ghodratollah emamverdi

One of the key elements to achieve economic growth and development of the country is financial stability. According to the empirical evidence in recent years and the intertwining of the financial system and the economic development of countries, high economic growth cannot be considered, but it was not successful in the financial markets. Therefore, considering the importance of the combined index of financial and economic stability in the economic growth and development of the country and especially the road transport sector, in this study the role of the combined index of financial and economic stability in the road transport sector of the country during the period of 1370-1401 has been examined. In the first step, the combined index of financial and economic stability was calculated with the help of the cumulative distribution function method, and then to investigate the effects of the combined indicators of financial and economic stability on the road transport sector, the Autoregressive Distributed Lag model was used to achieve the research findings. The results show that both in the short term and in the long term, the combined index of financial and economic stability has a positive effect on the added value of the road transport sector. The short-term and long-term elasticity of added value of the road transport sector is 0.074 and 0.153 respectively, which shows that its long-term elasticity is more than the short-term elasticity. Also, the results of the stability and diagnosis tests show that the coefficients of the variables are stable in the long term..

In recent decades the removal and reduction of subsidies or concessions for consumers of fossil fuel products such as oil has always been the focus of researchers in the field of energy economics .While the removal concessions for producers of these resources has rarely been discussed by researchers .This research with the purpose of studying the behavior of the oil producing company and considering the elimination of producer privileges ,in addition to developing an economic optimization model of oil production on one of the oil fields in the south of Iran according to the actual production of the field in the period from 2018-2037,has also been studied and investigated in the from of Iran`s new oil contracts (IPC).Therefore the implementation of the scenario of removing the privileges of the oil producing company shows the reduction of the optimal production rate .Despite the fact that the increase in the discount rate accelerates  the negative effects of removing points and has the opposite impact on the producer`s benefits.

​Financial stability is the cornerstone of modern economies and shows the proper functioning of the financial system and is a prerequisite for economic growth and strengthening the economy against shocks. In the path of economic growth and development, financial stability and its effects play an important role in the economy. On the other hand, road transportation is one of the important sectors of the country's economy and has a great impact on the country's economic growth. So, considering the importance and role of financial and economic stability in increasing the country's economic growth, as well as the importance of road transportation in the country's economy, in this study, the analysis of short-term and long-term causality relationships between the financial and economic stability index and the economic growth of the investigated road transport sector is included. For this purpose, first, the combined financial and economic stability indices were calculated, and then the short-term and long-term causal relationships between the financial and economic stability index and the economic growth of the road transport sector were analyzed. The results indicate a long-term and short-term relationship between the growth of the combined indicators of financial and economic stability and the value-added growth of the road transport sector, the growth of the amount of goods carried and the growth of the amount of passengers carried in the road transport sector. The effect of combined indicators of financial and economic stability on the added value of the road transport sector, the growth of goods transportation and the growth of passenger transportation has been positive. There is a one-way causal relationship from the combined indicators of financial and economic stability to the added value of the road transport sector, the increase in the amount of goods transported and the growth in the amount of passenger transportation.

One of the most important goals of every country is to establish financial and economic stability. If there is financial and economic stability, economic growth and investment and other goals will be achieved at the macro level and provide the basis for development. Considering the importance of this issue and since the combined index of financial and economic stability can play an important role in the economic growth and development of the country, especially in the rail transport sector, in this study the role of the combined index of financial and economic stability in the rail transport sector of the country has been examined. For this purpose, in this study, the combined indicators of financial and economic stability are first calculated, and then the effects of the combined indicators of financial and economic stability on the rail transport sector are investigated by using the self-regression econometric model with a wide interval to estimate the model and interpret the results. The results show that both in the short term and in the long term, the combined index of financial and economic stability has a positive effect on the added value of the rail transport sector. Also, according to the error correction coefficient, it can be stated that the speed of adjustment towards the equilibrium and long-term value is suitable, so that in each period about 0.46 of the imbalance error is adjusted and the short-term value tends towards its equilibrium and long-term value exponentially. Finally, the results show that the coefficients of the variables are stable during the investigated period.

