Issue #1 (Volume 18 2021)

Private equity (PE) stands out significantly in the world as one of the main development tools of the capital market in emerging economies and alternative sources of finance for companies. Particularly, the increase in fund value and continuous returns are objects of intense study in Brazil. The paper aims to find determinants to Brazilian private equity returns, regarding three relevant variables funds characteristics and GDP to a macroeconomic view. A sample of 1,112 PE funds registered at the Brazilian Securities and Exchange Commission (CVM) was used and analyzed by three main variables: period of establishment, equity size, and exclusivity as possible determinants of funds’ performance using multiple regression model and fourth variable GDP is applied as a descriptive variable. The results indicate that older funds had a return premium of 1.5% monthly over young funds, smaller funds had a return premium of 1.4% over larger funds, and exclusivity does not influence the funds’ performance. Thus, the paper provides a basis for the relevant factors that an investor should verify in Brazil’s private equity fund before allocating the resources.

The virtual nature of digital money is fueling the conflict between usability, functionality and trust in the digital form. Institutional trust drivers should move forward in understanding the nature of confidence in digital money. Do central banks digital money (CBDC – central bank digital currency) and private cryptocurrencies demonstrate the same or different trust patterns? The paper used the general regression method to discover the relationship between trust in different forms of digital money and selected variables that may generate this trust. Simple empirical tests were sufficient to find the fundamental importance of age as a confidence driver relevant to CBDC and cryptocurrencies. It is found that traditional factors associated with the inflation history and quality of monetary order (central banks independence and rule of law) do not play a role in the case of CBDC, but are important in the case of cryptocurrencies. Structural features (like FinTech development or social trust) that should support trust in digital money are not found to be important. Societies with larger fraction of younger generations demonstrate higher confidence in centralized and decentralized forms of digital money. This challenges the traditional approach to money and calls into question the future role of monetary stability institutions in the digital age. Digitalization is perceived as an improvement in welfare only when fiat money institutions become fragile. The efficiency and credibility of central banks are not a bonus to confidence in CBDC. This is a challenge for the institutional design of the future digital-based monetary order.

This study aims to examine whether the capital structure and several factors have significant influences on firm value in Vietnam. To achieve this objective, 435 non-financial listed companies have been selected from 2012 to 2019 on Vietnamese stock exchanges. Four groups of firms continue to be chosen from the total to investigate the differences in the outcomes among industries. The results altogether using the GMM method show that the impact of capital structure and other control variables on firm value is significant, yet different across industries: capital structure has a significant positive impact on firm value in the food and beverage industry, but has a significant negative effect on the value of the firm in wholesale trade and construction, as well as real estate industry, while has an insignificant influence on enterprise value considering all industries. Apart from the firm size, the impact of other control factors on firm value also indicates mixed results.

Many investors in order to predict stock prices use various techniques like fundamental analysis and technical analysis and sometimes rely on the discussions provided by various stock market analysts. ARIMA is a part of time-series analysis under prediction algorithms, and this paper attempts to predict the share prices of selected pharmaceutical companies in India, listed under NIFTY100, using the ARIMA model. A sample size of 782 time-series observations from January 1, 2017 to December 31, 2019 for each selected pharmaceutical firm has been considered to frame the ARIMA model. ADF test is used to verify whether the data are stationary or not. For ARIMA model estimation, significant spikes in the correlogram of ACF and PACF have been observed, and many models have been framed taking different AR and MA terms for each selected company. After that, 5 best models have been selected, and necessary inculcation of various AR and MA terms has been made to adjust the models and choose the best adjusted ARIMA model for each firm based on Volatility, adjusted R-squared, and Akaike Information Criterion. The results could be used to analyze the stock prices and their prediction in-depth in future research efforts.

According to the current situation in the world economy connected with the coronavirus pandemic, it is difficult to predict GDP growth. Non-economic factors determine the rate of decline in economies of almost all countries. Accordingly, it is extremely difficult to ensure the stable functioning of financial systems. In this situation, the role of public finance, especially the state budget, significantly increases, given the peculiarities of the formation of different levels’ budgets. This research aims to evaluate state budget structural changes on the example of Ukraine. Based on the linear coefficient and the quadratic coefficient of absolute structural changes, the quadratic coefficient of relative structural changes, and integral coefficients of structural changes the authors analyzed the state of public finance in Ukraine since the formation of the state and local budgets and their optimal use to mitigate the effects of the pandemic on the economy can become one of the factors in maintaining financial stability and developing anti-crisis measures. The forecast values of the growth rate of budget revenues and expenditures confirm that the projected revenue gaps are significantly higher than the projected expenditure gaps. The cost structure of the state budget of Ukraine is characterized as a structure with a low level of differences. The Gatev and Ryabtsev coefficients demonstrate unidirectional dynamics. In contrast, Salai coefficient shows the opposite dynamics, which confirms a lack of stability in the cost structure. From 2008 to 2019, the chain rate of change has a significant variation range.