درخت حوزه‌های تخصصی

The labor market is one of the main markets in the national economy and the equilibrium of the labor market is one of the main concerns of policymakers in every country. Theoretical foundations and empirical studies show that labor demand is the main factor in creating employment and equilibrium in the labor market. This article evaluates the effect of fiscal policy on labor demand in Iran from 1976-2018. During this period, the government has implemented employment-generating projects, tax and insurance exemptions via annual development programs and budgets, and encouraged investment in areas with high unemployment rates. we examined the effect of current government expenditures, government development expenditures, and taxes to study the effect of government fiscal policies on labor demand. In this article, we estimate the dynamic function of labor demand using the ARDL technique. According to the theoretical foundations and empirical studies in the specified function, labor demand is a function of intermittent values and independent variables of Gross Domestic Product (GDP), real wages, capital stock, current government expenditures, government development expenditures, tax revenues, and dummy variables of imposed war and the UN Security Council sanction. The results showed that fiscal policy has not had a positive effect on labor demand in the Iranian labor market in the short and long term.

Sanctions increase managers' motivations to manage profits and the accumulation of bad news due to their negative impact on corporate profitability and cash flow. According to Jin and Myers (2006), this issue increases companies' stock price crash risk. The higher probability of stock price crashes indicates a stock price overvaluation. As a result, the expected return on such shares will be low. The current paper used the softmax model to calculate the probability of stock price crashes and the expected return calculated by the Fama-French three-factor model. The sample used in this paper includes 80 import- and export-oriented exchange companies from 2008-2021. The results of this paper indicate a positive and significant relationship between the sanctions variables and the probability of stock crashes. So, sanctions cause an increase in the accumulation of bad news and information asymmetry between managers and investors. The second part of the paper's results indicates a negative relationship between the probability of stock crashes and the expected return on the stocks in the Iranian capital market. Therefore, investors have relatively good analytical skills in the Iranian capital market due to its shallow depth and infrastructure problems. The results of this paper can be used in portfolio management to select stocks with a lower probability of crash and higher return.

The main reason for people investing in the stock market is to make a profit, which requires accurate information about the stock market, price changes and predicting its future trend. Therefore, investors need powerful and reliable tools for forecasting Stock prices in the future. The main purpose of this study is to present a method based on wavelet denoising and dynamic time warping to identify the stock price pattern in the Tehran Stock Exchange. In this regard, first, using the wavelet denoising preprocessing step, noise is removed from the stock price time series, and then the extracted data is used as input to the dynamic time warping prediction model. MATLAB software version 9.11 was used to analyze the research data. The statistical population of the present study includes 3 shares among the shares of steel industry companies of Tehran Stock Exchange. The research was conducted in the period 1395 to 1398. The results show that the predictions obtained from the dynamic time warping method equipped with the wavelet denoising preprocessing step in comparison with the predictions obtained from the dynamic time warping method without the wavelet denoising preprocessing step in all three shares studied, have been associated with much less accuracy and error.

EXTENDED ABSTRACT INTRODUCTION In the last decade, concern about the environment has increased increasingly around the world. One of the serious environmental challenges is urban solid waste, the management of which has been raised as one of the main concerns of human societies. According to the World Bank, by 2025, more than 1.4 billion people will live in cities around the world, and each of them will generate an average of 1.42 kilograms of municipal waste per day. Estimates show that municipal waste worldwide triples every year. The annual generation of municipal waste worldwide has increased from 0.68 billion tons to 2.2 billion tons. Based on available data from countries up to 2012, the World Bank has published a report focusing on municipal solid waste generation. Waste is materials or objects that are discarded or thrown away. Solid waste is waste or discarded materials and objects obtained from industrial, commercial, mining, agricultural, and general daily activities (Ugwu, Ozoegwu & Ozor, 2020). Solid waste is one of the serious environmental issues in developed and developing countries. Solid waste management is a major challenge in urban areas around the world, especially in developing countries. The main reason for this challenge is the rapid population growth along with the expansion of cities, the reduction of financial resources, and the weakness of urban planning. Human activities and changes in lifestyle and consumption patterns have led to an increase in waste production rates (Bovard & ilanloo, 