Neural Networks in Bankruptcy Prediction - A Comparative Study on the Basis of the First Hungarian Bankruptcy Model

The article attempts to answer the question whether or not the latest bankruptcy prediction techniques are more reliable than traditional mathematical–statistical ones in Hungary. Simulation experiments carried out on the database of the first Hungarian bankruptcy prediction model clearly prove that bankruptcy models built using artificial neural networks have higher classification accuracy than models created in the 1990s based on discriminant analysis and logistic regression analysis. The article presents the main results, analyses the reasons for the differences and presents constructive proposals concerning the further development of Hungarian bankruptcy prediction.

Application of support vector machines on the basis of the first Hungarian bankruptcy model

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 ROC curve. In using the SVM technique, in addition to conventional kernel functions, we also examine the possibilities of applying the ANOVA kernel function and take a detailed look at data preparation tasks recommended in using the SVM method (handling of outliers). The results of the models assembled suggest that a significant improvement of classification accuracy can be achieved on the database of the first Hungarian bankruptcy model when using the SVM method as opposed to neural networks.

Trust and cooperation in buyer–seller relationships and networks. The co-evolution of structural balance and trust in iterated PD games

Our study has two aims: to elaborate theoretical frameworks and introduce social mechanisms of spontaneous co-operation in repeated buyer-seller relationships and to formulate hypotheses which can be empirically tested. The basis of our chain of ideas is the simple two-person Prisoner’s Dilemma game. On the one hand, its repeated variation can be applicable for the distinction of the analytical types of trust (iteration trust, strategy trust) in co-operations. On the other hand, it provides a chance to reveal those dyadic sympathy-antipathy relations, which make us understand the evolution of trust. Then we introduce the analysis of the more complicated (more than two-person) buyer-seller relationship. Firstly, we outline the possible role of the structural balancing mechanisms in forming trust in three-person buyer-seller relationships. Secondly, we put forward hypotheses to explain complex buyer-seller networks. In our research project we try to theoretically combine some of the simple concepts of game theory with certain ideas of the social-structural balance theory. Finally, it is followed by a short summary.