The purpose of this study is to compare the accuracy of predicting market risk calculation methods of value at risk with the relevance of the artificial intelligence approach. the increasing development of financial markets has revealed the importance of estimating the well-known measure of market risk, risk value more than before. Value at Risk (VaR) is a statistical measure that calculates and quantifies the maximum expected loss from holding an asset or portfolio over a period of time with a certain probability (known confidence level) and is one of the most important market risk criteria that is widely used to manage financial risk by financial regulators and portfolio managers. Macro-level risks have pervasive effects and can have negative effects on the entire financial market. Recognition of the interdependence and mutual relevance of companies and the development of risk factors that predict an increase of dependence on the sequence returns to the company during the crisis is of utmost importance. The existence of such methods provides a powerful tool for decision-makers to increase future financial stability. After reducing the fluctuations of Bootstrap, Historical, and Variance covariance with using wavelet transformation for model training and forecasting, the method is used every 15 consecutive days as input (same as the independent variable) in RVM model and 16th day as the dependent variable was considered as the model output and to evaluate the models, two evaluation criteria called Mean Square Error (MSE) and Mean Absolute Error (MAE) have been used. The relevance vector machine algorithm was used to predict variables. The RVM algorithm is a nonlinear model and causes the algorithm to be nonlinear by transferring data from the input space to the property space. The reason for checking with the MAE error is that this error represents the mean of the absolute value and is more comprehensible for us than the MSE, which is the Mean Squared Error. The results of testing the hypotheses and fitting the relevant artificial intelligence algorithm showed that the artificial intelligence algorithm is an efficient method for predicting daily value-at-risk methods. Also, in the Iranian capital market, risk-value forecasting is done with the semi-parametric bootstrap method with higher power and is recommended for use. Parametric methods (variance-covariance) and historical simulation are in the next ranks. Studies on value at risk have been limited to one industry or by portfolio definition and all listed companies have not been examined.In this study, we tried to calculate the market risk of all companies listed on the stock exchange with the value-at-risk approach under 3 important and widely used models: variance-covariance, historical simulation, bootstrap simulation and measure their efficiency using an artificial intelligence algorithm. In a way, previous researches have a less statistical population and do not measure the efficiency of models in practice.

This study examined the internal rate of return, capital return period, profitability index, and the party's share in oil and gas industry contracts based on the maximum price. The results of the study showed that many restrictions in cross-selling contracts and participation in production had been modified in modern oil contracts. The financial system of modern oil contracts was more efficient than the contract of participation in production and mutual sale. This means that modern oil contracts were more profitable for the country that owns the reservoir than the contract of participation in production and cross-sale. According to the modelling results and future oil price scenarios, modern oil contracts have proved more efficient than participation in production and cross-sale contracts.

The Effect of Correcting The Fuel Price Gap on Economic Growth in Iran and Selected ContriesAbstractThe purpose of this study is to investigate the effects of fossil fuel subsidy reform on economic growth in Iran and several other countries. For this purpose, an endogenous growth model was specified and estimated using OLS Robust and Panel GMM methods, based on annual data in the surveyed countries in the period 2000-2018. The OLS Robust model, which analyzes the long-term effects, has been developed for the diesel product in three time periods: 2000-2018, 2000-2004, and 2005-2018. The results show that the fuel price gap variable increases in the mentioned three periods per unit of reduction in fuel subsidies by 5.53, 10.69 and 5.12 units of economic growth, respectively. In the GMM Panel model, which analyzes the short-term and medium-term effects, during the period 2000-2018, a one-unit increase in the price of diesel will increase economic growth by an average of 8.84 units.e results show that the fuel price gap variable increases in the mentioned three periods per unit of reduction in fuel subsidies by 5.53, 10.69 and 5.12 units of economic growth, respectively. In the GMM Panel model, which analyzes the short-term and medium-term effects, during the period 2000-2018, a one-unit increase in the price of diesel will increase economic growth by an average of 8.84 units.The Effect of Correcting The Fuel Price Gap on Economic Growth in Iran and Selected ContriesAbstractThe purpose of this study is to investigate the effects of fossil fuel subsidy reform on economic growth in Iran and several other countries. For this purpose, an endogenous growth model was specified and estimated using OLS Robust and Panel GMM methods, based on annual data in the surveyed countries in the period 2000-2018. The OLS Robust model, which analyzes the long-term effects, has been developed for the diesel product in three time periods: 2000

