Detailed Information on Publication Record

"The aim of this article is to apply a neural network to be able to predict potential financial problems in construction companies based in the Region of South Bohemia of the Czech Republic. Data on all construction companies in the Region of South Bohemia over the period 2003 – 2013 were used for the modelling of the neural network. The data file contained 1,219 records of companies for the individual years. These records included both financial statements and non-accounting data (e.g. data on company employees, and proportional indicators as the case may be). 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 specific model of an artificial neural network. The neural network was 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 utilised by the managers of construction companies, competitors, etc."