Podrobný výpis o publikaci

The construction sector is one of the main pillars of an advanced economy. It is the first sector to indicate potential national economic problems. In a similar way it is the first sector to show signs of recovery when an economy is coming out of recession or crisis. The aim of this article is to apply a neural network to be able to predict potential financial problems in construction companies in the Czech Republic. Data on all construction companies in the Czech Republic over the period 2003-2013 were used for the modelling of the neural network. The data file contained 67,000 records. These records included both financial statements and non-accounting data (e.g. data on company employees). The following networks were used for modelling the neural network: a linear network, a probabilistic neural network (PNN), a generalised regression neural network (GRNN), a radial basis function network (RBF), a three-layer perceptron network (TLP) and a four-layer perceptron network (FLP). The analysis resulted in a concrete model of an artificial neural network. The neural network is able to determine with more than ninety per cent accuracy whether a company is able to overcome potential financial problems, within how many years a company might go bankrupt, or whether a company might go bankrupt within one calendar year. The text also includes the basic statistical characteristics of the examined sample and the achieved results (sensitivity analysis, confusion matrix, etc.). The model can be exploited in practice by construction company managers, investors looking for a suitable company for capital investment, competitors, etc.