativo éxito en el pronóstico de quiebra empresarial en general, tratamos de determinar cuáles de ellas están afectando en mayor medida a la microempresa. Para ello utilizamos una técnica no paramétrica de aprendizaje basada en los rough set, que aplicamos a una muestra de empresas del Reino Unido, con iguales porcentajes respecto a su situación de fallida y a su carácter familiar, por ser esta última característica un factor condicionante de los resultados.The small and medium enterprises are one of the main drivers of European economies. A large percentage consists of microenterprises that generate the most part of employment. Today this business segment is suffering the financial crisis, with the consequent increase in the rate of destruction of the same. So develop specific models for these bankruptcy and identify variables with greater explanatory power is challenging. So this study is becoming a pioneering work in this field in both the methodology used and the sector to which it applies, which has a higher opacity. Based on financial and non-financial variables that have been used with relative success in predicting bankruptcy in general, we try to determine which ones are affecting more to microfirms. We use a nonparametric learning technique based on the rough sets, which apply to a sample of UK firms, balanced on its failed situation and its familiar character, which determines the results

The use of social networks within the European Higher Education Area

Este trabajo recoge la experiencia de tres profesores noveles y una profesora mentora tras su participación en el proyecto “Formación del Profesorado Novel”, organizado por el Instituto de Ciencias de la Educación (ICE) de la Universidad de Sevilla durante el curso académico 2011-2012. La incorporación de las redes sociales a la metodología docente universitaria, así como un uso intensivo de la plataforma virtual WebCT, se propusieron como dos de las actividades a desarrollar. El objetivo del artículo es analizar si se produce una mejora en la participación activa y los resultados académicos del alumnado al incorporar tecnologías de la información y las comunicaciones (TICs) como base de las metodologías docentes. Para ello, se ha realizado un experimento de campo utilizando los resultados de tres asignaturas diferentes durante el curso académico 2011-2012: Introducción a las Finanzas, de primer curso, y Mercados Financieros Derivados, de tercer curso, ambas del grado en Finanzas y Contabilidad, y Dirección Financiera, de cuarto curso de la Licenciatura en Administración y Dirección de Empresas. Con este análisis buscamos: (a) contribuir a un mejor conocimiento a la hora de aplicar nuevas tecnologías como metodología docente; y (b) comprobar en qué medida una participación activa del alumno puede pronosticar una buena nota en el examen. Tras la aplicación de estadística descriptiva y análisis de regresión logística (logit), nuestros resultados muestran que aquellos alumnos con una participación más activa en el seguimiento de la asignatura mejoran significativamente su rendimiento académico.This paper describes the experience of three novice lecturers and a mentor lecturer, after their participation in the project "Beginner Teacher Training" organized by the Institute of Education Sciences (ICE) at the University of Seville during the academic year 2011-2012. The incorporation of social networks to the university teaching methodology and an intensive use of the WebCT virtual platform were proposed as two of the activities to be developed. Our objective in this article is to determine if there is an improvement in the student results when new technologies that require active student participation are incorporated as the basis of the teaching methodology. To this end, a field experiment is carried out by using the results of three different subjects for the academic course 2011-2012: Introduction to Finance (first year), Derivatives Financial Markets (third year), both belong to the degree in Finance and Accounting, and Corporate Finance (fourth year) of the degree in Business Administration and Management. With this analysis we aim to: (a) contribute to a better understanding when implementing new technologies into the teaching methodology, and (b) verify to what extent a student's active participation can predict a good mark in the exam. By using descriptive statistics and logistic regression analysis, the results show a marked improvement in the academic performance of those students whose participation is more active in the pursuit of the subject

Estimation of sample in the perception of the sports services through the Generalizability Theory

The present study uses the Generalizability Theory (GT) apply into the sport management area aiming to know the accurate sample size to evaluate the perceived quality. For this purpose, a compound structure was used for the participants (P) and items (I) aspects, with a fully crossed design. Applying general linear model (GLM), with both aspects, a variance analysis was realised and it estimated the generalization’s precision. The instrument was a survey composed for 25 items and the sample, in total 330, comes from a fitness centre in the city of Malaga. The obtain results shows a number of participants using a reliable register (ξρ²(δ) = 0.98), and it estimates the precision level in the generalization to the sample size used (generalizability coefficient, ξρ²(Δ) = 0.96), assuming in this side an important progress in relation to the efficiency in upcoming evaluations in sports service

Experiencia en el proyecto de profesores noveles

Este trabajo recoge la experiencia de tres profesores noveles y una profesora mentora, tras su participación en el proyecto “Formación del Profesorado Novel” organizado por el Instituto de Ciencias de la Educación (ICE) de la Universidad de Sevilla durante el curso académico 2011-2012. Las conclusiones de la experiencia recogen la opinión personal de los autores abarcando mejoras tanto en aspectos personales como organizativos relativos a la labor docente