mohammad nabi shahikitash

Rural migration crisis is one of the most challenging problems in the socio-economic space of Iran. In this study, the fuzzy approach is applied to examine the effects of some variables like poverty, rural unemployment rate, the rural Gini coefficient, urban to rural wage ratio, drought situation and literacy rate on migration. In order to find the appropriate model, the Tanaka and FLSR fuzzy regression approaches are compared. The effects of the socio-economic variables on rural migration in Iran are examined during the period 1990-2018

In Iran's economy, most of the financial resources needed by economic enterprises are provided through the granting of facilities by the banking network. Therefore, studying the factors affecting banking facilities in the private sector in Iran's economy is of particular importance. In this article, we investigate the factors affecting the facilities granted by banks using quarterly data from the Iranian economy from 1379 to 1399 with the nonlinear Markov switching approach. A non-linear approach was used because bank facilities show different effects of economic factors during periods of credit and commercial boom and bust. The results of the research show that the economic growth rate, the interest rate of facilities and the growth rate of bank deposits have a positive relationship with the growth of real facilities and the interest rate of bank deposits has a negative effect on it, but the degree of influence of each of the economic factors on the growth rate of real facilities is different in every cycle. This asymmetric impact of macroeconomic factors during different cyclical periods is important in policymaking and should be taken into consideration when making monetary policy.

Investigation of effective factors in business cycles in Iran, from 2009:3 to 2018: 3, based on the Bayesian approach with emphasis on the effects of financial cycles, is the major purpose of this paper.  At first, using the Hodrick-Prescott filter, we extract business and financial cycles, and then we study the causal relationship between financial and business cycles, using the Granger causality test. There is a two-way causal relationship among the variables (GDP, Tehran Stock Exchange index, land value (per square meter), current government and non-government credits, oil revenues, and current government payments), except for the credits and business cycles. Based on the Bayesian approach, we found that housing sector cycles have negative and current government payment cycles have a positive effect on the business cycles, and both of them are significant. However, Tehran Stock Exchange index, current government and non-government facilities and oil revenues do not have a significant effect on the business cycles.

The main purpose of this study is to determine the real and dynamic relationship between returns and stock market fluctuations in different time horizons so that its key results can measure the predictive power of returns and stock market fluctuations in determining the level of economic activity of investors. Accurate measurement of these relationships helps investors predict stock market movements in the future and Develop, plan and implement their own investment strategies in each time horizon. In this research Maximal overlap discrete wavelet transform has been used to investigate the relationships between industries return fluctuations and stock main indices in different time horizons from 2011 to 2020 have been estimated. The results showed that the wavelet variance of the rate of return varies in different industries. The return of investment companies on various time scales is equal to the investment return of the banking industry. The variance of the total index is less on different time scales than the value of the cash index.

The main purpose of this study is to do modeling and investigate the relationship between liquidity and its role in the development of the stock and the housing market. This is accomplished through comparing 10 of Bayesian Averaging methods and principal component analysis carrying out an extensive set of simulation studies based closely on real datasets that span a range of situations encountered in practical data analysis. The data used in this article includes the seasonal data during the period from (1385:1) to (1399:4. The evaluation results show 4 types of PC, so that the first PC shows more than 95% of the changes. In the first PC, the two variables of liquidity and exchange rate have the most weight, and respectively, the second, third and fourth PC are related to oil revenues, legal deposit ratio and coin price. The variables of liquidity, exchange rate and oil revenues have a positive relationship with the index of stock and housing prices, and the variables of the legal deposit ratio and the price of coins have a negative relationship with the index of stock and housing prices. Also, the estimation results of the 10 analyzed models of Bayesian averaging show that in the modeling of the stock price index as well as the modeling of the housing price index, the best models are obtained through applying PCA and the prior distribution of AIC. Therefore, the use of the principal component analysis and the AIC model as a basis for approximating the probabilities of the posterior model, under the condition that the probabilities of the prior model are similar to Zellner's g-prior, is approved in this research.

By utilizing the new Keynesian stochastic dynamic general equilibrium model, this paper examines the effects of credit easing policy on macroeconomic variables with or emphasizing on production. For this purpose, a model has been design including 5 sectors of household, enterprises, banks, government and central bank. Considering the dominance of fiscal policy over monetary policy in the Iranian economy, the integrated constraint of the government and the central bank has been used. The model has been estimated using Bayesian method and quarterly time series data during 1991 to 2017. The results of Impulse Response Function show that implementation of this policy has increased consumption, investment, government spending and ultimately production, which indicates the effectiveness of this unconventional monetary policy to get the economy out of recession. Also, in response to the positive impulse of the central bank’s credit line to banks and the negative impulse of legal reserves, bank facilities increase, which is in line with theoretical expectations. The impact of the negative impulse of interbank market rate has also led to an increase in production credits.