2019). Controlling environmental pollution, including waste, is an important part of human duty in maintaining human health, which has a special place in new sciences and techniques according to economic health standards. Waste production is inevitable in human daily life and population increase will increase it. Municipal solid waste is defined as waste generated by human, commercial, and construction activities that are collected and treated by municipalities (Xiao, Dong, Geng, Tian, Liu, & Li, 2020). The main composition of these wastes is almost the same in different countries of the world. However, the amount of production waste, density, and share of each part of it is different from country to country and city to city. This difference is caused by economic development, geographical location, weather conditions, and cultural and social considerations (Afshar Kazemi, Eftekhar & Omrani, 2014). About, 2.01 billion tons of municipal solid waste is produced annually in the world, of which at least 33% is not environmentally managed. Worldwide, waste generated per person per day averages 0.74 kg but varies widely from 0.11 to 4.54 kg. Of course, it is predicted that by 2050, the amount of waste produced in the world will increase to 3.40 billion tons, which is equivalent to 2 times the population growth in that year. The East Asia and Pacific region produces the most waste in the world at 23%, while this number for the Middle East countries is about 6%. Of course, it is expected that the total waste production in this region will more than double by 2050. It is worth noting that in these areas, more than half of the waste is discarded without reuse. This waste growth will bring many adverse environmental, health, and welfare consequences. Therefore, it requires basic measures.     METHODOLOGY The purpose of this study is to evaluate the efficiency of circular economies in Persian Gulf countries in terms of Municipal Solid Waste Management. This research is applied in terms of purpose and descriptive survey in terms of data collection. Its statistical population was formed from Persian Gulf countries. In this research, the per capita value of MSW production and the dimensions of the social progress index "basic human needs", "basics of well-being" and "opportunity" are used as input and recycling as output in the model. SPI indicators show how well society can cover the needs of its members and improve their quality of life. Data on MSW generation and recycling rates were obtained from the World Bank and data on the three SPI indicators were extracted from the Essential Social Progress website. The data of this research is for the year 2021. The DEA model was implemented in LINGO Softer.   FINDINGS The findings show that the countries of Kuwait (1), United Arab Emirates (0.952), Saudi Arabia (0.654) and Iran (0.453) respectively have the highest circular economy performance compared to other countries in the Persian Gulf. The aim of this study is to analyze the efficiency of DMUs from the perspective of variable returns to scale (VRS).   CONCLUSION The main purpose of this evaluation is the efficiency of circular economy in the Persian Gulf region in terms of urban solid waste management. The results show that Kuwait and the United Arab Emirates respectively have the highest circular economy performance compared to other areas of the Persian Gulf. Data analysis shows that according to the statistics of the World Bank, these three countries recycle 21% and 20% of manufactured products respectively. They have good performance compared to other input indicators. It should be noted that these results can be partially under the conditions of the Covid-19 pandemic. As, Iran will produce more solid food with more population and monitoring of health protocols. This could have caused it to fall to fourth place. Data analysis shows that production per capita in Iran is much lower than other countries. Iran has an average performance in terms of SPI indicators. But in terms of long-term performance, it has been ranked fourth. The reason for this is its very low recycling. Policymakers should use advertising, education, etc. to increase the collection rate of recyclable products. In order to improve their performance, the system needs to produce and at the same time increase the recycling rate. Education and public investment can help in this regard. Also, it should improve its performance in terms of SPI indicators. The province also improves access to basic medical care, food, water and housing. Also, they should provide access to basic education and even advanced education for those in the country who wish to increase their knowledge and skills. According to the results, it is suggested that other Persian Gulf areas improve their performance in recycling. The country's data shows that they recycle less than 10% of their production. It is suggested to evaluate the performance of the Persian Gulf with Europe for a better explanation. It is also suggested to use other indicators of reuse in calculating circular economy performance. Since it is new for environmental management and circular economy at the study level. This means that the special indicators of recycling evaluation and SPI indicators in previous years are different from previous years and practically will not reach a comparative output. For this purpose, it is suggested that future researchers study in different years with an interval of 5 years in the same field and compare the results of different years. However, there is no validity and reliability of this research. But more thought is needed in generalizing the results.

