Natalia Nebaba

The purpose of the article is to study the innovation levers of developing the intellectual background for economic growth in two groups of post-socialist Central and Eastern European countries (middle-income and lower-middle-income countries). To achieve that, the quantitative effect of the national intellectual capital components (human capital, market capital, structural capital and capital of renewal and development) on the dynamics of the countries’ economic growth was determined.
For both groups, multiple regressions have been constructed that reflect the quantitative relationship between the economic growth rates (in the regressions – the indicator of real gross domestic product per capita) and the components of national intellectual capital in 2010–2018. It has been established that the key innovative indicator of the economic growth of middle-income countries is the national capital of renewal and development, which in general corresponds to the pan-European model of innovation and investment development. Education is the main factor that provides the basis for the economic growth of lower-middle-income countries. Recommendations on improvement of national innovation policy are offered.

Successful crisis resolution of the enterprise depends heavily on its timely detection, which is facilitated by the use of forecasting models. This allows understanding the scale of the problems in a timely manner and developing the appropriate measures, applying various financial mechanisms to prevent it, and in case of occurrence, reducing the amount of losses. In this context, it is important to choose the most optimal informational model that would provide the most objective forecasts, considering the financial activity peculiarities of the analyzed enterprise. Given a wide list of models that predict the financial crisis, there is a need to analyze and select the most accurate model for enterprises in the real economy. Ten Ukrainian machine builders are used to assess the bankruptcy probability using the most popular models; a taxonomic analysis was carried out, which allows systematizing a large amount of data and analyzing their impact on enterprise development. An integral index was determined, which allowed predicting the financial performance dynamics. For each enterprise, ten indicators were used characterizing their financial state for the period 2014–2018. It is substantiated that the selected models differ from each other by the set of initial data and the number of coefficients from four to seven. It is also determined that the efficient use of studied models is quite different; so when choosing a model to predict the bankruptcy probability, it is necessary to consider the peculiarities of the enterprise’s production activity, the accuracy in creating the financial statements and many other factors, including the presence of company’s shares in circulation at the stock market. It is worthwhile to use a taxonomic analysis to make a comprehensive comparison of the enterprise financial state and to substantiate the final choice of the bankruptcy forecasting model.