درخت حوزه‌های تخصصی

The conjecture of stock exchange is the demonstration of attempting to decide the forecast estimation of a particular sector or the market, or the market as a whole. Every stock every investor needs to foresee the future evaluation of stocks, so a predicted forecast of a stock’s future cost could return enormous benefit. To increase the accuracy of the Conjecture of stock Exchange with daily changes in the market value is a bottleneck task. The existing stock market prediction focused on forecasting the regular stock market by using various machine learning algorithms and in-depth methodologies. The proposed work we have implemented describes the new NN model with the help of different learning techniques like hyperparameter tuning which includes batch normalization and fitting it with the help of random-search-cv. The prediction of the Stock exchange is an active area for research and completion in Numerai. The Numerai is the most robust data science competition for stock market prediction. Numerai provides weekly new datasets to mold the most exceptional prediction model. The dataset has 310 features, and the entries are more than 100000 per week. Our proposed new neural network model gives accuracy is closely 86%. The critical point, it isn’t easy with our proposed model with existing models because we are training and testing the proposed model with a new unlabeled dataset every week. Our ultimate aim for participating in Numerai competition is to suggest a neural network methodology to forecast the stock exchange independent of datasets with reasonable accuracy.

Given the growing importance of foreign trade, especially for Iran, as well as the importance of the Commonwealth of Independent States (CIS) , the main purpose of this study is to examine Iran's trade potential with the CIS during the time period 2014-2018. In this regard, by using Allen and Drysdale indices, we have firstly examined Iran's trade similarity with CIS countries. Then, the competitiveness has been evaluated by calculating RCA and Lafay indicators. Finally, based on RCA, Lafay and total trade potential criteria, Iran's trade potential with CIS countries has been measured. According to the study, Iran's trade potential with the CIS region varies between $ 19 billion and $ 30 billion during the time period, and Iran has used only less than 10 percent of its existing trade potential with this region. During the period, Iran has a comparative advantage in more than 400 commoditiy groups, and according to the RCA index, the most important competitive commodity groups are generally primary goods, based on natural resources, agriculture and oil. Also, according to the Lafay Index for 2018, Iran has about 500 commodity groups with a positive index, and there is a significant balance in favor of this group of commodities. Given some significant capacities and advantages over the CIS, Iran should develop its trade with this region, taking into account national interests.