Comparison of the impact of lending and inflation rates on economic growth in Vietnam and China

This study aims to investigate the role of Fintech in predicting the spread of COVID-19 based on consumers’ Fintech perceptions and behavior before and after the outbreak of COVID-19. The study used a questionnaire-based survey distributed in different countries of the world using the LinkedIn platform for this purpose to reach the targeted population. The snowball sampling technique was used. The study targeted consumers with Fintech experience, especially in digital payments services. 507 samples were retrieved. For the analysis, the Structural Equation Modeling (SEM) was used. The study revealed novel results in predicting COVID-19 spread; these three variables (Fintech Behavior before COVID-19, Fintech Behavior after COVID-19, and Fintech Perception after COVID-19) could predict 52.5% of the variance in the dependent variable (COVID-19 Spread) (R² = 0.525, p < 0.05). The findings show that Higher Fintech perception and behavior among Fintech users will help in reducing the spread of COVID-19 by avoiding the use of contact payment methods. Contactless payment methods are the main tools in Fintech that might help in avoiding the probability of COVID-19 spread. Consumers’ Fintech perceptions and behavior are the most influencing factors that could predict the spread of COVID-19 in this study, where digital payments are the main concern. It is recommended that consumers adopt digital payment methods and tools, especially contactless payment methods, to fulfill their financial services. Other researchers are also encouraged to use the same model to predict the spread of this virus in the Fintech context.

The article analyzes the influence of inflation on economic growth and substantiates the main directions of increasing the effectiveness of the central bank's anti-inflation policy.
In order to determine the limit of inflation, the excess of which has a negative impact on the economic growth, the relationship is analyzed between the inflation rate and the real GDP growth rate on the basis of IMF statistics using the example of 158 countries. It was determined that in 2010–2017, in the global economy, the 6.0% inflation was the marginal value of the inflation rate, beyond which the economic growth rate declined or slowed down.
Given the inverse relationship between the inflation rate and the real GDP growth rates as well as empirical calculations for the period 1996–2017, the threshold for inflation rate for Ukraine at the level of 4.51% was determined based on empirical calculations for the 1996–2017 period. The results indicate that the National Bank of Ukraine set the inflation target above the level of the calculated threshold inflation.
It has also been established that the link between the rates of nominal GDP growth, as opposed to real GDP, and the inflation rate, is more direct and more tight. It is substantiated that to analyze such dependence it is better to use GDP deflator instead of CPI. The results indicate that deflation constrains economic growth much less than inflation.
In order to increase the effectiveness of the central bank’s pro-cyclical monetary policy aimed at supporting economic growth, the relationship between the rates of real GDP growth and the indicator characterizing the gap between the growth rates of M3 and inflation, which actually reflects the real money supply dynamics, is determined. The results obtained allowed to conclude that in 2009 and 2014-2017, the artificial “squeezing” of the money supply took place in Ukraine, resulting in a decrease in the level of the economy monetization by 22.0% in 2017 compared to 2013.
It has been proved that in order to minimize the negative impact of inflationary processes on economic growth, the policy of the National Bank of Ukraine should be aimed at eliminating the artificial squeezing of the money supply through a reasonable increase in the economy monetization and the implementation of an effective monetary policy.

The aim of the article was to find out the optimal capital structure of the companies in relation to their maximum performance. To reach this aim, the data of the companies operating in the field of heat industry of the Slovak Republic were used. As the first method, a correlation matrix was applied. It was found out that there is statistically significant relationship between capital structure indicators and performance of the companies. Due to the lack of data in time series, the authors were not able to apply multiple regression model to assess the impact of these indicators on performance. Therefore, a method of modelling was used to analyze the impact of the change in capital structure on performance. Modelling was based on the principle of a gradual change in the capital structure in favor of debt. By the increase in debt, it was confirmed that there was a change in the values of selected indicators. In the course of analysis, it was confirmed that the value of EVA equity increased with the rising indebtedness by which the proposition I of the modified MM theory was supported. The performance expressed by EVA entity indicator is at its minimum when the capital structure is 90:10 in favor of equity. By increasing the debt, EVA entity rises. Based on these results, it can be stated that the performance of selected companies increases when the share of debt also rises, even when taking into account the rising financial risks.