In order to identify the determinants that influence the credit risk of Peruvian municipal savings banks, this quantitative research uses a nonexperimental design and a longitudinal sample to analyze monthly data corresponding to macroeconomic variables and microfinance institutions’ internal variables from 2011 to 2020. Using multiple linear regression, the results show that the interest rate, unemployment rate, and liquidity ratio positively influence the credit risk of Peruvian municipal savings banks; the study also shows that gross domestic product, efficiency of administrative expenses, solvency, and coverage of provisions exert a negative influence on credit risk. It is concluded that seven of the eight independent variables studied influence the credit risk of Peruvian municipal savings banks; only the inflation variable does not significantly influence credit risk.

In this article, the research team uses the VAR self-regression vector model to evaluate the impact of exchange rates on inflation and economic growth in Vietnam over the period 2005-2018. With six endogenous variables included in the VAR model: bilateral real exchange rate (Er), money supply (M2), exports (X), imports (IM), GDP at 2010 comparative prices (GDPR), the consumer price index (CPI) and the two exogenous variables, international price (Pw) and US Federal Reserve interest rate (Ifed), the research team examines the impact of exchange rates on endogenous variables in the model and considers the reaction of inflation and economic growth on various shocks. Based on the quantitative results, the research team will recommend some discus-sions to contribute for the improvement of Vietnam's macro environment, trade balance, inflation control, and economic growth support; implementing the goal of macroeconomic stability to suit the period of international economic integration and improving national competitiveness.

This paper attempts to predict financial distress companies in the manufacturing and non-manufacturing sectors in Malaysia using financial distress companies as the dependent variable and financial ratios and macroeconomic variables as the independent variables. Logit Analysis was used as the analysis procedure because ratios do not have to be normal if it is used. It is also suitable when the dependent variable is binary in nature. Furthermore, it can also provide the probability of a company being financially distressed. This study found that the independent variables that can be used to predict financial distress companies in the manufacturing sector in Malaysia were total assets turnover ratio, current ratio, net income to total assets ratio and money supply (M2). However, the independent variables that can be used to predict financial distress companies in the non-manufacturing sector in Malaysia were debt ratio, working capital ratio, net income to total assets ratio and money supply (M2). This study provides the prediction models of financial distress com-panies in the manufacturing and non-manufacturing sectors in Malaysia using financial ratios and macroeconomic variables as its independent variables.

This article evaluates the relationship of macroeconomic variables of the domestic market with the stock index on the Moscow exchange and selects forecast specifications based on an integrated autoregressive model - the moving average. The methods used are included in an integrated autoregressive-moving average model with exogenous variables and seasonal component, Box and Jenkins approach, auto.arima in R function, Hyndman and Athanasopoulos approach, and maximum likelihood method. The results demonstrate that the inclusion of external regressors in the one-dimensional ARIMAX model improves its predictive characteristics. Time series of macro-indicators of the domestic market – the consumer price index, the index of output of goods and services for basic activities are not interrelated with the index of the Moscow exchange, with the exception of the dollar exchange rate. The positive correlation between the Moscow exchange index and macro indicators of the world economy - the S&P stock index, the price of Brent oil, was confirmed. In models with minimal AIC, a rare presence of the MA component was found, which shows that the prevailing dependence of the stock market yield on previous values of the yield (AR component) and thus, better predictability of the yield. It has shown that for stock market forecasting, "manual" selection of the ARIMA model type can give better results (minimum AIC and minimum RMSE) than the built-in auto.arima algorithm in R. It is shown that from a practical point of view, when selecting forecast models, the RMSE criterion is more useful for investors, which measures the standard error of the forecast in points of the stock index. For the scientific novelty, using Russian financial data for the period from March 2000 to March 2018 to measure the connection of macro indicators of domestic and global markets with the Moscow exchange stock index, considering seasonality can be noticed. The comparison of the forecast model’s accuracy of the ARIMA type obtained by automatic and "manual “selection by AIC and RMSE is performed in favor of "manual" selection. It could be noted that the main conclusions of the article can be used in scientific and practical activities in the stock markets as a practical significance.

The purpose of this study is to analyze the factors influencing non-performing loans in companies listed on the Indonesian Stock Exchange Banking sector. All banks in Indonesia carefully review their Non-Performing Loans. According to the Central Bank regulations, the non-performing loan is at a maximum of 5%. Exceed the percentage; there will be one of the indications that the bank is experiencing difficulties and could potentially endanger business continuity. The researchers use the micro-economic and several macro-economic variables to predict the influencing factors toward the non-performing loan. Microeconomic variables studied are the ratio of bank capital to assets (CAP), the loans to deposits (LTD) ratio, the return to assets (ROA) ratio and the ratio of return to equity (ROE). Macroeconomic variables are the ratio of public sector debt to gross domestic product (DEBT), the surplus or deficit of the government budget to gross domestic product (FISCAL) ratio, the percentage increase in gross domestic product (GDP), annual inflation rate (INFL), and percentage of job seeker level (UNEMP). Researchers used some regression methods to analyze the results and the samples taken by researchers were companies listed in the banking sector during the period 2014-2017, and macroeconomic data in Indonesia during that year.

Measuring different risk factors such as credit risk in banking industry has been an interesting area of studies. The artificial neural network is a nonparametric method developed to succeed for measuring credit risk and this method is applied to measure the credit risk. This research’s neural network follows back propagation paradigm, which enables it to use historical data for predicting future values with very good out of sample fitting. Macroeconomic variables including GDP, exchange rate, inflation rate, stock price index, and M2 are used to forecast credit risk for two Iranian banks; namely Saderat and Sarmayeh over the period 2007-2011. Research data are being tested for ADF and Causality Granger tests before entering the ANN to achieve the best lag structure for the research model. MSE and R values for the developed ANN in this research respectively are 86×?10?^(-4) and 0.9885, respectively. The results showed that ANN was able to predict banks’ credit risk with low error. Sensibility analyses which has accomplished on this research’s ANN corroborates that M2 has the highest effect on the ANN’s credit risk and should be considered as an additional leading indicator by Iran’s banking authorities. These matters confirm validation of macroeconomic notions in Iran’s credit systematic risk.

Measuring different risk factors such as credit risk in banking industry has been an interesting area of studies. The artificial neural network is a nonparametric method developed to succeed for measuring credit risk and this method is applied to measure the credit risk. This research’s neural network follows back propagation paradigm, which enables it to use historical data for predicting future values with very good out of sample fitting. Macroeconomic variables including GDP, exchange rate, inflation rate, stock price index, and M2 are used to forecast credit risk for two Iranian banks; namely Saderat and Sarmayeh over the period 2007-2011. Research data are being tested for ADF and Causality Granger tests before entering the ANN to achieve the best lag structure for the research model. MSE and R values for the developed ANN in this research respectively are 86×?10?^(-4) and 0.9885, respectively. The results showed that ANN was able to predict banks’ credit risk with low error. Sensibility analyses which has accomplished on this research’s ANN corroborates that M2 has the highest effect on the ANN’s credit risk and should be considered as an additional leading indicator by Iran’s banking authorities. These matters confirm validation of macroeconomic notions in Iran’s credit systematic risk.