نشریه اقتصاد مالی
پیاپی 62 (بهار 1402)

With having 80 percent of world trade, ports nowadays are the most important national and international window towards trade. Despite their Increasing importance in development of transportation, ports and the relevant activities are completely neglected in Iran. Significant relationship between ports development and their capacity and throughput drive an important question: what is the net effect of port throughput on the host areas growth? The present study analyses the question using data of six provinces including Khoozestan, Booshehr, Hormozgan, Gilan, Mazandaran, and Sistan and Baluchestan during 2005-2017 (1384-1396). Results show that ports capacity, human development, economic growth at the initial period, employment, government investment in ports, government's current budget, wage, technology, production structure and urbanization have significant positive impact on economic output of the provinces while the transportation cost has a negative one.

 Corruption is a complex, multidimensional phenomenon with multiple causes and effects, ranging from an individual act (such as bribery) to a pervasive act that encompasses all elements of the political and economic system. Despite the complexity, difficulty and sensitivity of dealing with this complication, in recent years, extensive studies have been conducted to measure corruption, explain the reasons for committing such criminal activity and provide deterrent solutions by the international community. The purpose of this study was the impact of corruption on the environmental sustainability of companies. The spatial scope of this research was the companies listed on the Tehran Stock Exchange and the time domain was between 2016 and 2020. 107 companies are selected by eliminated systematic method as sample. As data nature, this is a kind of quantitative study and as purpose, this is a kind of applied study. Jarque-Bra test is done for normality testing. Regression significance and coefficient significance tests are considered in order to investigate significance in regression pattern. Heterogenity, F-limer and Husman tests are considered as regression pre-test as well. The results showed that financial corruption affects the environmental sustainability of companies. The impact of corruption on environmental sustainability varies between large and small companies. The impact of corruption on environmental sustainability varies in companies with high and low financial performance.

The purpose of this study was to investigate the constructive role of herbal medicines in the resistance economy of our country through acquiring domestic markets and maintaining foreign exchange capital, as well as the currency earning potential of these medicines. The approach of this research was a combination, in the first stage, the data were collected using the foundational data theory method and the interview tool. The researchers interviewed 12 doctors, pharmacists, and herbal medicine sales managers in a targeted manner. The extracted financial consequences included: improving the sale of herbal medicines, increasing the export capacity, commercializing herbal medicines and attracting capital for the production and development of herbal medicines, which was used in the quantitative stage of the descriptive-survey method and using first-order factor analysis techniques. and the second were examined. 420 samples were collected using available random sampling and analyzed using AMOS software.

In this paper, using an agent-based model, the behavior of commercial banks and investment funds is simulated. We have tried to show systemic risk transmission mechanism from the money market to the capital market through balance sheet changes. In this model, the money and capital markets are endogenously modeled and the price is determined by interest rate and stock market index in these markets respectively. 81 percent of Iranian commercial banks have a mixed portfolio of interbank loans and securities and can spread the crisis throughout the financial system in case of a shock. This process happens through the fire sale mechanism and investment fund unit redemption and can make stocks undervalued in the capital market. Although higher imposed capital adequacy and liquid assets rate can reduce the shock effect on price in the capital market, these high rates make banks portfolios mixed and increases systemic risk. Optimal capital adequacy rate is estimated 19 percent.

 The purpose of this study is investigating the capability of Grasshopper Optimization Algorithm (GOA) in more accurately predicting the financial distress by-using intra-corporate (financial and non-financial) and economic variables. The method of this research is improving the performance of the basic model of Multilayer Perceptron Artificial Neural Network (ANN-MLP) by-using a hybrid model with GOA (MLP-GOA) and Ant Colony Optimization Algorithm (MLP-ACO). The statistical research population of companies active in Tehran Stock Exchange during a 7-year period (from 1391 to 1397) included 476 companies, and finally, after systematic elimination, there were 289 qualified companies (including 2023 observation year-company). Checked and screened. The results showed the ability of ANN-MLP model to predict financial distress by-using financial and non-financial variables, and in addition the hybrid models (MLP-GOA and MLP-ACO) had been improved this ability. The accuracy of the MLP-GOA model for the year t, year t-1and year t-2 (before financial distress occurs), respectively are 97.30%, 94.53% and 91.30% that higher than the accuracy of the basic model and the hybrid MLP-ACO model. Although, entering the economic variables has increased the capability of all models significantly but the results showed that the financial distress is more affected by intra-corporate variables and the effect of economic variables has already been considered through the effect on financial events recorded in the accounting system. The results of this study can be used by company managers, banks and rating and credit institutions, insurance companies, financial analysts, investors and investment companies in assessing the risk of financial distress to make appropriate decisions and actions.

