Bankruptcy Prediction of Small and Medium Enterprises Using a Flexible Binary Generalized Extreme Value Model

We introduce a binary regression accounting-based model for bankruptcy prediction of small and medium enterprises (SMEs). The main advantage of the model lies in its predictive performance in identifying defaulted SMEs. Another advantage, which is especially relevant for banks, is that the relationship between the accounting characteristics of SMEs and response is not assumed a priori (e.g., linear, quadratic or cubic) and can be determined from the data. The proposed approach uses the quantile function of the generalized extreme value distribution as link function as well as smooth functions of accounting characteristics to flexibly model covariate effects. Therefore, the usual assumptions in scoring models of symmetric link function and linear or pre-specied covariate-response relationships are relaxed. Out-of-sample and out-of-time validation on Italian data shows that our proposal outperforms the commonly used (logistic) scoring model for different default horizons

Predicting class-imbalanced business risk using resampling, regularization, and model ensembling algorithms

We aim at developing and improving the imbalanced business risk modeling via jointly using proper evaluation criteria, resampling, cross-validation, classifier regularization, and ensembling techniques. Area Under the Receiver Operating Characteristic Curve (AUC of ROC) is used for model comparison based on 10-fold cross-validation. Two undersampling strategies including random undersampling (RUS) and cluster centroid undersampling (CCUS), as well as two oversampling methods including random oversampling (ROS) and Synthetic Minority Oversampling Technique (SMOTE), are applied. Three highly interpretable classifiers, including logistic regression without regularization (LR), L1-regularized LR (L1LR), and decision tree (DT) are implemented. Two ensembling techniques, including Bagging and Boosting, are applied to the DT classifier for further model improvement. The results show that Boosting on DT by using the oversampled data containing 50% positives via SMOTE is the optimal model and it can achieve AUC, recall, and F1 score valued 0.8633, 0.9260, and 0.8907, respectively

Changes in bankruptcy indicators of banks in Colombia from convergence to Colombian Financial Reporting Standards

El propósito de este artículo es examinar el efecto que tuvo la convergencia a Normas Colombianas de Información Financiera (NCIF) sobre la posibilidad de bancarrota de los establecimientos bancarios a partir del análisis de sus indicadores de quiebra. En Colombia, a partir del año 2014 se debía realizar la transición a estas normas, las cuales se basan en las Normas Internacionales de Información Financiera (NIIF), emitidas por la Junta de Normas Internacionales de Contabilidad (IASB). Los trabajos desarrollados a nivel internacional muestran la aplicación de métodos estadísticos para analizar los efectos de la implementación de NIIF, en ese sentido, este trabajo incluye análisis de datos de tipo cuantitativo como aporte a la literatura colombiana en este tema. Para ello se utilizó un modelo de análisis discriminante múltiple denominado ZII Score de Altman como predictor de bancarrota, en 24 bancos durante los años 2014 sin NCIF y 2014, 2015 y 2016 con NCIF. Entre los hallazgos se encuentra que una proporción importante de los bancos permanecen en la misma zona en la cual se encontraban antes de iniciar el proceso de paso a NIIF.The purpose of this article is to examine the effect that convergence to Colombian Financial Reporting Standards (CFRS) had on the possibility of bankruptcy of banking establishments from the analysis of their bankruptcy indicators. In Colombia, starting in 2014, the transition to these standards was required, which are based on the International Financial Reporting Standards (IFRS), issued by the International Accounting Standards Board (IASB). The works developed at the international level show the application of statistical methods to analyze the effects of the implementation of IFRS, in this sense, this work includes analysis of quantitative data as a contribution to the Colombian literature on this topic. For this, a multiple discriminant analysis model called Altman’s ZII Score was used as a bankruptcy predictor, in 24 banks during 2014 without CFRS and 2014, 2015 and 2016 with CFRS. Among the findings is that a significant proportion of the banks remain in the same area in which they were before starting the process of transition to IFRS

Bankruptcy prediction of small and medium enterprises using a flexible binary generalized extreme value model

We introduce a binary regression accounting-based model for bankruptcy prediction of small and medium enterprises (SMEs). The main advantage of the model lies in its predictive performance in identifying defaulted SMEs. Another advantage, which is especially relevant for banks, is that the relationship between the accounting characteristics of SMEs and response is not assumed a priori (eg, linear, quadratic or cubic) and can be determined from the data. The proposed approach uses the quantile function of the generalized extreme value distribution as link function as well as smooth functions of accounting characteristics to flexibly model covariate effects. Therefore, the usual assumptions in scoring models of symmetric link function and linear or pre-specified covariate-response relationships are relaxed. Out-of-sample and out-of-time validation on Italian data shows that our proposal outperforms the commonly used (logistic) scoring model for different default horizons