درخت حوزه‌های تخصصی

Iran's Ministry of Petroleum due to empowerment of Iranian companies for executing local mega projects, regional and international markets penetration and upgrading national technology in petroleum upstream industry, predicted a competent Iranian partner called Exploration and Production (E&P) companies, for cooperating with the International Oil Company (IOC) in Article Four of the Cabinet Approval. Now considering the absence of IOCs, it seems that the capacity of other oil contract models to be used by Iranian oil companies should be examined. The purpose of this paper is to investigate the feasibility of Concession agreements’ execution, by an E&P company in Iran’s upstream industry. The research method is descriptive and analytical and governing laws of Iran are reviewed in this study. Since the host government makes the final decision on the conduct of oil operations, it cannot be described as domination of oil resources, and therefore does not seriously contradict the Iran’s Constitution. Furthermore, E&P companies will not be subject to Article 81 of the Constitution. In the Oil Laws, the only restriction on the inflow of foreign capital in the upstream industry of Iran has been observed, which again does not apply to E&P companies. In the Laws of the Five-Year Plans, this restriction of oil laws has been adjusted too much in which it seems that the restriction has also been removed for IOCs. Finally, the investigation shows that there are no major legal barriers in applying Concession agreement in case that the operator is an Iranian E&P company.

The scholars of social responsibility have focused on identifying the factors affecting corporate social responsibility disclosure (CSRD). Corporate social responsibility is a key factor in the survival of any organization. The purpose of the study was to examine the relationship between the size of the company, ownership structure, and governance structure to explain the determinants of Corporate social responsibility in Iran. Regarding this, the data of 127 companies listed on the Tehran Stock Exchange from 2009 to 2018 were collected and analyzed. Multiple regression model as panel data and fixed effects method was used to test the hypotheses. The results from the study tests indicated that the size of the company is an effective factor in Corporate social responsibility in Iran. The role of this factor is positive. Larger companies have been more successful in Corporate social responsibility. Moreover, the ownership structure is a determining factor and has improved the disclosure of social responsibility. The presence of major owners in the company potentially enhances the dissemination of information and protects the interests of minority shareholders, and the corporate governance structure has not been a determining factor in Corporate social responsibility in Iran. Indeed, the board's ability to perform its duties decreases when its size is large.

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.