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Article

Выбор системы экономических показателей для диагностики и прогнозирования банкротств на основе нейросетевого байесовского подхода

Горбатков С. А., Белолипцев И. И.

Introduction.  There are a lot of models of bankruptcies prediction, which differ in methods of modeling and in set of factors. These methods are mainly belong to 5 groups: the classic statistical methods, regression analysis, the method of discriminant analysis, logit-analysis methods, methods of fuzzy sets and neural network methods. Combinations of these methods also can take place. The last three groups of methods are currently being developed especially quickly. As for the choice of factors bankruptcy, prevails heuristics. There is no formal methodology for selection and comparison groups of economic indicators to build the model of bankruptcies, as well as effective methods for data preprocessing. In this paper we propose an original method for the choice of indicators, followed by the construction of neural network model diagnostic of bankruptcies based on  Bayesian approach.

Methods. The study applied the methods of mathematical statistics, correlation analysis, neural network modeling, methods of knowledge representation in intelligent information technologies.

Results. The developed concept of formalization of choice and comparative evaluation of a system of indicators has created the preconditions for the development of effective neural network  model of bankruptcies.

Discussion. The proposed concept was tested for the construction sector of the economy. However, the authors believe that the generality of the approach, the concept and the method may be useful in other industries for a wide range of economic problems, such as the formation of the loan portfolio, an external audit or evaluation of the financial condition of the company.