The development of infrastructure assets is one of the important requirements for economic growth and increasing public welfare. Proper and efficient management of such assets is necessary for the development of infrastructure assets. The purpose of this study is to investigate the role of infrastructure asset valuation in infrastructure asset management and sustainable development. For this purpose, the necessary data for conducting research was prepared through a questionnaire and in the form of snowball among experts in the field of accounting and financial management in the public sector and also through the press line and it was distributed on social networks. After collecting the questionnaires, the data was classified through Excel 2016 software and SPSS software version 20. LISREL software and structural equation method were used to analyze the data. Findings from the study showed that the factor loads related to hidden variables and observed variables have a good relationship and also the management of infrastructure assets has a positive relationship with sustainable development. Also, the evaluation of infrastructure assets as a mediating variable has a positive and significant relationship with infrastructure asset management and sustainable development. Therefore, it can be concluded that the variables of infrastructure asset management and infrastructure asset valuation have a direct effect on sustainable development from all aspects of social, economic and environmental. The management of infrastructure assets, for example, increases economic growth (increasing the movement of passengers, goods), reducing unemployment, reducing corruption, reducing risks and traffic accidents, and preserving environmental species, and so on

The relationship between credit and economic growth is one of the issues discussed by economists and recent studies have shown that the relationship between credit and economic growth can be nonlinear. The aim of this study was to investigate the nonlinear Granger causality between the credit variable (which is the most important criterion for financial development) and the economic growth variable. For this purpose, Granger causality in the form of MSVAR model and seasonal data in the period 1383: 1 to 1398: 4 have been used. The results show a nonlinear relationship between economic growth and validity and MSVAR model has better explanatory power than its linear competitor (VAR model). According to this study, in regime one (which corresponds to periods of positive economic growth and high credit growth), causality exists only in terms of economic growth to credit, and in regime two (which corresponds to periods of economic growth and low credit growth) Causality is from credit to economic growth. In other words, in periods of prosperity, an increase in credit cannot significantly boost economic growth, and due to credit constraints in bad economic conditions, an increase in credit can affect economic growth

In the present study a new total uncertainty criterion in Tehran Stock Exchange was estimated and the impact of macroeconomic variables on this uncertainty was addressed. Risk filtering with an approach to GDFM was first used to detect specific component of 25 time series of the main indices of the Tehran Stock Exchange over 10 years. In the next step, the conditional volatility of the remaining time series’ specific components were estimated using Stochastic volatility (SV) model and finally conditional volatility simulated using Markov chain Monte Carlo (MCMC) approach was averaged to obtain total uncertainty of the Tehran Stock exchange. The ARDL results showed that Tehran Stock Exchange uncertainty is dependent on independent variables such as inflation rate, banks' real interest rate, exchange rate in free Exchange market, liquidity, tax revenue and oil price. According to the results, however, no significant correlation exists between unemployment rate and stock market uncertainty.

