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Discussion on methodological problems of corporate survival and solvency prediction is enjoying a renaissance in the era of financial and economic crisis. Within the framework of this article, the most frequently applied bankruptcy prediction methods are competed on a Hungarian corporate database. Model reliability is evaluated by Receiver Operating Characteristic (ROC) curve analysis. The article attempts to answer the question of whether the simultaneous application of data reduction and univariate splitting (or just one of them) improves model performance, and for which methods it is worth applying such transformations.

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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.

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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.

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In our work, we compare the predictive power of different bankruptcy prediction models built on financial indicators calculable from businesses’ accounting data on the database of the first Hungarian bankruptcy model. For modelling, we use data-mining methods often applied in bankruptcy prediction: neural networks (NN), support vector machines (SVM) and the rough set theory (RST) capable of rule-based classification. The point of departure for our comparative analysis is the practical finding that black-box-type data-mining methods typically show better classification performance than models whose results are easy to interpret, i.e. there seems to be a kind of trade-off between the interpretability and predictive power of bankruptcy models. Empirical results lead us to conclude that the RST approach can be a competitive alternative to black-box-type SVM and NN models. In our research, we did not find any major trade-off between the interpretability and predictive performance of bankruptcy models on the database of the first Hungarian bankruptcy model.

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Acta Biologica Hungarica
Authors: Zoltán Gazdag, Timea Stromájer-Rácz, Joseph Belagyi, Richard Y. Zhao, Robert T. Elder, Eszter Virág, and Miklós Pesti

The wild-type viral protein R (Vpr) of human immunodeficiency virus type 1 exerts multiple effects on cellular activities during infection, including the induction of cell cycle G2 arrest and the death of human cells and cells of the fission yeast Schizosaccharomyces pombe. In this study, wild-type Vpr (NL4-3Vpr) integrated as a single copy gene in S. pombe chromosome was used to investigate the molecular impact of Vpr on cellular oxidative stress. NL4-3Vpr triggered an atypical response in early (14-h), and a wellregulated oxidative stress response in late (35-h) log-phase cultures. Specifically, NL4-3Vpr expression induced oxidative stress in the 14-h cultures leading, to decreased levels of superoxide anion (O2 ·−), hydroxyl radical (·OH) and glutathione (GSH), and significantly decreased activities of catalase, glutathione peroxidase, glutathione reductase, glucose-6-phosphate dehydrogenase and glutathione S-transferase. In the 35-h cultures, elevated levels of O2 ·− and peroxides were accompanied by increased activities of most antioxidant enzymes, suggesting that the Vpr-induced unbalanced redox state of the cells might contribute to the adverse effects in HIV-infected patients.

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