List of Articles Macroeconomics

• Open Access Article
  • Abstract Page
  • Full-Text
  
  1 - Credit Risk Test Stress Model of the Banking Industry under Macroeconomic Scenarios
  mohsen Ziaee Bidhendy Mehrzad Minooee Mirfaz Fallah shams
  The main reason for conducting the present study is to design and explain the credit crunch risk test model of the banking industry under macroeconomic scenarios. In addition to the use of documents and reports related to the banking industry, the panel data related to More

  The main reason for conducting the present study is to design and explain the credit crunch risk test model of the banking industry under macroeconomic scenarios. In addition to the use of documents and reports related to the banking industry, the panel data related to the annual reports and datasets of the banking industry were used. In the present study, in order to perform econometric analyzes, E-Views software was used and Matlab artificial intelligence environment was used to design an intelligent system. Then, based on the GARCH method, the regression statistics related to the GARCH model for the fluctuations between the research objective function and GDP growth rate, interest rate, unemployment rate, inflation rate and per capita income growth rate are calculated equal to 0.927, which indicates very high predictive power. The econometric model of research is. One of the most important results of the present study is that according to the calculations performed, the bank's credit portfolio to reduce the probability of default is exactly 91 percent (the fifth level of system output is excellent). Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  2 - Designing and presenting a model to determine the effect of macroeconomic and banking variables on the occurrence of asset freezing in the country's banking system
  Fateme Davoudi Farkoosh mohammad ebrahim Mohammadpoor zarandi mehrzad minouei
  In this article, the goal is to design and present a model to determine the effect of macroeconomic and banking variables on the occurrence of asset freezing in the country's banking system using meta-heuristic models. The current research is applied in terms of purpose More

  In this article, the goal is to design and present a model to determine the effect of macroeconomic and banking variables on the occurrence of asset freezing in the country's banking system using meta-heuristic models. The current research is applied in terms of purpose, in terms of research method, correlation analysis type and in terms of overall research design, post-event and retrospective. In order to answer the research questions, the annual data of macroeconomic and banking variables, during the period of 1399-1390, were collected and using the test of regression models in EViews, Smart PLS software and also the neural network model. It was estimated in SPSS Modeler software. The estimation results of the regression model of the first hypothesis in EViews software showed that the economic variables of GDP, unemployment rate and interest rate, consumer price index, currency strength at the error level of one percent and the economic growth rate variable at the error level of ten percent have a significant relationship. They have a dependent variable (asset freezing). Also, the estimation results of the structural model of the first hypothesis in the PLS software are significantly aligned with the output of the Eviuse software. So; The first research hypothesis is confirmed. Also, the results of the regression model estimation of the second hypothesis in EViews software showed that the intra-bank variable of the bank size ratio, return on equity, and the amount of liquidity at the error level of ten percent, and the variables of capital adequacy, return on assets, bank capital, at the error level of one percent. The percentage has a significant relationship with the dependent variable (asset freezing). Also, the estimation results of the structural model of the second hypothesis in the PLS software are significantly aligned with the output of the Eviuse software. So; The second research hypothesis is also confirmed. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  3 - Anticipation of Iran Mercantile Exchange (IME) gold coin price using Artificial Neural Network Approach with GMDH Algorithm
  عباس معمار نژاد وحید فرمان آرا
  The economy of every country is composed of different sectors in which, the relationship amongst them determines the dimensions of the economy of that country. The capital market together with money market make up the financial market as the main arteries of an economy. More

  The economy of every country is composed of different sectors in which, the relationship amongst them determines the dimensions of the economy of that country. The capital market together with money market make up the financial market as the main arteries of an economy. Their operation has a significant influence on the growth and development of the economy. In cases where there is no constructive relationship between the financial market and economic sectors, economic performance might be subject to distortions. The stock market as a fundamental pillar of the financial market plays a crucial role in facilitating investments in the capital market. Given the importance of expectations in different economic fields, the main purpose of this study, as its title explains, is to anticipate of Iran Mercantile Exchange (IME) gold coin price Therefore, after a brief review of dominant economic theories, a new method, artificial neural network GMDH, is used to forecast the impact of macroeconomic variables( including the rate u.s. dollar as foreign exchange, the price of gold coin, the price of gold and oil in termes of dollar, the over-all index of stocks, the delivery date of gold coin) on the gold coin price. The GMDH Algorithm is a nonlinear model to anticipate complex systematic relationships between variables of the model. The special feature of this deductive algorithm is recognition and screening of the most effective variables to estimate the model with training samples and omit the non-significant ones from the simulation process with testing samples. So, an attempt is made to solve the model via iterative methods to minimize the typical standard Error like RMSE, MAPE, and so on. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  4 - Classification the Economic Entropy Index in a Macroeconomic Model
  Behrouz Sadeghi Amroabadi mohsen renani
  10.30495/eco.2021.1910638.2430
  The aim of this paper is the classification the economic entropy index in a macroeconomic model. Therefore, the descriptive-analytical methodology and econophysics and systems theory were used. The results show that the economic entropy was divided in four sections, sho More

  The aim of this paper is the classification the economic entropy index in a macroeconomic model. Therefore, the descriptive-analytical methodology and econophysics and systems theory were used. The results show that the economic entropy was divided in four sections, shock entropy, respiration entropy, sleep entropy, and entropy of waste. Increasing the economic entropy index due to the scarcity of environmental resources, predicts the likelihood of an economic catastrophe. This will not only bring about economic growth faces serious problems, but the environment poses a serious problem as a place of residence. According to the results, reducing the instabilities and external stresses to reduce shock entropy, developing the appropriate rules for firms to reduce respiratory entropy, policies based on the reduction of physical and social waste to reduce the entropy of waste and policies for the use of production spaces that are not currently in use to reduce sleep entropy recommended. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  5 - Company value prediction based on deep learning methods
  Seyedeh Maryam Babanezhad Bagheri Abbasali PourAghajan M. Mehdi Abbasian Feridoni
  10.30495/fed.2023.705603
  Abstract Prediction and clear understanding of the behavior of a phenomenon plays a major role in adopting strategies and decisions. All-round development and deepening of the capital market as the driving engine of economic development requires the public trust of par More

  Abstract Prediction and clear understanding of the behavior of a phenomenon plays a major role in adopting strategies and decisions. All-round development and deepening of the capital market as the driving engine of economic development requires the public trust of participants in its efficiency and correctness in determining the fair price of securities. On the other hand, predicting company value, price fluctuations, or stock returns is very important in portfolio selection, asset management, and even stock pricing of newly listed companies.In this research, using the data of 159 companies during a 10-year period including 2011-2020 and the factors affecting the company's value, including financial ratios, corporate governance mechanisms, macroeconomic factors, and the stock market, the company's value has been predicted. In this research, two structures of deep learning methods including GRU and BLSTM are used for better evaluation. The results of examining the data collected using deep learning techniques indicated that the combined model with a lower RMSE error than the GRU model predicted the value of the company. Manuscript profile