One of the important indicators that characterize the economy of the country is the business confidence index. It is the basis for tracking the cycles of economic dynamics and analysis of the country's business climate. Evaluation of this indicator makes it possible to predict the crisis phenomena that are occurring in the economy, and to develop possible ways out of difficult situations. On the example of the five countries (Ukraine, Germany, Hungary, Slovenia, Poland) it was also analyzed the possibility of constructing the business confidence index based on economic indicators, which characterize current economic activity of the country. For analysis, the quarterly values of economic indicators over the last years were taken. The selected economic indicators based on cross-correlation analysis were ranked into three groups: coincident, lagging and leading indicators. Using coincident and leading economic indicators, the several regression models of the business confidence index were built. On the basis of the obtained regression models, the forecast of business confidence index value for the next period is evaluated and the trends of its development are established.

The prospects for doing business in countries are also determined by the business confidence index. The purpose of the article is to model trends in indicators that determine the state of the business climate of countries, in particular, the period of influence of the consequences of COVID-19 is of scientific interest. The approach is based on the preliminary results of substantiating a set of indicators and applying the taxonomy method to substantiate an alternative indicator of the business climate, the advantage of which is its advanced nature. The most significant factors influencing the business climate index were identified, in particular, the annual GDP growth rate and the volume of retail sales. The similarity of the trends in the calculated and actual business climate index was obtained, the forecast values were calculated with an accuracy of 89.38%. And also, the obtained modeling results were developed by means of building and using neural networks with learning capabilities, which makes it possible to improve the quality and accuracy of the business climate index forecast up to 96.22%. It has been established that the consequences of the impact of COVID-19 are forecasting a decrease in the level of the country's business climate index in the 3rd quarter of 2020. The proposed approach to modeling the country's business climate is unified, easily applied to the macroeconomic data of various countries, demonstrates a high level of accuracy and quality of forecasting. The prospects for further research are modeling the business climate of the countries of the world in order to compare trends and levels, as well as their changes under the influence of quarantine restrictions.

The behavior of the socioeconomic agents of the foreign exchange market has always been characterized by insufficient predictability, therefore today researchers do not leave the search for new methods and indicators for their prediction. With the development of the information society and online technologies, one of the manifestations of the behavior of agents in the foreign exchange market has become their semantic-thematic online queries in search engines. Considering that the time series of exchange rates have been sufficiently studied, insufficient attention has been paid to the dynamics of the frequency of requests for exchange rates as to the parameters of the online behavior of foreign exchange market agents. Since the dynamics of the frequency of requests for exchange rates (dollar, euro) is non-linear, a recurrence analysis was applied. The results of this analysis proved that the tendencies of informational activity of agents are characterized by an antipersistent type of behavior with periodicity and the presence of drift, which are manifested in varying degrees for each exchange rate. If for the indicator of the dollar exchange rate a concentration of interest of market agents near the trajectory of a certain attractor with other deviations due to other factors is shown, then a similar indicator of the euro exchange rate is characterized by a more pronounced and stable over time cyclical manifestation of information activity of agents. In further researches, it is planned to implement a quantitative analysis of the constructed recurrence plots in order to take into account the behavior of the agents of the foreign exchange market.

To date, the country's business climate characterizes the state of economic development and the results of its effectiveness. One of the key indicators that determine the business climate is the business confidence index (BCI). The paper proposes an approach to modeling the business climate of the country, which is based on the principles of information transparency, and makes it possible to assess the development trends of the studied indicator. The proposed approach is based on the taxonomy method, which allows one to identify the measure of influence of each factor included in the model and exclude the influence of the subjective assessment of the researcher. This approach has been tested on the example of Ukraine. The quarterly values of socio-economic indicators for the past twelve years (2008-2019) were taken as input data. Based on the correlation analysis, from the generated array of incoming data, only those indicators were selected that have a significant relationship with the business confidence index. It has been established that the GDP annual growth rate and retail sales have the greatest impact on the business confidence index. A forecast has been built for the trend of changes in the business confidence index (forecast accuracy of 87.55%), which proves the similarity of development trends in the country's business climate. The results obtained make it possible to analyze the cyclical development of the country's economy with high accuracy and reliability.

One of the important leading indicators that show the current state of the country's economic cycle, its prospects in the near future and generally characterize the country's business climate is the business confidence index (BCI). In this paper a forecast of the business confidence index is built using neural network technology using the example of the four European countries (Germany, Hungary, Poland, Slovenia).Forecasting was carried out taking into account economic indicators that characterize the socioeconomic situation in the country. The selected economic indicators based on correlation analysis and have significant relationship with the business confidence index. For forecasting, the quarterly values of economic indicators for the last 12 years (2007-2018) were taken. Also the accuracy of the obtained forecasts was also assessed and the trends of the business confidence index development are established. It was found that the accuracy of the obtained forecasts is high. It has been proven that level of the BCI in the next period will decrease by 0.41% in Germany, by 1.05% in Hungary, by 0.99% in Poland and will remain unchanged in Slovenia. The obtained forecasting results make it possible to predict changes in the business climate in a country earlier than other macroeconomic indicators. The perspectives of the research are the elaboration of a country's business climate development strategies, based on the results of the BCI forecasting.