Value at risk, which is a way of measuring the risk of reducing the price of portfolios or financial portfolios, is one of the most important market risk measures, which is widely used to manage financial risk by the Financial Regulatory Authority and portfolio managers.the effect of monitoring criteria on value at risk is considered as the risk criterion considering the role of intellectual capital and risk management with regard to the importance of value at risk as the main purpose of the research.for this purpose, financial information of 138 companies active between 1390 and 1397 was used as statistical society.to test the hypotheses of the research, using structural equation modeling and statistical analysis tests, the t - statistic was used. the results of the research show the effect of Corporate Governance system on value at risk, as well as the moderating role of risk management on value at risk. in this study, it has been shown that intellectual capital is not related to the impact of the criteria of Corporate Governance and value at risk of the information content.

Risk in itself can upset equations and upset equilibria. Financial, economic, political and international risks are among the risks in the business environment. In this study, an attempt has been made to measure the impact of risks on the return index of the Tehran Stock Exchange and for this purpose, the Markov switching model has been used to observe the impact of these risks in different regimes. For this purpose, seasonal data from 1388 to 1398 have been used. The results indicate that by equipping the LR test, nonlinear models are better than linear models. Also, according to the results of shock-positive reactions, the positive doubt on the economic risk in both regimes for a period is initially It has a lot of negative effect on the stock index and then the effect of doubt disappears and the positive doubt on the financial risk variable, if we are in the first regime, this effect will be positive on the index in a future period and negative if it is in the second regime in a later period And in the variables of political and international risk in the first and second regimes with a positive doubt, the effect of both on the stock market index is similar, so that in the first regime is negative and in the second regime this effect is positive.

human labor is considered as the most important capital of any country, for this purpose the effective and optimal use or in other words effective productivity of this capital is one of the main goals and plans of countries at the macro level. Considering the importance of this issue and since human capital as one of the most important types of capital can play a significant role in the economic growth and development of the road transport sector, in this study the role of adult human labor in the productivity of the country's road transport sector has been examined. For this purpose, by collecting annual data and using the autoregressive Distributed Lag model and error correction model with the help of EVIEWS10 software, the model was estimated and the results were interpreted. The results show that both in the short term and in the long term, the working experience of people over 50 years old in the road freight transport sector, as well as the training of people over 50 years old in the road freight transport sector, in the productivity of the road freight transport sector. It has a positive effect. Finally, according to the error correction coefficient in the ECM model, it can be stated that the speed of adjustment towards the equilibrium and long-term value was appropriate. So that in each period about 0.227 the imbalance error is adjusted and the short-term value tends towards its equilibrium and long-term value exponentially.

his study was conducted with the aim of identifying the leading companies in the stock market and the impact of the currency crisis on the change of these companies, using the spanning tree model. For this purpose, the daily price returns of 50 selected stocks from the Iranian stock market and in two time periods were used to form the stock network. The period from March 24, 2016 to March 20,2018 was determined as the period before the currency crisis, and by identifying the currency event on April 4, 2018, using the ICSS model, the period from April 4, 2018 to July 21,2020 was determined as the period after the exchange rate crisis. According to the findings of the research, in the period before the exchange rate crisis, the symbols of Khepars, Foulad and Kegol have the highest correlation with other stocks. In other words, these stocks have been able to play a leading role. After the jump in the exchange rate, Veghdir and Foulad were identified as the leading stocks. According to the results, export-oriented industries have been able to maintain their leading role after the exchange rate crisis, and import-oriented industries such as automobiles have lost their leading role.

The tourism industry is the largest and most diverse industry in the world. The impact of tourism on increasing employment and foreign exchange earnings, the prosperity of domestic industries, the expansion of international cooperation has changed the attitude of countries around the world and has found an important place in government policy. Therefore, in this study, the orthogonal effect of financial development and tourism on economic growth in Iran has been modeled using the factor-augmented vector regression model (FAVAR) combined with the model of variable parameters over time (TVP). And using time series data during the years 1363 to 1397 has been studied. The results show that there is a positive and significant relationship between economic growth and tourism and a positive and significant relationship between economic growth and financial development. Financial development such as easy turnover in the country of origin and the simple use of financial instruments to finance tourists play an important role in the growth of this industry. Just as increased financial development leads to economic growth, so does economic growth lead to improved infrastructure and the development of the tourism industry.

