Oil and gas industry

Recent theories of firm dynamics emphasize on the role of financial variables as determinants of firm growth. Most of the technical literature shows that there is a positive relationship between financial leverage and firm growth. The purpose of this paper is to examine whether such relationship exists among oil and gas companies within the Organization of the Petroleum Exporting Countries (OPEC). Data were collected from the selected members of the OPEC. The collected data was then analyzed using the Arellano and Bond (1991) GMM method and Sargan test. The results showed a significant and positive relationship between financial leverage and firm growth which is in line with the technical literature. This research contributes to the body of knowledge by examining a specific and important sector within several different countries. It shows the current theory is not affected by industry or country.

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.

Project portfolio management is a comprehensive framework for decision making and selecting the portfolio of projects to achieve the goals of the organization by considering resource constraints. The importance of this issue in Iran's oil and gas industry is even more remarkable than ever due to its unique position in the country's economy, capital-intensive and capital budget constraints that have been intensified in recent years. Identifying and defining different scenarios for each oil and gas field, determining the parameters of the mathematical model, the required data to calculate the parameters of the model and the process and methods of identifying this data, indicate the distinction and necessity of this research. This study is an applied research in terms of objective, using mathematical modeling approach, has provided a pattern to determine the optimal portfolio of capital plans of oil and gas companies. The research method is case study which has studied one of the most important oil and gas producing companies in the country and the only offshore company. In this study, a framework for selecting the optimal portfolio of capital projects is determined and after gathering required data, the zero-one integer linear mathematical programming model with the objective function of maximizing the net present value from fields (as the strategic goal of company) by considering investment constraints was designed and solved by GAMS software. Finally, according to the defined constraint, the best investment mode for each field was identified and the optimal portfolio was defined.

One of the innovations that has been formed in the insurance industry in recent years is transfer the risk to the capital markets. Today, this possibility is provided by issuing insurance bonds and Catastrophe bonds, which are a most important type of insurance-linked securities (ILS), can redress inefficiency in the insurance industry.Today, more and more Catastrophe (CAT) bonds are being issued worldwide, which is welcomed by investors and insurance companies. On the other hand, traditional insurance solutions to cover the risks of Iran's oil and gas industry is not efficient and sufficient and using CAT bonds to transfer risks of this industry to capital markets is a necessary and inevitable issue.The aim of this research is to identify effective factors for issuing Catastrophe bonds in Iran's oil and gas industry. On this basis and after reviewing the literature through library studies, 33 factors were identified in the form of seven categories, based on the similarities. Then, based on Delphi method, experts were asked to express their opinions through an iterative questionnaire. After take the experts' opinions in every round, the statistics analysis was performed and the Delphi process was stopped in the third round. Based on the results, the number of 32 factors in six categories with the titles Legislation and Amendment of the Rules, Knowledge Management, Process Management, Transparency, Creation and Strengthening of Software Platforms and Cultivation were approved by experts and identified as effective factors for issuance of Cat bonds in Iran's oil and gas industry.