Managing and evaluating the probability of bankruptcy of Ukrainian enterprises is one of the most complex and relevant problems of the economy and management. In the context of Ukraine’s integration into the international space, there is an arising issue of assessing the bankruptcy of Ukrainian enterprises that meets international financial standards and allows administering this process. A qualitative assessment of the bankruptcy of an enterprise is possible only using artificial intelligence methods – the fuzzy sets method, which allows including qualitative and quantitative indicators to the model for assessing bankruptcy of enterprises in Ukraine. The aim of the article is to improve the existing method for assessing the probability of bankruptcy of Ukrainian enterprises on the basis of the fuzzy sets method, which will include indicators of international financial reporting and allow more efficient administration and management of this process. The subject of the research is the process of formalizing the method of the enterprise bankruptcy assessment in accordance with the indicators of International Financial Reporting Standards. The study offers a mechanism for a comprehensive assessment of the probability of bankruptcy of Ukrainian enterprises with the use of the methods of fuzzy sets, which is based on international financial indicators: current ratio, payable turnover ratio, equity turnover ratio, return on assets, equity-to-debt ratio. The mechanism allows quickly managing bankruptcy conditions. In order to administer the economic activity of the bankrupt enterprises, based on the theory of a fuzzy sets, a system of enterprises management takes into account the international financial reporting.
The processes of integration of Ukraine into the European Economic Community, the presence of powerful competitors in the European markets encourage the formation of a set of measures with the distinction of tools to ensure the competitiveness of the agro-industrial complex of the state. The necessity of ensuring the competitiveness of Ukraine's agro-industrial complex on the basis of determining the competitive advantages dictate the urgency of scientific search for new methods, forms, tools for its enhancement, which will further promote the market relations in Ukraine and will have a direct impact on the wellbeing of the population. The aim of the work is to develop an innovative economic-mathematical model for assessing and forecasting the grade of competitiveness of the agricultural sector of Ukraine based on fuzzy sets, which will allow acceleration of the process of Ukraine integration into the European market. The object of the research is the process of ensuring the competitiveness of the Ukrainian agricultural sector. The subject of the research is methodological aspects of economic and mathematical modeling of the competitiveness of the agrarian sector of Ukraine. The methodology of the study is based on the principles and mathematical provisions of fuzzy sets, which allows to use both qualitative and quantitative indicators of influence on the process under study. As a result of the research, based on fuzzy set theory, which allows taking into account both quantitative and qualitative factors of influence on the level of competitiveness, an innovative economic-mathematical model of valuation and forecasting of the grade of competitiveness of the agricultural sector of Ukraine has been developed. The classification of factors influencing the level of agrarian industry competitiveness has been formed. The forecast of the grade of competitiveness of the agricultural sector of Ukraine in 2025 has been made, which will allow formulating a strategy of development of the agricultural sector of Ukraine.
In this article, an updated approach to investigate the effects of demographic factors on economic growth is proposed. The initial hypothesis was that these factors significantly affected production proportions, determining development vectors. The predictable shifts in production dynamics are considered for the institutional framework. The article investigates the statistically significant relationships between the demographic variables and economic growth for the sample of the OECD countries (excluding Columbia) and Armenia, Belarus, Bulgaria, Croatia, Georgia, Kazakhstan, Romania, the Russian Federation, and Ukraine, from 1990 to 2017; unbalanced panel data was used. The investigation aimed to highlight the intrinsic interconnection between the changes in demographic variables (e.g., the working‑age population growth rate and the average life expectancy growth rate) and economic growth. Our investigation focused on the issue of whether demographic influence on economics was the same for advanced and developing countries in the sample. Over the period, a significant increase in life expectancy adversely affected the real GDP per capita growth rate. However, the empirical study pointed out that life expectancy was strongly linked to nominal GDP per capita. In advanced countries, the demographic indicator was considerably higher than in emerging markets. We found that the rise in the working‑age stratum of the nation’s population radically reduced the output dynamics as well, but that interconnection was not robust. The institutional framework should be taken into account in order to achieve a favorable performance of public governance in the long‑run. The main demographic variables should be properly forecasted and calibrated for potential endogenous economic triggers. Both public and private investments are important when considering the economic growth rates that are achieved. We propose a balanced approach to macroeconomic policy regarding both demographic and institutional determinants.
Investment plays a very important role in the economy, ensures its sustainable growth, contributes to the improvement of the living standards of the population. The most common mistake of planning investment projects is the insufficient development of risks that may affect the profitability of projects. The purpose of the paper is the formalizing the normal distribution for investment project evaluation using the Monte Carlo method. Such formalizing should allow to present normal distribution in a form that is understandable for nonspecialists in mathematical statistics. A user can easily calculate the standard deviation value and determine the limits of the confidence interval and the range of deviation from the mean value. Such mistakes can lead to incorrect investment decisions and significant losses. The desire to minimize risk requires developing a risk model. One of the risk assessment tools is the Monte Carlo method, which combines and develops both methods of sensitivity analysis and scenario analysis. In the Monte Carlo method, risk analysis is performed using models of possible outcomes where any factor that is characterized by uncertainty is replaced by a probability distribution. Some types of distributions such as normal distribution is used less frequently, because their use requires special knowledge in the field of mathematics. In this paper, the aim is to formalize the normal distribution for use of non-specialists in mathematical statistics. Object of study is the risk assessment of investment projects. Subject of study is the normal distribution formalization for investment project evaluation. As the result the formulas for investment project variables and the form for normal distribution formalization in MS Excel are proposed. The empirical result is an experiment, which identify a pseudo-random numbers sequence as normally distributed. It facilitates the work of an expert and allows him to use the normal distribution variables correctly. JEL classification:C12, C53, E22
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