EXTENDED ABSTRACT INTRODUCTION Scarcity and justice have always been the concerns of human beings. To make a choice between these two concepts, capitalist systems have given priority to scarce resources and the optimization of their use in the hope that economic growth will also provide justice. In recent decades, environmental degradation, increasing pollution, extinction of various animal and plant species, climate change and global warming have cast doubt on the unbridled growth approach adopted by the capitalist system. This approach has proposed the idea of ‘sustainable development’ as a solution to those problems. The concept of sustainable development, which was proposed in 1980, was actually a response to the destructive environmental and social effects and to the concept of economic growth in general, in such a way that this opinion originated from environmental trends (Zakerian et al., 2014). Based on this idea, it is possible to take advantage of development opportunities through the interaction and adaptation of social, environmental and economic goals. The approach focuses on sustainable development, which gives importance to the equitable distribution of economic growth benefits (social and environmental goals) and pays attention to scarcity (economic goals). On this basis Sustainable development is development that can continue for a long period of time without causing damage to the environment (Shafiei, 2018). These points bring one to the conclusion that, to exploit resources, not only economic interests but also social, environmental and economic goals should be taken into consideration.Since in recent years, the continuation of economic growth along with the development of urbanization and population growth has led to further destruction of the environment and the reduction of scarce natural resources, including water resources, this issue has led researchers to pay more attention to the environmental aspect of sustainable development. From the perspective of sustainable development, priority is given to activities that, while providing the most economic benefits, put the least pressure on water resources and the environment of the region (Nasrollahi & Zarei, 2017). In Iran as a developing country, environmental issues took on a wider and more visible dimension. Although Iran has devised measures to overcome environmental challenges, the gap with global indicators is large, and according to many activists in this field, Iran is approaching a multi-faceted environmental crisis (Rezayan ghayehbashi and Marzban, 2020). The location of Iran in the arid and semi-arid climatic region and the uneven distribution of rainfall have led to the aggravation of the water shortage in Iran, so that the increase in demand has faced the country with the limited supply of water resources.Therefore to prioritize the economic activities, it is necessary to pay attention to the water shortage issue and the other environmental considerations. In this regard, the present study has identified and prioritized the economic activities of Isfahan Province by using the criteria for different dimensions of sustainable development and with an emphasis on the importance of water resources. To this end, input-output models and the TOPSIS method are innovatively combined to provide a proper picture of prioritizing the economic activities in the province. The rest of this article is organized in several sections. The second section provides the research background. The third section presents the Background studies, and the forth section research method and the data. The findings of the study are put forth in the five section. The last section is dedicated to the conclusion of the study and suggestions for future research. METHODOLOGY Therefore, the present study prioritizes the economic activities in the province of Isfahan by combining an input-output model and the TOPSIS method as well as considering the six criteria of water abstraction, energy consumption, pollution, intensity of inter-sectoral relations, job creation, and value added. To achieve the objectives of the research, first, the national input-output table of 2016, the Central Bank data and the SFLQ quota method were used to prepare a twenty-sector input-output table for Isfahan Province. Then, using the internal multipliers of the province, the forward and backward linkages, direct and indirect water intensity, direct and indirect energy intensity, and direct and indirect employment of the economic sectors of the province were calculated. Shannon weighting method was also used to weight the criteria. Finally, the prioritization of the sectors in Isfahan Province was done with the TOPSIS method and the desired criteria. In the following sections, more details are provided on the methods collection in this research. Shannon Entropy MethodShannon entropy method served to determine the weight of each criterion in the study. This method is an alternative to using the opinions of experts, and it is advantageous in that the opinions and personal judgments of individuals are not involved in it; the weight of each criterion is determined based on a decision matrix (Dorostkar Ahmadi and Dehghani, 2020). There were a number of steps to determine the weights by Shannon entropy. First, all the decision matrices were normalized through Equation ‎(1) and the value of E (entropy) was estimated using Equation ‎(2)   In Equations‎(1) and ‎(2), , the values studied in the research and m and n represent the number of the sectors and indicators studied in the research.Next, the weight vector was calculated, and the weight of each criterion was determined using Equation‎(3).                Topsis ModelThe TOPSIS model, as a multi-criteria decision-making method, is a simple but efficient method of prioritization. It is one of the best methods in which m options are evaluated by n methods.This technique, which is a compensatory model based on MCDM methods, follows a mathematical logic. This logic first introduces a "positive ideal solution" and a "negative ideal solution". The positive one is a solution that increases the profit criterion and decreases the cost criterion. In other words, the negative ideal solution has the opposite value of the positive one. Then, all the examined options are compared with the best and the worst options, and the linear distance of each option from the best option and the worst option is measured. Finally, the option at the most distance from the worst option and the least distance from the best option is selected as the best or the optimal option (Khatami Firouzabadi et al., 2013).The TOPSIS method can be described as a sequence of steps as follows:1) Calculate the normalized decision matrix. This regards the formation of a decision matrix with a number of criteria and options. The criteria are placed in the columns and the options in the rows of the matrix.             