In this study, we examine the correlation between stock returns of Export-oriented (EOIs) and Import-oriented (IOIs) industries and exchange rates, to derive stock-exchange optimal weights, attempting to manage the risk of investors in the capital market. To do so, the ADCC and DCC models are used. The data consists of the stock return of the listed industries, and the daily exchange rate from 2008 to 2020. The results suggest that EOIs have a dynamic asymmetric conditional correlation, and IOIs have a dynamic symmetric conditional correlation with the exchange rate. Moreover, the results indicate that in both currency crises, the weight of optimal portfolio in all industries except pharmaceuticals, in non-crisis period is over 50% and in the crisis period is less than 50%. Accordingly, and to reduce the risk of the portfolio, in the non-crisis period, investors should invest more than half of a one-Rial portfolio to dollar exchange, and in the crisis period, they should allocate less than half of a one-Rial portfolio to this currency. In case of the currency crisis, it is suggested that investors invest in the stock of basic metals, because this industry is a pioneer in attracting currency crisis and increasing stock value of the industry through future cash flow and replacement value, and reduce the stock of pharmaceuticals and computers in their portfolio, due to attracting negative effects of the exchange market.

Risks and hazards in the economic context of a country can affect the country's economic indicators and have a reason for the growth or decline of the index .Risk can be explained as a factor that upsets the balance and changes the situation In emerging economies, one of the concerns of economists is to control existing risks to reduce costs and increase the growth of economi indicatorsIn this study, the effect of financial, economic, international and political risks on the total index of the stock exchange is examined, which, while examining the presence or absence of impact, examines the direction of impact. For this purpose, the modelARDL Quarterly data for the years 1388 to 1398 were used The results in the short and long term are that financial risk has a negative impact on the index and economic and international risk have a positive effect in the short term and in the long term financial risk and economic risk are also negative but international risk. It has a positive effect

This study aims to detect path of currency crisis in different listed industries, in order to manage risk of shareholders in stock market. For this purpose, joint Contagion Test, Joint Coskewness Contagion Test and the Ornstein – Uhlenbeck Process are used. The data used in this study include: stock return of the listed industries and daily exchange rate during 2008 to 2020. The results suggest that currency crises of 2011 and 2018 have transmitted to all export- oriented import- oriented and neutral industries (except mass construction). Moreover, the findings support the fact that starting point of crisis contagion in both currency crises is pharmaceutical industry that attracted currency crises due to its strong correlation with exchange market. The next point of contagion in the first currency crisis is investment industry, and in the second currency crisis, basic metals and oil products. It is suggested that when currency crisis occurred, investors increase weight of basic metals stocks and decrease share of pharmaceutical and computers in their portfolio.

The purpose of this study is analyzing the relationship between firm financial status and value at risk by emphasizing the role of risk management. Therefore, from value at risk information with the bootstrap simulation between the period of 2012 and 2019 and information of 138 companies in Tehran stock exchange (TSE) for statistical sample companies and the company's financial status criteria (performance measures and firm risk) and management criteria, as a moderating variable firms were used. In this study, the method of structure analysis which tests a specific model of relation between variables is used because this model is a comprehensive approach to test assumptions about the relationships of the observed and latent variables. The results of the hypotheses and model fitting showed that the firm's financial status on the value at risk is significantly effective and the risk management of this connection can be properly adjusted, however, performance measures were higher power in explaining the company's status as well as the value at risk.

