One of the most important tasks in the risk management is the correct determination of probability of default (PD) of particular financial subjects. In this paper a possibility of determination of financial institution’s PD according to the creditscoring models is discussed. The paper is divided into the two parts. The first part is devoted to the estimation of the three different models (based...

[Abstract] The assessment of credit risk of real-estate of Chinese banks has become a hot point nowadays. Traditional gross risk-evaluation methods are mainly Discriminant Analysis (DA) and logistic regression, and the most common approach is a credit rating system based on logistic regression. Yet the daily ripening neural network technique has opened up a new idea for experts in fields of ban...

In this paper, we focus on the new model selection procedure of the discriminant analysis. Combining resampling technique with k-fold cross validation, we develop a k-fold cross validation for small sample method. By this breakthrough, we obtain the mean error rate in the validation samples (M2) and the 95% confidence interval (CI) of discriminant coefficient. Moreover, we propose the model sel...

In this article we present a new and more accurate model for the prediction of the solubility of proteins overexpressed in the bacterium Escherichia coli. The model uses the statistical technique of logistic regression. To build this model, 32 parameters that could potentially correlate well with solubility were used. In addition, the protein database was expanded compared to those used previou...

Case-based reasoning (CBR) is an easily understandable concept. Business failure prediction (BFP) is a valuable tool that can assist businesses take appropriate action when faced with the knowledge of the possibility of business failure. This study aims to improve the performance of a CBR system for BFP in terms of accuracy and reliability by constructing a new similarity measure, an area seldo...

Logistic regression and linear discriminant analysis are used to estimate probability of success for binary data based on a training sample and a certain amount of prior information. Posterior probabilities of success are calculated for different choices of the model for the training sample distribution. An approach to model selection is suggested for certain classes of distributions based on t...

Company default prediction is a widely studied topic as it has a significant impact on banks and firms. Moreover, nowadays, due to the global financial crisis, there is a need to use even more advanced methods (such as soft computing techniques) which can pick up the signs of financial distress on time to evaluate firms (especially small firms). Thus, the author proposes a Genetic Algorithms (G...

Entering engineering students’ cognitive data from high school and their non-cognitive selfbeliefs can be influential factors affecting their academic success and retention decision. Effectively modelling the relationships between these early available factors and student’s future status of persistence in engineering can be particularly valuable to improve student retention in engineering. In t...

Credit risk assessment of agricultural enterprises in the Republic Serbia was analyzed this research by applying discriminant analysis and logistic regressions. The aim is to determine financial indicators which analysts consider when analyzing a loan application that have most influence on decision approve or reject application. internal determinants credit are analyzed, i.e., leverage, profit...