Using the input-oriented bootstrap data envelopment analysis approach, using the LSW algorithm, this study estimated the corrected bias efficiency values of the technical, managerial and scale efficiency of technology centers with an emphasis on science and technology parks in Iran. The central question of this article is what is the industrial capacity and regional innovation capacity in each province and what is the matrix of the relationship between the commercialization coefficient and the industrial and innovation coefficient of each region. The results obtained from this study indicate that the super-efficiency score in the technology centers of the country (variable return to scale) is 2.97, the efficiency score is 0.94, and the efficiency score using the bootstrap method is 0.85. Also, the findings of the research show that the most important factor in improving the technical efficiency of science and technology parks in the country is the improvement of managerial efficiency. According to the research findings, only 5 of the 15 science and technology parks with high commercialization efficiency have high innovation, three of them are located in industrial areas and the other two are located in semi-industrial areas. Among the reasons for their success, we can mention the existence of driving industries, the existence of effective communication between universities and research institutions with industry, networking related to innovation, and such things. On the other hand, looking at 4 non-innovative regions that have less commercial efficiency, it is clear that they are located in semi-industrial and weak regions, and among the most important obstacles to their innovation, we can mention the lack of knowledge clusters, emphasis on absolute theoretical skills, lack of necessary coordination between organizations He pointed out specialization and lack of emphasis on innovation.

The growth centers of technology units and science and technology parks can play a unique role in the commercialization of knowledge and the growth and development of the regional economy, and from the functional point of view, the parks cultivate and apply knowledge in a targeted manner and in creating knowledge-based employment, the rapid growth of the income of the local community. and creating wealth, increasing the level of specialized competition, creating influential business networks, creating the capacity to access other markets, improving the level of education and market-oriented skills, defending a cooperative and competitive knowledge business culture, and providing creative and innovative public services.
In Iran, like the member states of the European Union, various science and technology parks are active in different provinces of the country. Now the central question is how successful these parks have been in commercializing ideas? Also, another central question is what is the industrial capacity and regional innovation capacity in each province, and what is the matrix of the relationship between the commercialization coefficient and the industrial and innovation coefficient of each region? The answer to each of the above questions can help to examine the state of regional innovation and commercialization development or industrial lock-in and commercialization in each province.

Modified bias efficiency of technical, managerial and scale efficiency of technology centers with emphasis on science and technology parks in Iran.

In this research, the bootstrap approach has been used. First, he explained the general process of data generation and bootstrap, and then, in the second section, we introduced the method of evaluating the efficiency, i.e. data envelopment analysis, and finally, this section ends with the introduction of algorithms for estimating the data generation process (DGP) in the third part.

The Results of the research confirm that among the types of parks and according to the required infrastructure, it is possible to make the country's science and technology parks mission-oriented according to the coordinates of the advanced technology park, innovation and entrepreneurship park, university park, virtual park and business park. The central mission of the parks based on which model is defined requires a detailed analysis of the structure of human capital, the state of universities in each province, the evaluation of the local economy according to the structure of the macro economy, the business environment, analysis of the local, regional and extra-regional market, the structure of investment and sources of supply. Finance depends on the local economic cycle, local capabilities and talents, entrepreneurial infrastructure, and the state of technology in each province. In this article, the results and achievements of the commercialization of science and technology parks have been examined and quantified.

The regional science and technology park, as a market support institution, together with universities and industry, can create a regional innovation ecosystem. The regional science and technology park should balance local and extra-regional facilities and needs. By examining the institutional structure of science and technology parks in the world, it can be seen that the parks do not have the same structure and countries have adopted different goals and diverse operational models depending on the development goals and university structure and industrial and technology structure according to regional requirements; Therefore, developed and developing countries have followed different development patterns according to their needs.

Compared to GARCH, ARDL, VAR, and similar methods that are commonly used for stock market analysis and portfolio pricing, the quantile regression has proven to be more advantageous. In this study, we combine the quantile regression with wavelet decomposition to analyze different investment horizons in Tehran Stock Exchange. The discrete wavelet decomposition is used to divide the indices time series into short-term (2-16 days), mid-term (16-128 days), and long-term (128-512 days) horizons. The investment horizons are then accurately studied in a bear, normal, and bull market. Since Iran is an oil-exporting country and its economy is highly impacted by fluctuations in the USD exchange rate return, it is of crucial importance to analyze the effects of oil price and free-market USD exchange rate return on the stock market for investment policy-making and portfolio management. The results demonstrate how the exchange rate return volatility and the OPEC basket price fluctuation affect the stock market. The results illustrate strong evidence on the assumption of a long-term strong positive correlation between TSE and the USD exchange rate return increase.

