مطالب

It is generally believed that macroeconomic and financial performance in oil exporting countries is interlinked to oil price movements. Regarding that assumption, the present study aims to examine the impact of oil price movements on bank nonperforming loans (NPLs) ,as a criterion for evaluation of bank credit risk, by applying the Generalized Method of Moments (GMM) on data from 18 Iranian banks data over period 2006–2017. The result of the estimated model indicates that there is a significant relation between fluctuations of oil price and bank nonperforming loans; accordingly, any decrease in the price of oil will result in an increase in bank nonperforming loans. Also, in order to have comprehensive assessment, economic and bank specific control variables were used in the model. Findings show that the NPLs ratio increases as economic growth decreases and exchange rate and real interest rates rise. Among bank specific factors, equity ratio as a criterion for efficiency and loan growth has a negative effect on NPLs, but by raising bank industry concentration, credit risk and financial stability can be threatened. Thus, the reliance of oil rich economies on oil incomes leads to the linkage of oil prices, and macroeconomic and financial performance. Therefore, the result of this study will be useful in adapting and diversifying macroeconomic policies in the face of drastic changes in oil prices and mitigating its adverse effects.

Stock price prediction is one of the crucial concepts in finance area. Machine learning can provide the opportunity for traders and investors to predict stock prices more accurately. In this paper, Closing Price is dependent variable and First Price, Last Price, Opening Price, Today’s High, Today’s Low, Volume, Total Index of Tehran Stock Exchange, Brent Index, WTI Index and Exchange Rate are independent variables. Seven different machine learning algorithms are implemented to predict stock prices. Those include Bayesian Linear, Boosted Tree, Decision Forest, Neural Network, Support Vector, and Ensemble Regression. The sample of the study is fifteen oil and gas companies active in the Tehran Stock Exchange. For each stock the data from the September 23, 2017 to September 23, 2019 gathered. Each algorithm provided two metrics for performance: Root Mean Square Error and Mean Absolute Error. By comparing the aforementioned metrics, the Bayesian Linear Regression had the best performance to predict stock price in the oil and gas industry in the Tehran Stock Exchange.