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...

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...

In this paper we use a reduced form model for the analysis of Portfolio Credit Risk. For this purpose, we fit a Dynamic Factor model, DF, to a large dataset of default rates proxies and macrovariables for Italy. Multi step ahead density and probability forecasts are obtained by employing both...

In this paper we use a reduced form model for the analysis of Portfolio Credit Risk. For this purpose, we fit a Dynamic Factor model, DF, to a large dataset of default rates proxies and macrovariables for Italy. Multi step ahead density and probability forecasts are obtained by employing both...

This paper examines the impact of equity misvaluation on the predictive accuracy of bankruptcy models. We find that structural bankruptcy prediction models are not affected by misvaluation. However, for hazard models, forecasting accuracy for properly-valued firms is greater than for misvalued...

This study proposes a simple theoretical framework that allows for assessing financial distress up to five years in advance. We jointly model financial distress by using two of its key driving factors: declining cash-generating ability and insufficient liquidity reserves. The model is based on...

Objective – The purpose of this study is to construct a business failure classification model that may be reliably applied to companies in the manufacturing sector. The model will be used to improve the predictive abilities for companies with different financial, business and operating...

This study investigates the ability of three versions of Altman's Z-Score model (Z, Z', and Z”) of distress prediction developed in the U.S. to predict the corporate distress in the emerging market of Sri Lanka. The results show that these models have a remarkable degree of accuracy in...