This paper aims to model the housing price behavior in the Iranian market using three stochastic differential equations: the Black-Scholes model, Merton model, and geometric Brownian motion with nonlinear GARCH. The data of this study include monthly observations on housing prices between 2009 and 2018. Also, to estimate the coefficients of equations, we used the maximum likelihood approach, and the drift and diffusion parameters were calculated. The findings suggest that the efficient-market hypothesis does not hold in the Iranian housing market since the sudden jump under systematic risks is indicative of an increase in inefficiencies in the housing market. In this paper, we also use the nonlinear GARCH (NGARCH) model based on the Merton model to investigate the impact of good and bad news and positive and negative shocks. According to the results of the NGARCH model, housing price is more affected by bad news and negative shocks. According to the estimated equations in the Iranian housing market and the maximum likelihood function, the geometric Brownian model with stochastic NGARCH-based fluctuations has more explanatory power than the Merton model and the geometric Brownian model with constant volatility.

Due to the strategic role of volatility and instability of crude oil prices and its effects on all countries of the world, different methods of modeling and forecasting are necessary. Over the past two decades, extensive literature has emerged on various approaches to empirically modeling volatility in the crude oil market. In this research, WTI crude oil price volatility modeling, which is one of the most important types of crude oil in the market of this strategic commodity, is examined with six flexible stochastic volatility (SV) models. Then the experimental performance of these models is compared with each other using Bayesian methods. The findings of this study show that adding jump in efficiency and leverage effect to the stochastic volatility (SVLJ) model greatly improves its performance compared to other models. According to the findings of this model, the stability of volatility in the WTI market is very high and on average one jump occurs in this market every year. However, this model shows that in 2020, two jumps in WTI returns occurred in April and May, which is a unique event. In addition, the correlation between the return jump component and the volatility jump (Merton correlation jump) is not confirmed in the WTI data. Also, due to the negative leverage effect, negative shocks have stronger volatility effects than positive shocks in the crude oil market.

According to empirical evidence, the consequences of supportive policies will not necessarily lead to efficient industries, optimal allocation of resources, international competition, and optimal use of opportunities. Inappropriate or suboptimal use of the supportive policies in manufacturing industries may change the direction of scarce resources and the relative prices in the society which may lead to price distortion and some monopoly power in the country's industries.  In this study, using the Hall-Rager method, we analyzed the effect of supportive policies on price deviation in the Iranian manufacturing industries. To do this we used the Herfindahl index, the simple rate of tariff, and Implicit nominal support index as independent variables and the price deviation in the manufacturing industries of the country as a dependent variable. The model as a dynamic panel data has been estimated with the generalized method of moments for 92 industries based on the four-digit ISIC code during 2004-2014. The results showed that between the years 2004 to 2014, the highest level of simple tariff rate was for the shoes and clothes industry with 100 percent. The cement and plaster industry had the most implicit support amongst the  92 studied industries. According to the estimated models, centralized industrial structure, implicit nominal support, tariff rate, and barrier entry intensity have had a positive and significant effect on the formation of price deviation in Iran's manufacturing industries.

The primary objective of the present article is to study the impact of positive shocks in government expenditures during different financial periods on economic variables in Iran. To this end, first, financial condition index was created through principal components analysis. Then, using LR non-linear test and the data related to the period of 2005 to 2018, while studying non-linearity, the value of the variable threshold variable is estimated exogenously. Therefore, the value of the threshold of financial index is considered to be -0.36. Threshold Vector Autoregression Model (TVAR) with the assumption of the possibility of regime switching and generalized impulse-response functions are extracted to examine the impact of positive and negative shocks in government expenditures. Based on the results, the reaction of economic variables to positive shocks in government expenditures will be independent from financial periods whereas the reaction to negative shocks in government expenditures is influenced by financial periods, especially the period of recession. financial sector cannot improve real sector due to structural problems. increasing government authority, lack of competition space between public and private sector, allocating inefficient government spending have caused that financial sector cannot significant effect on economic real sector such as economic growth And in some cases we will see the negative impact of this sector.

