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.

The calculation of dynamic ecological footprint on the basis of the dynamic input-output model

The Leontief input-output model is widely used to determine the ecological footprint of consumption in a region or a country. It is able to capture spillover environmental effects along the supply change, thus its popularity is increasing in ecology related economic research. These studies are static and the dynamic investigations are neglected. The dynamic Leontief model makes it possible to involve the capital and inventory investment in the footprint calculation that projects future growth of GDP and environmental impacts. We show a new calculation method to determine the effect of capital accumulation on ecological footprint. Keywords: Dynamic Leontief model, Dynamic ecological footprint, Environmental management, Allocation method