We evaluate the prediction accuracy of models designed using different classification methods depending on the technique used to select variables, and we study the relationship between the structure of the models and their ability to correctly predict financial failure. We show that a neural...

En este artículo aportamos evidencia empírica de predicción del fallo financiero en empresas no financieras. Hemos desarrollado diversos modelos para la evaluación del riesgo de fallo financiero en PYME. Contrastada la capacidad predictiva de modelos paramétricos (análisis discriminante...

El objetivo del presente trabajo de investigación es mostrar el impacto de las amenazas de la inundación, la sequía y la helada en el bienestar de las diferentes regiones de Bolivia, tomando a las vulnerabilidades como variables de aproximación a los indicadores socioeconómicos de...

Since 1968, after the development of multivariate model, financial health of the corporate sector to predict their financial failure is heavily studied. Altman Z-Score is the most efficient model to judge the financial failure of the companies. This study uses Altman’s Z-Score and current...

Data processes run by states, governments and the like have been a great deal and as old as the modern human history. Data had always been important. Tons were collected and siloed, but never in the past had its importance been felt as much as it had been when the last crisis broke out in 2008....

A Bázel-2 tőkeegyezmény magyarországi bevezetése új lendületet adott a sokváltozós csőd-előrejelzési módszerek alkalmazásnak és továbbfejlődésének. A cikk a nemzetközi szakirodalomban és pénzintézeti gyakorlatban leggyakrabban alkalmazott négy csőd-előrejelzési...

Predicting default probabilities is at the core of credit risk management and is becoming more and more important for banks in order to measure their client's degree of risk, and for rms to operate successfully. The SVM with evolutionary feature selection is applied to the CreditReform database....

The purpose of this paper is to propose a hybrid model which combines locally linear embedding (LLE) algorithm and support vector machines (SVM) to predict the failure of firms based on past financial performance data. By making use of the LLE algorithm to perform dimension reduction for feature...

Discussion on methodological problems of corporate survival and solvency prediction is enjoying a renaissance in the era of financial and economic crisis. Within the framework of this article, the most frequently applied bankruptcy prediction methods are competed on a Hungarian corporate...

In our study we rely on a data mining procedure known as support vector machine (SVM) on the database of the first Hungarian bankruptcy model. The models constructed are then contrasted with the results of earlier bankruptcy models with the use of classification accuracy and the area under the...