Given that changes in interest rates can only partly empower financial authorities to enhance the country’s economy, there has been a major shift toward outcomes of fiscal policies, particularly after the big financial crises and the global recession. Thus, the government’s spending plans are implemented to motivate the economy. The present study aims to investigate government expenditure shocks on consumption spending, private investment, and financial cycles during 2005-2018 using the Structural Vector Auto Regression (SVAR) model. The findings indicate that there is no significant relationship between government expenditure shocks and consumption spending and private investment. The findings show a crowding out effect between government spending shock and the private sector in Iran. However, you can see a positive relationship between GDP and the private sector. Moreover, these shocks can lead to a positive impact on GDP accordingly. However, government expenditure shocks may only have short-term effects on business cycles because of the instabilities and uncertainties in government spending.
JEL Classification: H11, H3, H5.

Study of the comovement between financial markets plays a crucial role in increasing the performance of trading strategies. Due to the strong dependence of Iran economy on oil prices and the fluctuation of the dollar exchange rate in the open market, it is very important to study these macroeconomic level factors on stock market performance for portfolio management. In this research, using time-frequency domain analysis, while discovering the interdependence between financial markets, capital turnover trends in Tehran stock exchange are analyzed and the effect of exchange rate fluctuations and the price of the OPEC oil basket on the indices including TEPIX, industry, banking, automobile, and oil products are studied. The results show that the longer the investment horizon, the stronger this effect is, and the increase in the exchange rate causes the index to increase in order for the market to prosper more, while there is a weak interdependence between TEPIX and oil price. During the investment horizon of 4 to 9 months, any increase in USD encourages shareholders to trade more, while in similar conditions, the increase in oil prices, except in the banking and the petroleum sectors, cause money to flow out of the market in other sectors.

Many economic and financial time series, especially stock prices, have gone through stages in which their behavior seems to have changed significantly. This change in behavior in time series may be over time in terms of the mean value, variance, or covariance of the current values of the time series with its past values. Today, the analysis of a market separately from other markets is almost invalid and analysts need to conduct their analysis based on the relationships between different markets. In addition, it is important to pay attention to the relationship between different markets especially the relationship between domestic and international markets to study the behavior of different economic variables and their time series.  

Oil, gold and stock markets are complex, time-varying, nonlinear and multivariate economic systems that are affected by various factors such as political, military, economic, and supply and demand factors. And these few studies have focused on GARCH, VAR,… econometric methods that are based on linear regressions. Despite the importance of examining the relationship between the three variables of crude oil price, gold price, and stock price index, unfortunately insufficient studies have been done in this country. Given that the time series of crude oil, gold prices, and stock price indices are a combination of components of different cycles, the relationship between these variables has changed over time, and examining these variables separately provides only partial and perhaps misleading information. Therefore, in contrast to previous research, the correlation between the stock market, crude oil and gold has been investigated with a multivariate framework, in which the correlation of time intervals has been considered. In addition, due to the fact that previous studies have emphasized the effects of uncertainty of one variable on other variables separately and have not examined the simultaneous effects of several variables on another variable, in this study, the relationship between the three variables has been investigated in the form of simultaneous communication using wavelet analysis. The main purpose of this study is to investigate the relationship between the price of gold, oil and the stock price index of the Tehran Stock Exchange in different periods and time periods. In other words, in this research, we seek to answer these questions  whether the stock price index of Tehran Stock Exchange is related to the price of international oil and gold assets and whether the cross-sectional relationship between the three variables has changed at different times and in different time zones. In line with the purpose of this article, in addition to using wavelet analysis and multiple correlation analysis, Granger causality and co-integration tests have also been used. The Johansson co-integration test was used to examine co-integration. The variables used in this research are daily time series data  of gold price, crude oil price and stock price index of Tehran Stock Exchange from January 2003 to May 2016.

The results obtained from this research (from R software) are as follows: 1- The above three time series have a significant correlation throughout the period. 2- The relationship between the stock market in Iran and the international markets of crude oil and gold has changed at different time scales. 3- Based on the results of the wavelet correlation of stock price index and crude oil price, in periods higher than 128 to 1024 days in the whole period (2003 to 2016) there is a high correlation. This issue indicates a temporary correlation between stock price index and crude oil price that fluctuates in the short term. 4- Based on the results of the correlation between the stock price index and the gold price, there are large correlation zones at intervals above 128 to 1024 days throughout the period. This issue indicates a temporary correlation between the stock price index and the gold price, which fluctuates in the short term.    

