درخت حوزه‌های تخصصی

Corporate Governance debates raised seriously in scandals and financial crisis of recent decades in large companies and banks. The focus of this debate is not merely the corporate governance system, and its main function is to preserve the interests of stakeholders. At the macro level, economic efficiency, sustainable growth and financial stability are important effects of the system. Establishing an efficient corporate governance system in the banking system and evaluating and ranking them requires a comprehensive model that encompasses all the internal and external mechanisms affecting corporate governance and localized indices and relevant international principles. In this research, corporate governance in banks has been evaluated via a model developed in the framework of systematic thinking which includes three dimensions 1) Inputs (structures and inputs), 2) Roles and processes, and 3) Outputs (goals, performance and results of them). The main objectives of this study are to evaluate the compliance status of 27 banks with the indicators of corporate governance model and their efficiency and effectiveness, as well as to examine the correlation between the components of corporate governance within the system including inputs, processes and outputs. According to the results, 47.8% of the qualitative indicators are covered in the current situation of the banks of the country, but there is a significant gap between the full compliance with the model. Based on the results in the performance sector, private banks scored the highest and state banks scored the lowest. The results of evaluating the correlation between the components of corporate governance model showed that there is a positive and significant relationship between the dimensions of inputs and processes with the goals, results and performance of banks. Accordingly, by improving the status of input indicators and the corporate governance model process, the status of target indicators, results and performance of banks will also improve

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.