Unstable regions in the scorecards’ input space

Data mining algorithms become more and more popular to satisfy the Basle II requirements, like to predict the probability of default. Not all of these models can be understood easily from economical point of view, which involve the importance of stress tests. In this paper we try to map a retail credit scorecard’s input space to find regions where predictions can lead to significant differing results. Different definitions for similarity and prediction difference are examined to reach an economically and statistically simultaneously interpretable abstraction.

Informational Paradox of external financing

In this paperI develop a model for small and medium enterprises’ external financing. I concentrate on a situation where a firm offering trade credit to his customer applies for a credit at a bank. The effect of a defaulting customer on the borrowing capacity of the supplier has not yet been covered by the litterature. In a contract theroretical more generaly, in a game theoretical framework, there is an informational asymmetry between the lender and the borrower on the credit worthyness of the entrepreneur. In this model, the informational asymmetry leads to moral hazard which results in credit rationing.An optimal contract considering also a possibly defaulting customer generates additional credit rationing. The paper develops two subcases: in the first one the borrower has informational advantage on his customer compared to the creditor, in the second there is not any informatioanl advantage related to the customer. The results show an informational paradox, the informational advantage of the borrower reduces the volume of credit he can receive, while the informational symmetry leads to a higher borrowing capacity.The model decribes a typical situation on the Hungarian market. Banks provide a low volume of credit to SME because the firms’s financial statements and their relationship with their customers is not opac. So the informational disadvantage induces the bank to offer a low level of credit even if the net present value of the financed projects is highly positive.

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

Physics and chemistry of hydrogen in the vacancies of semiconductors

Hydrogen is well known to cause electrical passivation of lattice vacancies in semiconductors. This effect follows from the chemical passivation of the dangling bonds. Recently it was found that H in the carbon vacancy of SiC forms a three-center bond with two silicon neighbors in the vacancy, and gives rise to a new electrically active state. In this paper we examine hydrogen in the anion vacancies of BN, AlN, and GaN. We find that three-center bonding of H is quite common and follows clear trends in terms of the second-neighbor distance in the lattice, the typical (two-center) hydrogen-host-atom bond length, the electronegativity difference between host atoms and hydrogen, as well as the charge state of the vacancy. Three-center bonding limits the number of H atoms a nitrogen vacancy can capture to two, and prevents electric passivation in GaAs as well