Therefore, according to the results of this study, it can be concluded that oil and gold assets are not suitable to cover the risk of stock price index and building a portfolio consisting of these three assets is not a good  one because the price changes of these variables have a significant correlation. In addition, it can be concluded that in the real world, other factors affect the stock price index, which are suggested to be identified and studied in future research.

Today, paying attention to environmental indicators is considered as one of the most important approaches to agricultural production. For this purpose, it is necessary to identify and prioritize, measure the effect, the intensity of sensitivity of environmental indicators to changes in chemical inputs over time. The aim of this study is to identify and prioritize chemical inputs on environmental indicators of pistachio in Kerman province. The statistical population of the study included veteran experts of agricultural jihad in Kerman province. The sample consisted of 50 specialists who were purposefully selected. To prioritize the environmental indicators of chemical inputs used in pistachio orchards, the fuzzy hierarchical analysis process method (Chang method) was used. Findings of the study showed that soil contamination due to the use of chemical inputs has the highest priority. Water pollution caused by chemical inputs is in the second place. Air pollution caused by the use of chemical inputs is in the third degree. Exports of chemical inputs and risk aversion in the use of chemical fertilizers are the last ranking. It can be suggested that the use of organic fertilizers along with biological and natural methods of pest control in pistachio orchards can be alternative options related to environmental pollution.

The present paper aims to identify the input response of firms to the risks of demand uncertainty concerning the levels of technology. In this paper we examine panel data from1995 to 2018 and analyze the behavior of the manufacturing operating in the industry sector of the country from the prospect of risk taking and risk aversion and, its impact on the two inputs of labor and capital. To do this, the industries of Iran based on two-digit ISIC codes are divided into three categories of Low-level Technology (LLT), Moderate Level Technology (MLT) and High-level Technology (HLT) with respect to the level of technology. Research results show that low-tech industries that include food industries, textiles, garments, wood products, paper production, and paper products represent a significant and positive response to the risk of demand uncertainty and are risky. As a result, the ratio of increased capital to labor is higher and the industry is also expensive. But the situation is different for the other industries with a moderate to low technology and moderate to high technology. MLT industries and HLT industries show a negative and significant reaction to the risk of uncertainty and in fact, they are risk-averse, and the uncertainty of demand is higher and the ratio of the use of capital to labor is lower.

The main purpose of this study is to investigate the relationship and effects of financial cycles on economic growth cycles by using uncertainty in the model. One of the appropriate techniques in the model is the Bayesian Model Averaging.This study consider the strong affecting factors of the financial cycles on the economic growth cycles in Iran during period 2009 to 2018. The studied variables are GDP, Total Index of Tehran Stock Exchange, Average Cost per Square Meter, Govermental and nongovernmental Current Credit, oil Revenues and Government current payments.First, the cycles are extracted by using the Hodrick–Prescott (HP) filter.Then, the relationship between financial cycles and economic growth cycles is investigated using the Granger causality test. The results show bilateral causal relationship between the Variables Exception of current credit with economic growth cycles.Finally, estimation of eight regressions and Bayesian model averaging of the coefficients show that all five explanatory variables have been identified as the most important factors on the economic growth cycles in Iran.Also,we determine the variables of the Stock Exchange Index,current credit and current payments have the positive effect on the economic growth cycles, and the variables of the housing and oil revenues cycles have the negative effect on the economic growth cycles index. The results of this study can be used by macroeconomic planners from a policy viewpoint.

Monopoly and negative externalities are two aspects of market failure that affect the market performance. This study extends the Leibenstein approach, a framework to measure the market performance, which evaluates the social welfare costs of market power and environmental inefficiency. To assess the deadweight loss, we capture pollution impacts, on the market performance in an imperfect competition. In doing so, we assess marginal costs and price elasticity of demand by a Translog function, market power by Herfindahl-Hirschman and Lerner indices, and environmental inefficiency by directional distance functions, at a Cournot competition for Iran’s energy-intensive industries at the four-digit ISIC level. Our results demonstrate that the social welfare costs of welfare triangle and economic rent are negligible and include a small amount of welfare costs. Non-ferrous foundry imposes the lowest social cost (1.03% of its production value), and cement, lime and gypsum industries impose the highest social cost (50.7% of their production value). Those industries with more market power pay less attention to the environment. In polluting industries, welfare loss, due to market power, is relatively negligible. However, relatively high cost of social welfare, due to environmental inefficiency, indicates the necessity of levying a green tax to reduce the adverse effects.

