Accounting
Ali mohammad Ghanbari; Hamid Jamshidi
Abstract
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 ...
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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.
Accounting
Ali mohammad Ghanbari; Amin Pourfarzad
Abstract
Designation of incentive mechanisms to increase the speed of liquidity of receivables is one of the leading options for streamlining domestic resources in the field of oil and gas industry financing. The main objective of this research is to investigate and optimize the liquidity methods of gas sales ...
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Designation of incentive mechanisms to increase the speed of liquidity of receivables is one of the leading options for streamlining domestic resources in the field of oil and gas industry financing. The main objective of this research is to investigate and optimize the liquidity methods of gas sales receivables in line with the requirements of the National Iranian Gas Company. After a deep review of literature, we extract methods and legal platforms for receivables liquidation methods. The current research is an applied and descriptive-analytical research in terms of its purpose and methodology. The data analysis is a combination of quantitative and qualitative methods and required data for this study including the amounts and duration of deferred receivables by different categories of subscribers of the NIGC during 2012 to 2018. Subsequently, in order to visualize the data in the form of heat tables, Microsoft's Business Intelligence software is applied to understand the process of creating and settlement of receivables, cluster receivables by the different categories of subscription and, finally, reveal the subscriber behavior patterns in disbursements. Finally, using theoretical foundations and experts' opinions, a set of optimal methods has been recommended to accelerate the process of collecting current receivables, depending on the behavior of different groups of subscribers. Based on the results of the interviews, NIGC should apply IT-based methods, incentive policies and deterrent (punitive) strategies to accelerate the liquidation of receivables in household and commercial subscription.
Accounting
Seyed Mohammad Javadi; Ali Mohammad Ghanbari; Arsalan Anisi
Abstract
The purpose of this study is to evaluate the financial performance of provincial gas distribution companies as affiliates for National Iranian Gas Company. To this end, we identified financial performance indicators in accordance with the requirements of NIGC through a review of theoretical foundations ...
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The purpose of this study is to evaluate the financial performance of provincial gas distribution companies as affiliates for National Iranian Gas Company. To this end, we identified financial performance indicators in accordance with the requirements of NIGC through a review of theoretical foundations and interviews with a number of financial and planning experts and then prioritized using the Analytical Hierarchy Process method. These indicators lied in four groups of liquidity, capital structure, profitability and activity criteria, respectively. Then, the weighted indicators were analyzed using TOPSIS technique in Expert-Choice software and the final ranking of the companies was provided. The results showed that based on the identified criteria, provincial gas companies of Hormozgan, Yazd, Markazi, and Kermanshah had a favorable financial performance and Ilam, Mazandaran, Chaharmahal and Bakhtiari and Zanjan provincial gas companies had a weak financial performance with respect to other companies in three years under review and some suggestions have been made in this regard.
