درخت حوزه‌های تخصصی

The oil price fluctuations are a debatable issue among scholars. Understanding the appropriate utilization of underground resources is paramount, considering their abundance and historical significance as a crucial source of financial income for Iran. However, oil shocks pose challenges to petroleum exporting nations due to fluctuating oil prices and their reliance on the global economy. This study employs Econometrics models and auto-regression analysis to investigate whether oil shocks significantly affect sustainable development in OPEC countries during 2000-2019. Findings reveal a 'resource curse' affecting the analyzed nations, with rising oil prices having no substantial impact on their gross domestic product. Furthermore, increased oil revenues drive inflation, impeding urbanization and exacerbating inequality (Gini coefficient), indicative of the 'Dutch disease phenomenon. In conclusion, policymakers should prioritize the economy's size and reduce investment risks by formulating attainable long-term plans concentrated on sustainable development indicators, aiming for more stable progress. Oil shocks notably influence the sustainable development of petroleum-exporting countries.

The modelling of strategies for buying and selling in Stock Market Investment have been the object of numerous advances and uses in economic studies, both theoretically and empirically. One of the popular models in economic studies is applying the Semi-parametric Markov Switching models for forecasting the time series observations based on stock prices. The Semi-parametric Markov Switching models for these models are a class of popular methods that have been used extensively by researchers to increase the accuracy of fitting processes. The main part of these models is based on kernel and core functions. Despite of existence of many kernel and core functions that are capable in applications for forecasting the stock prices, there is a widely use of Gaussian kernel and exponential core function in these models. But there is a question if other types of kernel and core functions can be used in these models. This paper tries to introduce the other kernel and core functions can be offered for good fitting of the financial data. We first test three popular kernel and four core functions to find the best one and then offer the new strategy of buying and selling stocks by the best selection on these functions for real data.

Investment fund managers have been trying to provide more attractiveness for investors by earning higher yields and generating liquidity. The investment rate of return of a mutual fund is obtained from the difference between the issuance and redemption of Net Asset Values (NAVs) per investment unit. However, the Exchange Traded Funds'(ETFs') yield should be calculated differently, and investors must consider the changes in market prices (Not only the NAVs), which are not usually announced through the Ads, so they disregard the investors. Investment fund managers believe that the attractiveness of the rate of return, even to a small extent, leads to over-welcoming of the investors and a faster growth of the funds' size. According to the information available in recent years, investors have shown more interest in fixed-income investment funds, and the growth of ETFs has been significant compared to the growth of total funds, so it can be assumed that investors are more interested in ETFs. Thus, in this research, we will identify factors affecting the development of ETFs, especially fixed-income ones. To fulfill our purpose, we examined all active fixed-income investment funds that are traded in the Tehran Stock Exchange and the Iran Farabourse from March 2021 to September 2023, including 25 ETFs. We analyzed our panel data through ordinary least squares. Analyzing the effective rate of returns of 25 Fixed Income ETFs, which have been active from March 2021 to September 2023, our results showed that ETFs development is significantly related to the fund size, daily price volatility, and liquidity. Despite our expectations, rate of return, fund age, and price adjustment do not have a significant effect on the development of investment funds. The result showed that a higher rate of return did not have a significant effect on the attractiveness and acceptance of investors, but the liquidity of investment units positively affects the development of fixed-income ETFs. We also found that the daily volatility of the final price inversely impacted the fund's size. This is consistent with investors' low risk-taking spirit in fixed-income funds and emphasizes their preference to avoid liquidity risk and price volatility risk.

Dynamic capabilities refer to an organization's capacity to purposefully create, expand, or change its resource base. In all countries of the world, the gas industry of each country plays an important role in its economy and growth. And we are in an era where rapid global changes and technological innovations occur, and gas refineries are not unaffected by these changes. The objective of this research is to identify dynamic capabilities within knowledge-based companies in the gas refining industry. This study employs a mixed-methods approach to develop a model of dynamic capabilities specific to knowledge-based gas refining enterprises. By analyzing 54 articles through a meta-synthesis method and conducting semi-structured interviews with 12 experts in this field, the components of dynamic capabilities were identified through thematic analysis. Following this, the factors were evaluated using confirmatory factor analysis, and a questionnaire was distributed to specialists, with all components being validated in the initial phase. To develop the final model, the research utilized structural interpretive modeling (ISM), and the corresponding questionnaire was completed by 15 managers of knowledge-based companies in Tehran

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

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.