The capital asset pricing model (CAPM), developed by Markowitz (1952), Sharpe (1964) and Lintner (1965), explains stock returns based on the mean-variance framework. However, many researchers and practitioners have found that stock returns cannot be fully explained by the CAPM. This has led to further attempts to incorporate other aspects of stock into the CAPM. One of the most successful findings is the role of liquidity in asset pricing. Amihud and Mendelson (1986) were among the first to examine how the level of liquidity affects asset prices. Pastor and Stambaugh (2003) and Acharya and Pedersen (2005) also examined the role of the second moments of liquidity in asset prices. Other research works have been done in this area. In our study, we seek to make a liquidity adjustment to the consumption-based capital asset pricing model (CCAPM) and show that the liquidity-adjusted CCAPM is a generalized model of Acharya and Pedersen (2005). This aspect of CAPM has not been investigated in my country. So, the research is of novelty here. The study aims to show that the expected stock return is determined by both consumption risk (CR) and liquidity risk (LR). The latter (i.e., LR) has been defined as the covariance between transaction costs and consumption growth. In this study, the effective trading cost index of Hasbrouck (2009) was estimated using Gibbs Bayesian method and roll’s model with the daily data of the stock closing price in Tehran Stock Exchange during 2009-2010. Then, by including the two types of Hasbrouck trading cost and the bid-ask spread as well as the liquidity risk in the traditional consumption-based capital asset pricing models, adjustments are made in these models. The purpose is to show that the liquidity-adjusted CCAPM provides a better fit for the cross-sectional expected returns across various liquidity-based portfolios, while the traditional CCAPM fails to capture the liquidity effect. This study also seeks to show that the liquidity adjusted CCAPM is robust enough to include industry portfolios The model considered in this study is a generalized version of Acharya and Pedersen (2005) and suggests a novel source of liquidity risk which is the covariance between transaction costs and consumption growth. The question to arise is ‘Can the three channels of liquidity risk of Acharya and Pedersen (2005) be captured by the covariance between transaction costs and consumption growth?’ We try to enrich the literature that highlights the pricing of various systematic risks associated with consumption by showing the positive relation between stock returns and the sensitivity of transaction costs to consumption growth. Reviewing the literature, it seems this area has worked in advanced countries (Lettau and Ludvigson, 2001; Bansal and Yaron, 2004; Parker and Julliard, 2005; Yogo, 2006; Jagannathan and Wang, 2007; Savov, 2011; Boguth and Kuehn, 2013), while, in some country such as Iran, it has not been tried yet. The focus of this research is on the liquidity adjustment to the consumption-based pricing models, as an area that has attracted little attention in the literature. The research will add to the literature on asset pricing models in Iran by answering the following questions: -  Is there a significant and positive relationship between the expected stock returns and the expected transaction costs? -  Does liquidity-adjusted CCAPM have the power to explain the expected stock returns of Tehran stock exchange? -  Compared to the traditional CCAPM, can liquidity-adjusted CCAPM better explain the cross-sectional expected returns across various liquidity-based portfolios? - Does the increase in stock liquidity lead to lower stock returns (and vice versa)?   Also, the main research purposes are as follows: -  Investigating the impact of companies' expected returns through a factor called the liquidity systematic risk - Presentation of liquidity-adjusted CCAPM which is consistent with Tehran Stock Exchange, as a practical model to determine risk and return - Considering the liquidity risk as a covariance between transaction costs and the total consumption growth -  Testing the explanatory power of the theory in determining the rate of the expected return in Tehran Stock Exchange and examining the existence of a significant relationship between risk and return

In this study, the liquidity-adjusted model is examined by means of a portfolio constructed on the basis of liquidity criteria and market characteristics. Based on the previous studies and the structure of the capital market in Iran, 20 portfolios were observed in the present study, and Liu and Strong’s (2008) approach was used to calculate the portfolio return. This model shows that the expected return on stocks is determined by the risk of consumption and the risk of liquidity. According to the study by Liu et al. (2016), the following two regressions are used to estimate the beta consumption and the beta liquidity in the present study: (1)   (2)  where  is the ratio of the residual of the returns in portfolio i to risk-free returns, ΔC refers to the growth in the consumption of non-durable goods and services, and  is the residual of the following regression:  (3)   where  is the transaction cost of the asset i in season t. Using the change in the transaction costs, , is because of the durability and stability of liquidity. In addition, beta liquidity can be directly estimated using transaction costs as follows:                     (4)   It should be noted that, in this study, Pooled GLS and Generalized Method of Moments (GMM) were used to estimate the regressions. However, considering the similar results yielded by these two methods, only the results of Pooled GLS are analyzed in this section. Comparative assessments between the liquidity-adjusted CCAPM model (6) and the traditional CCAPM (5) are performed using the following cross-sectional regressions:(5)  (6) where  refers to excess of portfolio p to risk-free returns in season t,  is the beta consumption,  is the transaction costs of portfolio p and  is the beta liquidity. Beta consumption is estimated through a time series regression of the excess return on consumption growth as in Equation (1). Beta liquidity is also estimated through the time series regression of liquidity changes on consumption growth as in Equation (2). Similar to the procedure applied in studies by Acharya and Pedersen (2005) and Lettau and Ludvigson (2001), beta consumption and beta liquidity are estimated over the entire sample period in this study.

