درخت حوزه‌های تخصصی

Data mining is known as one of the powerful tools in generating information and knowledge from raw data, and Clustering as one of the standard methods in data mining is a suitable method for grouping data in different clusters that helps to understand and analyze relationships. It is one of the essential issues in the field of investment, so by using stock market clustering, helpful information can be obtained to predict changes in stock prices of different companies and then on how to decide the correct number and shares in the portfolio to private investors and financial professionals' help. The purpose of this study is to cluster the companies listed on the Tehran stock exchange using three methods of K-means Clustering, Hierarchical clustering, and Affinity propagation clustering and compare these three methods with each other. To conduct this research, the adjusted price of 50 listed companies for the period 2019-07-01 to 2020-09-29 has been used.  The evaluation results show that the obtained silhouette coefficient for K-means Clustering is higher and, therefore, better than other methods for stock exchange data. In the continuation of the research, calculating the co-integration of stock pairs that have the same co-movement with each other were identified, and finally, clusters were compiled using the t-SNE method.

Iran attempts to expand the non-oil exportsfor diminishing the dependency on oil export income. This research tries to examine the export and trade balance of Iran's agricultural sector. Accordingly, the gravity model was used for export, applying panel data from 1997 to 2017. Also, the trade balance of Iran's total agricultural and the related sectors’commodities was examined. It should be noted that for the trade balance, time series data from 1978 to 2018 were used. The results of the gravity model show a negative effect for the variable of distance. The coefficients of Iran’s per capita GDP and also the GDP of trading partners are positive, as expected. It was found that a one percent increase in the per capita GDP of Iran causes a rise of 3.42 percent in the export of agricultural products; however,that of importing countries haslow statistical significance. Based on the coefficient obtained for the population, an increase in the population of the importing countries raises the demand for Iran's agricultural products.The degree of trade openness revealed a positive and significant effect on the export of agricultural products. The coefficient for the real exchange rate was found to be around 0.9 percent. It was also found that the volatility of the exchange rate is related directly to the export of agricultural products. Comprehensive sanctions have a negative and significant effect, while less restricting sanctions have an insignificant effect on the export of agricultural products. The global economic crisis has also had a dampening effect on exports. For trade balance, the results show that the value added of the agriculture has a positive effect on the trade balance of entire agriculture and sectors. The real exchange rate has a negative effect on the trade balance of agricultural commodities as a whole and livestock and agronomy sectors, confirming the J-Curve theory while it was not supported for the horticultural sector. Also, the variable of exchange rate volatility was included in the model using two measures of positive and negative series of exchange rate changes and the Autoregressive Conditional Heteroskedasticity (ARCH) effect, but their effect on the trade balance was not the same in terms of both the direction and statistical significance. The trade openness for the agricultural and horticultural sector was found with a positive coefficient, indicating that their production is based on comparative advantage. However, for the sectors of agronomy and livestock, it illustrated a negative effect. Sanctions have also harmed the trade balance.