A review of financial distress prediction models: logistic regression and multivariate discriminant analysis

In corporate finance, the early prediction of financial distress is considered more important as another occurrence of business risks. The study presents a review of literature for early prediction of financial bankruptcy. It contributes to the formation of a systematic review of the literature regarding previous studies done in the field of bankruptcy. It addresses two most commonly used financial distress prediction models, i.e. multivariate discriminant analysis and logit. Models are discussed with their advantages and disadvantages. After methodological review, it seems that logit regression model (LRM) is more advantageous than multivariate discriminant analysis (MDA) for better prediction of financial bankruptcy. However, accurate prediction of bankruptcy is beneficial to improve the regulation of companies, to form policies for companies and to take any precautionary measures if any crisis is about to come in future

Business failure research

In spite of a growing body of literature on business failures in China and effects of government policy, our understanding of the current state of knowledge remains unclear. The study advances research on the subject by developing the “four-parties” framework to review and synthesise the literature. The paper lays the groundwork for an integrated understanding of the causes and consequences of business failure. In sharp contrast with the evolution and development of Western-based business failure research, much of the literature on China and Chinese firms has focused largely on business failure prediction models by bypassing the traditional evolution from qualitative case study/story approaches to quantitative-based approaches. The study outlines the important implications and promising avenues for future research

Business failure research

In spite of a growing body of literature on business failures in China and effects of government policy, our understanding of the current state of knowledge remains unclear. The study advances research on the subject by developing the “four-parties” framework to review and synthesise the literature. The paper lays the groundwork for an integrated understanding of the causes and consequences of business failure. In sharp contrast with the evolution and development of Western-based business failure research, much of the literature on China and Chinese firms has focused largely on business failure prediction models by bypassing the traditional evolution from qualitative case study/story approaches to quantitative-based approaches. The study outlines the important implications and promising avenues for future research

On Random Subspace Optimization-Based Hybrid Computing Models Predicting the California Bearing Ratio of Soils

The California Bearing Ratio (CBR) is an important index for evaluating the bearing capacity of pavement subgrade materials. In this research, random subspace optimization-based hybrid computing models were trained and developed for the prediction of the CBR of soil. Three models were developed, namely reduced error pruning trees (REPTs), random subsurface-based REPT (RSS-REPT), and RSS-based extra tree (RSS-ET). An experimental database was compiled from a total of 214 soil samples, which were classified according to AASHTO M 145, and included 26 samples of A-2-6 (clayey gravel and sand soil), 3 samples of A-4 (silty soil), 89 samples of A-6 (clayey soil), and 96 samples of A-7-6 (clayey soil). All CBR tests were performed in soaked conditions. The input parameters of the models included the particle size distribution, gravel content (G), coarse sand content (CS), fine sand content (FS), silt clay content (SC), organic content (O), liquid limit (LL), plastic limit (PL), plasticity index (PI), optimum moisture content (OMC), and maximum dry density (MDD). The accuracy of the developed models was assessed using numerous performance indexes, such as the coefficient of determination, relative error, MAE, and RMSE. The results show that the highest prediction accuracy was obtained using the RSS-based extra tree optimization technique

Un modelo global de predicción de quiebra con redes neuronales

El capítulo 3 está dedicado al proceso de obtención de las muestras, a las variables utilizadas y a los criterios tenidos en cuenta para la selección de las mismas. Por su parte, en el capítulo 4 se presentan los resultados del análisis empírico, dejando constancia de los modelos de predicción de la quiebra desarrollados y de la robustez de los mismos. Finalmente, el trabajo concluye con una discusión sobre los resultados alcanzados, con la exposición de las principales conclusiones obtenidas y con el detalle de la bibliografía consultada. Fecha de lectura de Tesis Doctoral: 29 de enero 2019.El presente trabajo trata de responder a la cuestión de investigación de si es posible mejorar la precisión de los modelos globales de predicción de quiebra existentes en la literatura previa. Para responder a esta cuestión se ha tenido en cuenta los excelentes resultados de clasificación que proporcionan los métodos computacionales tales como las redes neuronales artificiales, y se han construidos tanto modelos regionales para Asia, Europa y Norte América, como modelos globales. En concreto, se ha utilizado el denominado Perceptrón Multicapa y los resultados obtenidos han permitido constatar una mayor precisión de los métodos computacionales frente a las técnicas estadísticas tradicionales. La estructura del presente trabajo de investigación es la siguiente. En el capítulo 1 se lleva a cabo un análisis de la literatura previa sobre predicción de quiebra. De este análisis se han obtenido conclusiones sobre los métodos aplicados y su perfeccionamiento, sobre las variables empleadas, y sobre la evolución de los resultados obtenidos por los distintos modelos. Además, y atendiendo al enfoque de estudio adoptado, se ha analizado la literatura diferenciando entre modelos globales y modelos regionales. Este primer capítulo concluye aportando una clasificación de los estudios previos en la que se pone de manifiesto los principales argumentos utilizados y la brecha existente acerca de la superioridad de los modelos globales frente a los modelos regionales. El capítulo 2 aborda los fundamentos del método de naturaleza computacional utilizado en el presente trabajo. Además, se presentan la técnica de validación cruzada y los principales criterios de selección de modelos, que han sido adicionalmente utilizados para el contraste de los resultados

La Previsione dell’insolvenza aziendale: confronto della performance dei modelli Zscore, Logit e Random Forest su un campione di aziende manifatturiere italiane

With this study we intend to compare three different methodologies applied for bankruptcy prediction, in order to define which one is the most reliable: Zscore analysis, Logit model and Random Forest. The aim is to establish if Altman’s Z-score, a widely used tool to evaluate the financial health of a company, is still an efficient methodology to predict bankruptcy or financial stress conditions. Several other forecasting methods have been developed over the years, most of them based on logistic regression. Here we present a methodology based on a machine learning algorithm (Random Forest) to analyze and predict the bankruptcy of 3.000 Italian manufacturing companies. We performed the same analysis with Altman's Z-score and Logit model. According to our results, Random Forest obtained the best performance, with a prediction accuracy of 99,85%. Our results show that applications of machine learning based methods to predict bankruptcy might overcome pre-existing methodologies and be more efficient to identify companies that may become insolvent and unable to repay loans.</br