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

This paper investigates the gender wage gap in Spain by analyzing data from the 2018 Spanish Structure of Earnings Survey to quantify the gap and decompose it into the explained and unexplained gaps using three different ìdiscrimination-freeî distributions (Reimers, Cotton and Jann). As covariates, in addition to seniority at work, studies and type of contract, common in the literature, they introduce occupation. The study treats full-time workers separately from part-time workers. This distinction is important due to the characteristics of the Spanish labor market, where part-time contracts predominate among women. The results show that part-time female employees are less discriminated against than full-time female employees, and that at lower levels of education and in lower skilled occupations The results show that part-time workers are less discriminated against and that at lower levels of education and in lower-skilled occupations, womenís wages are closer to those of men.

La importancia de las micro-entities como generadoras de empleo y propulsoras de la actividad económica conlleva, unida a sus mayores tasas de quiebra y a su dificultad para acceder a las fuentes de financiación, la necesidad de diseñar métodos apropiados que anticipen sus quiebras. Con este fin, en este trabajo se desarrolla un modelo híbrido mediante la combinación de enfoques paramétricos y no paramétricos para la detección de sus quiebras. Para ello, se seleccionan las variables con mayor poder predictivo para detectar la quiebra mediante un modelo híbrido de regresión logística (LR) y árboles de regresión y clasificación (CART). Nuestros resultados muestran que este modelo híbrido obtiene una mejor performance que aquellos modelos implementados de forma aislada, además de tener una más fácil interpretación y una convergencia más rápida. Por otra parte, se constata la conveniencia de la introducción de variables no financieras y macroeconómicas que complementen a la información proporcionada por los ratios financieros para la predicción de la quiebra de las micro-entities, lo cual está en línea con las características propias e idiosincrasia de este tamaño empresarial recientemente definido por la Comisión Europea.

The Basel Accords encourages credit entities to implement their own models for measuring financial risk. In this paper, we focus on the use of internal ratings-based (IRB) models for the assessment of credit risk and, specifically, on one component that models the probability of default (PD). The traditional methods used for modelling credit risk, such as discriminant analysis and logit and probit models, start with several statistical restrictions. The rough set methodology avoids these limitations and as such is an alternative to the classic statistical methods. We apply the rough set methodology to a database of 106 companies that are applicants for credit. We obtain ratios that can best discriminate between financially sound and bankrupt companies, along with a series of decision rules that will help detect operations that are potentially in default. Finally, we compare the results obtained using the rough set methodology to those obtained using classic discriminant analysis and logit models. We conclude that the rough set methodology presents better risk classification results.