Price fluctuation is one of the most important features of the energy market that leads to price risk and economic instability. In the financial market, one of the best uses of derivative securities is in hedging. The most common way of hedging in the investment is through appropriate derivative instruments. They include options, swaps, futures and forward contracts. Even though there are many criteria used in the derivation of the optimal hedge ratio, the minimum-variance (MV) hedge ratio considered by Johnson (1960) has been one of the most popular choices. The basic concept of the minimum variance hedging risk lies in the combination of investments in the spot and future markets in order to reduce value fluctuations. Thus, the optimal number of futures contracts that a person must hold to hedge against the risk of price fluctuation in the underlying assets can be obtained by calculating the optimal ratio of hedging risk. The literature shows that researchers mainly use future contracts to minimize the risk of price fluctuation in the spot market. Accordingly, in these studies, various econometric methods have been used to calculate the optimal hedging risk ratio. Also, in order to introduce the best hedging risk model, the performances of different models have been compared. The evaluation of hedging performance is based on the percentage reduction in spot variance compared to portfolio variance. Then, the purpose of this study is to choose an optimal model with the highest degree of hedging risk for the selected commodity.

Several techniques have been proposed in the literature to estimate the hedge ratio with index futures contracts.  Many practitioners and academicians have sought to solve the problem of how to calculate the optimal hedge ratio accurately. To achieve the goal, we compare the estimates of the hedge ratio from the ordinary least squares methods (OLS), autoregressive model (VAR/VECM), autoregressive conditional heteroscedasticity (ARCH/ GARCH) and copula. Also, to determine the changes in the optimal hedging risk ratio, we use the weekly time series of spot and future contract prices for crude oil and natural gas during the five-year period of 2013-2018. In the next step, the rolling window regression technique will be used to compare the performances of the studied models and select an efficient hedging risk model. The results of the weights for future by each of the four above-mentioned models will be used for hedging the spot prices of the two examined commodities. The obtained hedge ratios are applied on the real data in the following 20 weeks. Thus, the ability to reduce risk in every method is measured and compared during the specified period.

All the models are able to offer a significate reduction in the portfolio. The conventional approach to estimating the MV hedge ratio involves the regression of the changes in spot prices on the changes in future prices using the OLS technique. As we found, the minimum variance hedge ratio by the OLS method was 62% for crude oil and 37% for natural gas. However, for the OLS technique to be valid and efficient, the assumptions associated with the OLS regression must be satisfied. Thus, we use an autoregressive model (VAR/VECM). The optimal hedging risk ratio obtained from the VECM model is 98% for crude oil and 86% for natural gas. However, the OLS and VAR methods only capture the influence of two risk factors on stock returns in the mean on average but are not sufficient to capture the dependence structure in higher moments or tail dependence. The volatility clustering phenomenon and the existence of ARCH effects demonstrate that hedge funds volatility varies over time. Then, we use the conditional autoregressive model (GARCH). Furthermore, we utilize the copula method to capture the general dependence structure between the futures and spot prices. The copula method has been used for multivariate statistical modelling owing to its edibility and convenience to describe its ability to capture the nonlinear relationship of random variables. The copula approach allows us to model the marginal distributions of individual random variables and their dependence structure separately. Our finding show copula serves normally to hedge crude oil and natural gas at the rate of 98% and 93% respectively. These rates are the models for crude oil and natural gas copula at 98% and 94% respectively. In this paper, the efficiency of different models of the rolling window regression technique are compared. This section is the core of the research. The results of the effectiveness of the optimal hedging rates of the crude oil and natural gas market show that copula functions in both markets have been in better conditions than the other models. Thus, the result of the research indicates the high efficiency of the copula functions approach to calculate hedging risk rates.

The results show that modeling the relationship between the current and future prices in the form of copula functions is more efficient.

This study examines the relationship between volatility and the probability of occurrence of expected extreme positive and negative returns in The thehran security market.‌‌ The purpose of this research is optimation portfolios via hold positive return and eleminate nagative return.‌‌ Two measures of volatility and the factor of expected shortfall are examined:‌‌ conditional volatility calculated using an EGARCH model and idiosyncratic volatility based on the Fama and French five-factor model and expected shortfall calculate with smi-parametric metod.‌‌ This paper is based on regresion probit‌ model and data use from 1382 to 7/1397.‌‌ A significantly positive relationship is observed between a firm’s idiosyncratic volatility and the probability of occurrence of an extreme return.‌‌ Other firm characteristics, including firm price, volume and Book-to-Market ratio, are also shown to be significantly related to firm extreme returns.‌‌ The relationship Between the age factor and exterem return is not found relation.‌‌ The effects of conditional and‌ expected shortfall are mixed.‌‌

The purpose of this study was to investigate the existence of two phenomena of adverse selection and moral hazards in the Iranian supplementary health insurance market.

