Macroeconomic Variables

Evaluation of stock portfolio performance is considered one of the important issues in the capital market and investment management in stocks. Proper evaluation of portfolio performance requires recognizing the factors affecting it. The macroeconomic variables are important and effective factors due to affecting the systematic risk of companies. In this research, ordinary least squares method (OLS) was used to evaluate the effect of macroeconomic variables including inflation, interest rate, liquidity growth rate, oil price and currency rate (Rial versus Dollar) on the stock portfolio performance based on traditional and modern network theory. Performance of portfolios including growth portfolio, growth-value portfolio, and value portfolio, and offensive portfolio, indifferent and defensive portfolio was measured based on seasonal data from 2006 to 2016 using the Teriner Index. The research results show that at the error level of 5%, macroeconomic variables have an impact on the performance of both traditional and modern networks. However, the Akaike information criterion for the modern network model is equal to 5.822, which is less than the traditional network value with the value of 6.724. This suggests that the interpretation of macroeconomic variables in a modern network portfolio is better than that of traditional one. In addition, the effect of macro variables on the performance of the six portfolios will be different

This paper seeks to investigate the determinants of banking network profitability in Iran from 2007 to 2012. The results of our study indicate that both bank-specific factors and macroeconomic factors influence banks’ profitability in Iran. Results confirm that bank profitability is significantly influenced by investment to total assets ratio, non-performing loans to total assets ratio, and time deposit to total assets ratio. Among external factors, it turns out that economic growth rate has a significant positive impact on bank profitability. JEL Classifications: C23, G21, M20

The aims of the present study are developing a financial stability index (FSI) using banking indices to measure financial stability in Iran, and examining the relationship between financial stability and macroeconomic variables for policymaking. To these ends, we have employed principal-component analysis, out of sample forecasting, Autoregressive Integrated Moving Average (ARIMA) method, and Vector Error Correction Model (VECM). The monthly data period is spanning 2007:3 through 2017:2. We find evidence of one cointegrating vector. According to the cointegration test, there is a long-run relationship running from inflation, Gross Domestic Product (GDP) growth rate, and unemployment to FSI. Also, the results of the Engle-Granger test indicate bidirectional causality between FSI and unemployment. Forecast evaluation shows that VECM-based FSI prediction is more accurate than the ARIMA model.

The Russian Federation, as an energy-oriented and developing country, has suffered a great large-scale damage under the influence of COVID-19. In the economic and political spheres, the Russian government has adopted various  strategies to reduce the negative effects exerted by the pandemic. On the other hand, Russia is known as the first country to unveil and offer the first Coronavirus vaccine. At the same time, however, the policies pursued by the Russian government have been criticized. This study tries to measure the impact of the pandemic as an exogenous shock on important Russian economic variables. The main question in this study is what effect has the Corona epidemic had on the Russian economy? To answer to this research question, correlation analysis was used to evaluate the relationship between different variables and the number of deaths because of Coronavirus. According to the results of this study, the Coronavirus has affected the process of all Russian economic variables except for economic growth. This highlights the necessity of the implementation of exact plans and policies to maintain a stable economic recovery in the post-COVID era in this country.

Nowadays, maritime trade plays an important role in the economy of some countries, including Iran. Especially, some of the ports in Iran that are located on the Persian Gulf border carry out container shipping operations. Therefore, in the present study, we investigate the impact of the most important macroeconomic factors on the container trading in Iranian ports in the Persian Gulf. For this purpose, we employed the autoregressive distributive lag (ARDL) approach for the estimating effect of oil price, gross national production, industrialization and exchange rate on the container trading volume. The used data are quarterly. Container business data is related to the ports of Shahid Rajaei, Bushehr, Imam Khomeini, Khorramshahr, Shahid Bahonar, which are obtained from the Ports and Maritime Organization. The results of this study show a negative impact of increasing oil price on Iran's container trading. Moreover, our empirical findings indicate that the effect of industrial indicator (INDS) on the container trading volume is positively. Also, one percent increases in gross national production (GNP) leads to %0.37 increase  in container trading, and one percent increase in exchange rate (EXH)  leads to %.17 decrease  in container trading, respectively. Finally, coefficient of ECM is -0.54. It means that speed of adjustment in the function of container trading volume is relatively high, and in each period, 54 percent deviation from long run direction of container trading volume to be corrected by variables of model.