In recent studies, transaction costs have been cited as a key measure of performance and a central role in financial markets. Also, transaction costs have been discussed in a non-liquidity way in many studies. However, transaction costs are considered as a determining factor in market liquidity and assets return. Given the importance of the role of transaction cost in financial markets, this study proposed an adjusted consumption-based capital asset pricing model and examined how this model is adjusted by transaction costs and liquidity risk. In this study, like Hasbrouck (2009), the researchers aimed at estimating the dynamic model of Roll (1984) in order to estimate effective transaction costs using daily ultimate prices of Tehran Stock Exchange. In other words, using Gibbs cost-effectiveness estimation, the initial analysis of the liquidity changes has been carried out, which is a new approach in the literature related to the Iranian capital market. In other words, using different proxies for transaction costs such as Hasbrouck’s (2009) effective transaction costs and estimated bid-ask spread based on the highest and lowest prices, liquidity-adjusted CCAPM is shown to be more appropriate for the expected cross-sectional returns through portfolios formed on the basis of liquidity criteria. However, the traditional CCAPM is unable to account for liquidity effects. Comparing the results of the adjusted CCAPM model with the traditional CCAPM model in explaining the stock return in the Iranian capital market shows that the explanatory power of the model in liquidity-adjusted CCAPM is 67% (when the transaction cost is based on CSspread) and 50% (when the transaction cost is based on cGibbs). But in the traditional CCAPM model, this explanatory power is 28% and 21% respectively. It means that the adjusted CCAPM can better explain the expected stock return in the Iranian capital market as compared to the traditional CCAPM. In this study, liquidity risk is expressed as the covariance between transaction costs and consumption growth. This is because the high sensitivity of transaction costs to fluctuations in consumption highlights the difficulty of converting investment funds into cash. The model presented in this study shows that neglecting the transaction costs and liquidity risk can lead to inaccurate estimation of the expected returns.

The results of this study show that traditional CCAPM underestimates the expected risk and return. This is a reason for the poor performance of the traditional model. In fact, the results of the study are in line with the results of previous studies. The consumption risk proposed in the model of this study has limited power to explain the expected stock return. But liquidity risk has a significantly positive effect on the expected stock return, so that, with no increase in liquidity risk, the expected stock return can increase. Furthermore, the analysis of the proposed model shows that the transaction costs have a significant effect on the expected stock return. In other words, this study theoretically and empirically supports the effective role of liquidity and transaction costs in asset pricing. According to the results, the adjusted CCAPM model is suggested as a more desirable model to estimate the returns in Tehran Stock Exchange. It is also suggested to evaluate other pricing models using transaction cost and liquidity risk in future research. This is because neglecting these two factors can lead to inaccurate estimates of expected returns. Moreover, the effect of other variables such as stock interest and growth on stock returns should be investigated.

Scientific theories are a way of knowing man. The main difference between this method and other methods is the testability of its claims. Testability helps to correct scientific knowledge of error. In economics, this knowledge is created by using econometric methods and models. Assumptions Econometric models are called auxiliary assumptions. Two fundamental problems in econometrics that challenge testability; ignoring auxiliary assumptions and statistically significant. The Duhem (1904) stated that if the auxiliary assumptions are inconsistent with the reality falsity method will be questioned. In the present study, the Duhem problem in the EMH and financial data structure has been investigated. In another section, the use and interpretation of statistical significance is studied, which is usually evaluated with an index called p < /em>-value. While the p < /em>-value can be a good statistical metric, it is often misused and misinterpreted. As a result, some reputable scientific journals have banned the use of p < /em>-values. To make good use of this criterion in the econometric methodology, the recommendations of the American Statistical Association in this field have been discussed. In order to deal with the role of auxiliary assumptions, it is necessary to specify tests and pay attention to the data structure.