One of the basic goals of any society is to achieve economic growth with less ‎fluctuations. In fact, achieving sustainable economic growth and economic stability is one of ‎the main goals of any country. If the existing economic base and the future perspective ‎indicate economic stabilization, the path of long-term investment growth will be strengthened, ‎otherwise, the capital will be inclined to the service, trading and hoarding sectors, and in the ‎long term, it will reduce the economic growth rate. In this article, the effect of macro-‎economic indicators (international trade, human capital, physical capital, inflation and foreign ‎debt) and institutional indicators (combined index consisting of the components of voice and ‎accountability, political stability without violent, government effectiveness, regulatory ‎quality, rule of law and Corruption Control) and corruption control) on economic growth ‎were investigated and analyzed in 15 selected developing countries during the years 2006 to ‎‎2019 with the generalized method of moments (GMM). The regression results showed that, ‎among macroeconomic variables, inflation and foreign debts had a negative effect on ‎economic growth, but international trade, human capital and physical capital had a positive ‎effect on economic growth. The combined index of institutional factors also had a positive ‎effect on economic growth.‎

Today, issues related to energy and its role in the economic and financial development of societies have become a strategic and important category. The use of renewable energy that is available through the technology channel can optimize energy consumption and this is possible by accepting innovation and renewable technologies. For this reason, the current research was conducted with the aim of designing a model of acceptance of technology and new innovations in the field of renewable energy by Iranian consumers with an economic, financial and social approach. This research is applied in terms of purpose and qualitative research in terms of method. 16 components and 50 indicators of the model were identified through library studies and open and semi-structured interviews with 14 experts in the field of renewable energy and experienced university professors and using the Grounded Theory method and open, axial and selective coding. Among the most important effective factors in the technology acceptance model are participation, technology, price, perceived benefit, financial conditions, and economic, social, and environmental consequences.

The main purpose of the research is to Identifying and ranking the impact of lifestyle variables and behavioral indicators on purchase decisions and mental conflicts after purchase - case study: young women with financial resources and high income in Tehran city.

10 variables. A behavior was identified through the Delphi group. The research is descriptive-survey in terms of method, practical in terms of purpose, the method of collecting data in the field through a questionnaire with a Likert scale and validity and reliability has been confirmed. The statistical sample size was determined to be 384 people.

After analyzing the data, all the hypotheses were confirmed and in the following order, they had the greatest impact on women's purchase decisions: self-deceiving behavior styles (1) successful people (2). herd behavior (3), aura effect (4), sophistry of gamblers (5), gap (6), idealism (7), realism (8), anchoring (9), projection (10) ).

The results show that the decisions made based on behavioral variables have more satisfaction and cognitive inconsistency, mental conflict and psychological tension after purchase.

The present study intends to use the Mixed Data Sampling model (MIDAS) to investigate the effect of exchange rate changes on the variable of real production in Iran during the period 1397:1  to1380:4  that Provides implement a flexibility model whit  high descriptive power, including variables of varying frequency (eg daily, weekly and monthly) together in a regression.The results of this study show that the MIDAS model is statistically strong in identifying the asymmetric dynamic effects of the independent variable with higher freuency (exchange rate changes) on the dependent variable with lower frequency (GDP) compared to the same frequency model of these variables and shows asymmetry better.Also, based on the obtained results, the intensity of the impact of the momentum and the persistence of each momentum are different, so that the negative momentum of the exchange rate has more intense and lasting effects on Iran's GDP.

Since the creation of the stock market in the nineteenth century, many researchers have focused on research into stock price forecasting models and market returns. Statistical prediction models such as Arma, Arima, Arch, have been widely used but none of them have had the desired result. Therefore, many researchers have recently considered the stock market as a nonlinear dynamic system. The application of nonlinear models as well as advanced techniques, although not many years have begun, but in a short time has been able to open its place in various sciences. The purpose of this study is to predict the stock index using the dynamic model averaging DMA and also the method of the dynamic model selective DMS and the use of quarterly data for the years 1380-1399. The main advantage of the model used in the present study is the introduction of a large number of independent variables for its dynamics without the usual problem of overfitting appearing in the model. In this paper, the effect of some macroeconomic variables on the process of modeling and forecasting stock returns on the stock exchange was investigated. The results of the article showed that the probability of entering the variables of money supply growth, quasi-money growth, inflation, land price index growth in large cities is more than other input variables.

With the increasing popularity and widespread use of cryptocurrencies, the creation and development of methods for predicting price movements in this field has attracted a lot of attention. In between, recent developments in deep learning (DL) models with structures such as long-short-term memory (LSTM) and convolutional neural network (CNN) have made improvements in the analysis of this type of data. Another approach that can be effective in the analysis of cryptocurrencies time series is the decomposition through algorithms such as complete integrated empirical mode decomposition (CEEMD). Considering the importance of forecasting in the cryptocurrencies field, in this research, by combining deep learning models and complete integrated empirical mode decomposition (CEEMD), The hybrid CEEMD-DL(LSTM) model has been used to forecast the bitcoin return (as the most popular currency). In this regard, the daily data of the total index of the Tehran Stock Exchange was used in the period of 2013/01/01 – 2022/05/28 and the results obtained were compared with the results of competing models based on efficiency measurement criteria. Based on the obtained results, the use of the introduced model (CEEMD-DL(LSTM)) has increased the efficiency and accuracy of bitcoin return forecasting. Accordingly, the use of this model in this field is suggested.