First, a demand model for the use of complementary health care services was defined under the least assumptions about how the insurance companies repay the policy, the distribution of the hidden health status of hospital clients and also the type of their supplementary insurance despite the two phenomena of unfavorable choise and moral hazards. The main model was estimated from GMM method and the existence of these two phenomena was tested using non-parametric statistical methods. The data used in this study were collected through a questionnaire and randomly from clients of 11 hospitals in the two groups of supplementary and uninsured treatment in Tehran.

The results showed that risk aversion for the consumption of health services is more than the consumption of composite goods and this amount is more for the insured than the uninsured, which indicates the low health status of the insured compared to other people. The Kolmogorov-Smirnov test also confirmed the different health distributions of the two groups. In addition, according to the results, there are small moral hazards in the insurance market. Moral hazards are lower for people with higher incomes, and the more insurance is demanded from people with higher incomes, the lower the moral hazards will be, and the lower the health of a person, the greater the moral hazards.

The results confirm the existence of adverse selection and moral hazards in complementary health insurance in Iran.

Abstract According to statistics, risk estimation makes unusual predictions without focusing on the relevant factors and only focusing on a set of equations. In this study, we used a spreadsheet data set of time series and a new method for risk estimation. This estimation was based on a generalized dynamic factor model (GDFM) and daily data series obtained from different measures of Tehran Stock Exchange over a 10-year period during 2008 to 2018. we first utilized a generalized dynamic factor model proposed by Forni et al in order to determine statistic and dynamic factors. In the second step, by using MATLAB, we estimated the joint component of the study series as Tehran Stock Exchange risk. Next, using the generalized least squares (GLS) method, we examined the impact of each of the filtered risks on the index returns. The results showed that although both risks estimated through one-side and two-side filtering substantially and significantly explain the changes in the performance of the studied indices, but the risk estimated through two-side filtering using GDFM can explain the returns changes much better and more accurate than the one-side filter using the same model.

Recency bias as one of a variety of behavioral phenomena, is been a while has entered into economic and financial discussions. Similar phenomena have been discussed in terms of content over and over again and its case in capital markets has been approved in some cases. In this situation, the vacancy of such topics in accounting issues is felt. Accordingly, this study tries to focus on the capital market mechanisms and using accounting information, investigate the role of accounting information quality and recency bias on the relationship between profitability and stock returns.For this reason, the data from 101 listed firms in Tehran Stock Exchange during the period of 2008 to 2017 and data-based multivariate regression were used. The results showed that the accounting information quality criteria significantly changes the relationship of recency bias behavior on the basis of earnings per share with corporate stock returns.

Nowadays, production of economic production is one of the most important of policymaker’s tensions. Address to this. It is necessary to merge the farmer’s aims along policy makers as well as their necessities. Therefore, the current study attempted to investigate on optimum cropping pattern of agricultural crops in Kurdistan province respect to each county emphasizing on minimize virtual water. To follow research aim the necessary data were collected for the agricultural year 2015-6 using goal programming. The results showed that, the current cropping pattern does not has no comparative advantage and it has to experience significant change. In other words, the current cropping pattern willing must to change from mixed form to specialized one according to comparative advantage according to resource possibilities of each district. The range of change showed that, there are significant decrease and increase in some crops compare to current situation. The results showed that, irrigated wheat did not get permission to enter optimum model. In addition, the most of current crop due to low return as well as disadvantage of study area omitted in recommended pattern.