درخت حوزه‌های تخصصی

The most important policymaker entity in the bank-centered economy of Iran is central bank of Islamic Republic of Iran which has always been trying to manage and improve macroeconomic indicators by applying monetary policies. However, investigation of Iran's economy after four decades shows that this country has always suffered from double-digit inflation rates and 15000-fold liquidity throughout this period, while GDP of Iran has grown only by two times during this period. This paper tries to evaluate the effect of monetary policies of central bank on macroeconomic indicators by analytical-descriptive and library method via combining system dynamics and fuzzy multi-criteria decision-making using Vensim and Super Decision software. Monetary policy instruments in this re-search include foreign exchange rate, deposits interest rate, facilities interest rate, required reserve ratio, and open market operations. Further, the macroeconomic indicators include inflation, liquidity, national foreign exchange value, and eco-nomic growth. The results indicated that the most important macroeconomic indi-cators in the country according to economic experts are "national foreign ex-change value" and "inflation". The most important tool for monetary policies of central bank is "foreign exchange rate". Indeed, in order to improve the economic misery index, this bank should take measures to improve the national foreign exchange value, then manage inflation and liquidity, and eventually adjust the banking interest rate

Stock prices are influenced by economic, technological, psychological and geopolitical factors. A review of the literature in this field shows that stochastic approaches, trend analysis and econometrics have been used to demonstrate stock market dynamics and price forecasting. However, these techniques cannot provide a comprehensive overview of market dynamics. Because they ignore the temporal relationship between these factors and are unable to understand their cumulative effects on prices. By integrating chaos theory and continuous data mining based on price acceleration, this study has eliminated these gaps by inventing a new price forecasting method called dynamic stock market recognition simulator and combining two methods: one is delay structures. Or gives time intervals to the data set, and the other is the method of selecting new variables for the market environment. The results showed that the method used can be used to predict the long-term stock price using a small data set with small dimensions.

This paper aims to investigate the portfolio optimization under various market risk conditions using copula dependence and extreme value approaches. According to the modern portfolio theory, diversifying investments in assets that are less correlated with one another allows investors to assume less risk. In many models, asset returns are assumed to follow a normal distribution. Consequently, the linear correlation coefficient explains the dependence between financial assets, and the Markowitz mean-variance optimization model is used to calculate efficient asset portfolios. In this regard, monthly data-driven information on the top 30 companies from 2011 to 2021 was the subject to consideration. In addition, extreme value theory was utilized to model the asset return distribution. Using Gumbel’s copula model, the dependence structure of returns has been analyzed. Distribution tails were modeled utilizing extreme value theory. If the weights of the investment portfolio are allocated according to Gumbel’s copula model, a risk of 2.8% should be considered to obtain a return of 3.2%, according to the obtained results.

It is well documented that using linear models to forecast plenty of financial observations due to their nonlinearity is not satisfactory. Therefore, in this paper, the technical analysis indicators are forecasted using Neural Network Auto-Regressive model with eXogenous inputs (NNARX). Then the effect of different factors (economic, systematic risk, company's properties and corporate governance) on their forecasting error (eRSI, eMA1, eMA2 and eMACD) was investigated. For this purpose, required data were collected using the removal sampling method for 323 companies listed on the Tehran Stock Exchange from 2014-2020. In addition, the mean absolute percentage error (MAPE) was applied to measure the error of forecasting technical analysis indicators. NNARX and dynamic panel data models (GMM) were used to study the effective factors on the error of forecasting technical analysis indicators. Results indicated that the error of forecasting technical analysis indicators is less than 0.1 and has sound accuracy. Also, the company's size and corporate governance indicators didn't significantly affect the error of forecasting technical analysis indicators. In addition, financial leverage doesn't significantly affect eRSI and eMACD but has a significant inverse effect on eMA1 and eMA2. On the other hand, return on assets has a significant inverse effect on eRSI, eMA1, eMA2 and eMACD. Also, economic recession and prosperity, inflation fluctuations, exchange rate fluctuations and systemic risk have a significant positive effect on eRSI, eMA1, eMA2 and eMACD.

Basically, the progressing society without the presence and existence of specialized work resources with strong tendencies towards ethics and professional principles in the work environment will not be able to achieve its lofty goals and experience development and growth in various economic, political and cultural sectors. Based on this, the current research aims to provide a model of professionalism in the system Iran administration was done. The current research is a qualitative research method. Research in terms of the result of the component It is developmental-applied research that the statistical population of this research is 25 elite people identified in the University of Medical Sciences They were from Shiraz, who were interviewed who had at least 10 years of experience and had a master's degree and a doctorate and the snowball sampling continued until the opinions of these experts reached the point of theoretical saturation and direction Conducting research after designing the general questions and formulating the interview process, conducting in-depth interviews with experts became. The data collected in this research were combined using thematic analysis technique. To review the validity of the research findings was presented to the participants and the text of the theory was studied by them and their points of view were applied at the end of this research, it was studied and reviewed by the professors, and some points were made to modify or change the final theory became. Reliability of data by showing the path of researchers' decisions and also placing all the raw data of the analysis done, the codes, categories, the process of studying the primary goals and questions were given to the academic professors and the desired corrections applied. In order to analyze the data, schematic analysis and data base theory were used. In the presented model, and social accountability as causal factors, organizational conditions as background factors, requirements and necessity environmental factors as intervening factors. In addition, with strategies such as creating open doors for dialogue Knowledge and convergence of actions with professionalism can lead to consequences such as social, financial and behavioral functional results Clients and employees found hands.

Cluster fluctuations and fractal structures are the two important features of space-time correlation in complex financial systems. However, the microscopic mechanism of creating and expanding these two features in financial markets remains challenging. In the present study, using factor-based model design and considering a new interactive mechanism called multilevel convergence, the process of forming cluster fluctuations according to the fractal structure of financial markets is investigated. Virtual agents' trade in different groups according to market performance and their mass behavior is measured at three levels of stock, segment and market. The results, in addition to providing new insights into the space-time correlations of financial markets, show that multilevel convergence is one of the microscopic mechanisms of microstructure of such markets. In other words, multilevel collective behavior is an important factor in the occurrence of cluster fluctuations, price bubbles and market fractals and therefore should be considered in interpreting the concept of risk and defining risk management strategies from this perspective.