The purpose of this article was to evaluate the relationship between the degree of concentration as a structural variable and advertising as a behavioral variable in Iran's  manufacturing sector. For this purpose, data related to ISIC 4-digit activities of Iran's manufacturing sector for the period  1375-1392  was used. While  using panel data and fixed effects method, the inverse U relationship between concentration and advertising in manufacturing markets was tested. The results indicated that there was an inverse U relationship between concentration and intensity of advertising in Iran's manufacturing industries. In addition, the findings of this study showed that size of market and profit margins had a negative significant effect on the intensity of advertising. During the period under review, the highest intensity of advertisements occurred in "soap and detergents and sanitary ware and cleaning and perfumes and cosmetics" ;  "motor vehicles" and  "home appliances" industries.

This study aims to estimate the bid-ask spread criterion based on the daily highest and lowest prices and to imply this criterion as a proxy for transaction costs. Then, using this type of transaction costs and liquidity, the consumption-based capital asset pricing model is modified. To perform experimental tests. Daily data is collected from 47 companies accepted on the Tehran Stock Exchange and for the period 2009 to 2018. This study is carried out on 20 portfolios formed based on liquidity criteria Liu (2006), DVOL, Size, and Gibbs. The results of this study show that the capital asset pricing model based on traditional consumption has a poor performance in explaining the return on cross-sectional stocks and liquidity-adjusted CCAPM can explain the bigger portion of cross-sectional return changes compared to the traditional CCAPM model. Also, the results show that the entry of trading cost variables and liquidity risk leads to improved CCAPM.

Liquidity is one of the most important aspects of development in financial markets and oneof the risk factors of financial assets. In this study, liquidity risk is also used as a factor tomodify the Epstein-Zin model, and its performance is evaluated against traditionalconsumption-based asset pricing models. In order to adjust the Epstein-Zin model in thisstudy, the liquidity factors of Liu (2006), Turnover ratio, Gopalan and cGibbs have beenapplied and the used data are for 48 stock companies from 2009 to 2017. The resultsindicate that the adjusted model has a higher adjusted-
 than the traditional consumptionbasedcapital asset pricing model and the traditional Epstein-Zin model, and indicates thatliquidity is a significant risk factor and adds a significant explanatory power to model.Thus, overall, the results of this study show that liquidity risk is a pricing factor and itsincorporation into pricing models leads to improved model performance.

 Recent studies have identified social capital as an important and influential factor in improving the management of environmental issues as it facilitates and strengthens the cooperation of environmental activists to solve problems in the field of the environment. This paper aimed to examine the role of social and human capital in the ways that environmental investments affect the quality of the environment in Iran.

 In this research, using the Kalman Filter approach and data from 1974 to 2016, the trend of changes in the effecting coefficient of environmental investment on the quality of the environment in Iran was estimated using the Eviews software. Then, in the framework of the Johansson cointegration method, while examining the stationarity of the variables by augmented Dickey Fuller unit root test and determining the number of convergence relationships using the effects and maximum eigenvalues tests, long-term relationships between social capital, human capital, and effecting coefficient of environmental investment was estimated using Maximum Likelihood method in Microfit software. Impulse response functions were also used to examine how the effecting coefficient of environmental investment on the quality of the environment reacts to changes in social and human capital.

 The results of estimating the effecting coefficient of environmental investment on the quality of the environment in Iran using the Kalman filter method showed that this coefficient has experienced a fluctuating trend in the studied period. The results of the estimation of long-term cointegration relationships suggest that there was a significant relationship between fluctuations of this coefficient and fluctuations of social and human capital. This means that with the increase of social capital in a society, the effecting coefficient of environmental investment on the quality of the environment will be strengthened and vice versa. The findings of the impulse response functions showed that applying a shock in social capital in a given period leads to stimulation and reaction of the affecting coefficient of environmental investment on the quality of the environment and, ultimately, the formation of a different level of equilibrium for this coefficient in the next periods. The results of the estimation of error correction vectors also indicate that, on average, 26% of each period nonequilibrium in environmental investment impact on the quality of the environment is moderated in the next period.

 Based on the findings of this research, the effectiveness and efficiency of environmental investments in improving the quality of the environment depend, to a large extent, on the knowledge and trust of the community in relation to the programs and activities defined in this regard, as well as the level of their effective participation in the definition and implementation of these programs and activities. In other words, the one-way and top-down approach of the government in the field of environmental protection is not enough to guarantee the achievement of the goals set for environmental management. It is necessary to design and provide the mechanisms required to attract the participation and trust of individuals and different stakeholders involved in the environmental management process at all levels.