Quite a few in-depth articles were found in the above-mentioned domain. Bankruptcy is one of the most critical factors which most of companies don't want to face. To predict the Bankruptcy of Banks, there have been several attempts made, some have been successful and some are coming up with new...

This study analyses credit default risk for firms in the Asian and Pacific region by applying two methodologies: a Support Vector Machine (SVM) and a logistic regression (Logit). Among different financial ratios suggested as predictors of default, leverage ratios and the company size display a...

This paper examines the post-pandemic performance of micro, small, and medium-sized firms using Self-Organizing Maps (SOMs), a type of Artificial Neural Network that groups patterns based on their similarities. The goal is to identify the key characteristics that enable firms to face market...

This paper shows the evolution of financial distress prediction models of the past four decades. Special attention is paid to linear discriminant analyses, logistic regression analyses and neural networks. Based on accounting data of 50 UK industrial firms, prediction models are estimated using...

I study the use of non-linear models and accounting inputs to predict the occurrence of litigated bankruptcies and their associated filing outcomes. The main purpose of this study is to identify the accounting patterns associated with bankruptcies. The filing outcomes include, among others, how...

Graphical data representation is an important tool for model selection in bankruptcy analysis since the problem is highly non-linear and its numerical representation is much less transparent. In classical rating models a convenient representation of ratings in a closed form is possible reducing...

In many economic applications it is desirable to make future predictions about the financial status of a company. The focus of predictions is mainly if a company will default or not. A support vector machine (SVM) is one learning method which uses historical data to establish a classification...

This study analyses credit default risk for firms in the Asian and Pacific region by applying two methodologies: a Support Vector Machine (SVM) and a logistic regression (Logit). Among different financial ratios suggested as predictors of default, leverage ratios and the company size display a...

We develop a framework to simultaneously compute the unobservable parameters underlying the structural-parametric models for bankruptcy prediction. More specifically, we compute the unobservable parameters such as, asset value and asset volatility, through learning by embedding in the structural...

The prediction of financial distress has emerged as a significant concern over a prolonged period spanning more than half a century. This subject has garnered considerable attention owing to the precise outcomes derived from its predictive models. The main objective of this study is to predict...