2) Calculate the weighted normalized decision matrix. The weighted normalized value is calculated with Equation ‎(4) where  is the weight of j criteria, .=1 3) Determine the positive ideal and negative ideal responses: Thus, the best response (A+) is selected as the option that contains the highest values of the criteria, and the worst response (A-) contains the smallest values. In EquationError! Reference source not found.‎(6), O corresponds to the utility criteria, and I corresponds to the cost criteria.4) Calculate the Separator Dimensions: Using the Euclidean criterion, the distance of each point from the ideal response (positive and negative) is estimated through Equation‎(7): (The distance of option i from the positive ideal) (The distance of option i from the negative ideal)5) Calculate the relative proximity to the ideal response. The relative proximity of option to A+ is as Equation‎(8). There is also ∈ [0 to 1]. 6) Rank the priorities. Using Ri, the options can be ranked in a descending order. In other words, any option with a larger Ri has a higher priority (Moazzami Gudarzi et al., 2014). FINDINGS To show the importance of the simultaneous consideration of different criteria, the results of the study were prioritized once with the water intensity criterion and once without it. This was done with regard to the significance of water resources.For example, in the prioritization of the sectors according to the water intensity criterion, the agriculture sector was in the twentieth place, while this rank was the second in the prioritization without considering that criterion. Also, in the sector of "manufacture of leather apparel", this criterion was downgraded from the ninth to the thirteenth rank considering the water intensity criterion. Similarly, the sectors of "manufacture of textiles" and "manufacture of rubber and plastic products" were ranked 7th and 9th, respectively, while the ranks of these sectors without considering the water intensity criterion were fifteen and twelve. In the meantime, the priorities for the sectors of "wood and products of wood", "manufacture of machinery and equipment not classified elsewhere" and "construction" remained constant. CONCLUSION The results of this prioritization showed the importance of ranking the sectors in terms of water intensify as a criterion. As it occurred, through the TOPSIS method, the priority of most sectors emerged to be different from the results of prioritization with no water intensity considered. The difference was very significant in some sectors. For example, in the prioritization of sectors according to the water supply criterion, the agriculture sector was in the twentieth place, while it shifted to the second rank when prioritized without that criterion considered. Similarly, for the sector of manufacture of leather apparel, the involvement of the water intensity criterion downgraded the sector from the ninth to the thirteenth rank. Thus, the change in the rank of a sector depends on the water level of that sector. As it was found, the agriculture sector was in the second place in the prioritization without considering the water supply criterion, while the rank of this sector moved to twenty with the inclusion of the water supply criterion. This role of water intensity suggests the importance of paying attention to the water level. Therefore, it is necessary to prioritize the economic sectors in a region in accordance with the climatic conditions there. It is also advisable not to establish water-demanding activities such as steel industries and cultivation of highly water-consuming products in low-water areas.

Financial statements are critical to users, as the increasing fraud cases have left behind irreversible impacts. Hence, this study aims to identify the appropriate financial ratios for fraud risk prediction in the financial statements of companies listed on the Tehran Stock Exchange within the 2014–2021 period. The study is based on data from 180 companies listed on the Tehran Stock Exchange, encompassing a total of 1440 financial statements. To select the most appropriate ratios for fraud risk prediction, all financial ratios were tested by three metaheuristic algorithms, i.e., genetic algorithm, grey wolf optimization, and particle swarm optimization. Metaheuristic and data mining methods were employed for data analysis, and these analyses were conducted using MATLAB R2020a (MATLAB 9.8). According to the research results, the fitness function yielded 0.2708 in particle swarm optimization (PSO). With an accuracy of 72.92% after 19 iterations, PSO was more accurate and converged faster than the other algorithms. It also extracted 11 financial ratios: total debts to total assets, working capital to total assets, stock to current asset, accounts receivables to sales, accounts receivables to total assets, gross income to total assets, net income to gross income, current assets to current debt, cash balance to current debt, retained earnings and loss to equity, and long-term debt to equity. The support vector machine (SVM) classifier was then employed for fraud risk detection at companies through the ratios extracted by the proposed algorithms. The accuracy and precision of financial ratios extracted by PSO and SVM were reported at 80,60% and 71,20%, respectively, which indicates the superiority of the proposed model to other models. Considering that the results obtained from the performance evaluation of financial ratios provided by PSO-SVM demonstrate the capability of this method in predicting the likelihood of fraud in financial statements, it can assist financial statement users. By incorporating these ratios about the performance of the target companies and comparing them with those of other companies, users can make more informed decisions in economic decision-making, investments, credit assessments, and more, ultimately minimizing potential losses and risks.