Static Model Classification Status: Taking into Account Emerging External Factors
Analysis of the problem of predicting bankruptcy shows that foreign and domestic models included only internal factors of enterprises. But the same indicators of internal factors in the rapidly changing external environment can lead to bankruptcy, and in others not. External factors are the most dangerous, because the possible influence on them is minimal and the impact of their implementation can be devastating. This paper aims to - on the same factors to assess the impact of the Russian enterprises account macroeconomic indicators (external factors) on the parameters of static models predict a local approximation of the crisis at the plant. To accomplish the purpose of the database was compiled Spark set of 100 companies, including 50 companies officially declared bankrupt in the period 2000-2009 and 50 stable operating companies with a random sample of the same time period. External factors were extracted from the Joint Economic and Social Data Archive . The author compared two data set: (1) microeconomic indicators—money to the total liabilities, retained earnings to total assets, net profit to revenue, Earnings Before Interest and Taxes (EBIT) to assets, net income to equity, net profit to total liabilities, current liabilities to total assets, the totality of short-term and long-term loans to total assets, current assets to current liabilities, assets to revenue, equity to total assets, current assets to revenue; and (2) external factors—index of real gross domestic product (GDP), industrial production index, the index of real cash incomes, an index of real investments, consumer price index, the refinancing rate, unemployment rate, the price of electricity, gas prices, oil price, gas price, dollar to ruble, ruble euro Standard & Poor (S&P) index, the Russian Trading System (RTS) index, and region. The aim of the comparison results paging classes “insolvent” and “non-bankrupt” is achieved using two methods: classification and discrimination. In both methods, computational procedures are realized with the use of algorithms: linear regression, artificial neural network, and genetic algorithm. In the 2-m model, data set includes both internal and external factors. The results showed that the inclusion of only the microeconomic indicators, excluding external factors impedes models about 2 times.