One of the most important problems in modern finance is finding efficient ways to summarize and visualize stock market data. Modeling the filtering of complex networks in the stock market allows this to be achieved by reducing the market size, obtaining reliable information with less disturbance. Since stock price changes are not independent of each other, the study of the correlation between stock price changes provides a better understanding of market performance for investors. Stock market analysis based on complex networks allows studying the correlation of stock prices. In this paper, using the stock market data in the Tehran Stock Exchange, the Iranian stock market network is created by the threshold method, and then the network filtering is based on MST. The results show that the filtration modeling of Iran's stock market network based on the MST can form a subset of the stock market that follows the performance of the entire market with a significant reduction in size and has a similar degree of diversification with the entire market. These analyzes provide a more in-depth insight into the structure of the stock market while reducing the size.

Non-governmental organizations (NGOs) as community-oriented structures with the aim of benefiting the people of the society and by creating business spaces by the government and removing the obstacles that existed before, are developing their activities in the field of commercial and economic activities. The aim of the present study was to provide a local model of empowering non-governmental organizations with emphasis on key business factors. In order to conduct this research, the researcher used a mixed method (qualitative-quantitative).in the qualitative part based on the data method of the research paradigm model including causal conditions (management and macro-organizational factors, social and support factors, marketing and information); contextual conditions (society-oriented areas, business-oriented areas); Intervening conditions (intra-organizational factors, extra-organizational factors, electronic empowerment of employees, electronic platform); and strategies (commercial development of cements, media actions, support, fundamentals); It was identified and classified. In the quantitative part, through exploratory factor analysis (EFA), the indicators of the obtained categories were tested and the factor loadings of all indicators were estimated, which showed the approval of those indicators and categories. . Also, the researcher, based on the outputs of the qualitative section and the EFA test, designed assumptions (8 hypotheses) and measured the claimed relationships through the structural equation test in the Smart PLS software.In the end, based on path coefficients (beta) and significance coefficients (Z), all the claimed hypotheses were confirmed

This article is devoted to the study of the impact of a reform in power plant fuel prices on the key variables of Iran's electricity market (average electricity price and the financial balance of the electricity industry). It is based on a simulation model. In this regard, we employed a model in which the presence or absence of competition in the electricity market is based on the amount of power plant storage factor that is available in every hour. The corresponding critical storage factor, which is based on actual operation, is also calculated. Due to a suppressed price cap and availability rate (that is even lower than what is necessary for sending optimal investment signals), the current price cap and availability rate is not a valid starting point for calculating the desired effects. Hence, before examining the effects of a price reform on the mentioned variables, the price cap and availability rates need to be adjusted to a level that can guarantee a minimum IRR for investors in this sector. According to calculations, in order to sending the necessary signals for investment, by assuming a fixed availability rate, it is necessary to increase the current price cap (nearly 417 Rials per kilowatt hour) to a new level (570 Rials per kilowatt hour). According to that, the average wholesale market price will increase from nearly 600 Rials per kilowatt hour to 706 Rials per kilowatt hour. The implementation of this policy imposes a financial burden of about 29894 billion Rials to the Ministry of Energy. Given that, in markets amid with a price cap (and hence an availability payment), a price reform in fuel price, requires a proportional modification in the market price cap. Based on this obligation, the effects of the price reform on the mentioned variables, is calculated and analyzed. The analysis is based on three different scenarios about fuel price for power plants; The price of gas used in the petrochemical industry (as a raw material for this industry), the price of gas that is exported to Turkey, and, the minimum bound foreseen in the "law on the targeting of subsidies" in relation to the price of natural gas. Implementation of the mentioned scenarios will increase the market cap from 570 Rials per kilowatt hour to 1783, 3655 and 2830 Rials per kilowatt hour respectively. Adjusted average market price, proportional to the adjustment made in the market price cap, would be equivalent to 1922, 3801 and 2973 rials per kilowatt hour respectively. Furthermore, the financial burden corresponding to these scenarios will be 61717, 156574 and 114866 billion Rials respectively.

One of the factors that can affect the improvement of competitive advantage in the steel industry is the explanation of effective policies to accelerate the attraction of investment. This research has been done with a mixed approach of qualitative and quantitative. In the qualitative part, the investment policy factors were extracted by content analysis method and selected by the expert panel of experts by fuzzy Delphi method. In the quantitative part, the model was formed with a comprehensive interpretive-structural modeling approach. Data were obtained in the qualitative part using semi-structured interviews and in the quantitative part through the pairwise comparison questionnaire. The research population consisted of experts including senior managers of the steel industry and 20 people were selected by purposive non-probabilistic sampling method and snowball technique. The results of the qualitative section showed that the factors of investment policy for competitiveness are in 14 categories, which based on the findings of comprehensive interpretive-structural modeling, the factors of "government rules and regulations", "establishment of standards in industry", "competitive strategy" "Industry", "industry financial structure", and "investment policy" can increase a higher level of competitive advantage for the steel industry, which highlights the importance of macro policies. On the other hand, investment policies for "knowledge management", "research and development", "marketing capabilities", and "risk management" are subject to macro-factors of investment policies that lead to competitive advantage. To be.