درخت حوزه‌های تخصصی

This paper addresses the absence of a suitable criterion for measuring information asymmetry between managers forecasting earnings and analysts forecasting earnings through statistical methods. Besides, this paper aims to provide a model of information asymmetry, emphasizing the role of financial and managerial criteria. This is applied qualitative and quantitative research (mixed method). The library method is used to prepare and formulate theoretical bases. In addition, the field method is used for collecting data to measure and identify indices and modeling. Factor analysis was used to analyze the data, following identifying the dimensions and variables of financial and managerial criteria of information symmetry to eliminate extraneous factors and classify. The following five main dimensions were determined, including corporate profit forecast, corporate governance, capital market, capital return, and management characteristics of the company. Then, the modeling was done using fuzzy mathematics through triangular numbers, Mamdani implication, and center of gravity methods. The final results of the study of the company listed on the Tehran Stock Exchange show that the level of information symmetry in the range of zero to 100 equals 55.1, to predict the company's profit is 48.54; corporate governance is 56.95; the capital market is 1/59; capital return is 61.07, and managerial characteristics of the company are 67.84. Finally, we examined the factors affecting the information asymmetry obtained from fuzzy neural networks. The findings show a higher prediction accuracy of fuzzy neural network methods than other related prediction methods.

The purpose of this research is to provide a growth model of fixed assets based on the financial criteria of companies admitted to the Tehran Stock Exchange. The current research is applied in terms of objective classification and descriptive-correlation in terms of method. The research method is de-ductive-inductive. The statistical population of the current research is all the companies admitted to the Tehran Stock Exchange in the period from 2012-2021 and the financial information of 101 companies are use. Research hypotheses were tested using artificial intelligence algorithm. In this research, investment in fixed assets has been consider as a dependent variable, and financial criteria has been considered as primary independent variables. The results of research hypotheses testing using the methods of linear and non-linear algorithms of artificial intelligence PINSVR and KPLSR in predicting fixed asset investors of companies and by calculating the three errors criteria MAE, MSE and SMAPE in annual fixed assets. The asset forecasting in the next year of companies showed that the error difference between linear models and non-linear models is not so great that it can be claim that linear models are ineffective in predicting asset growth so that artificial intelligence algorithms are capable of predicting investment in company assets.

EXTENDED ABSTRACT INTRODUCTION The number of revealed corruption-related crimes is one of the major challenges of Iran which has been significantly increased in recent years. According to official reports, although only two cases of embezzlement were reported in 1990s and 2000s with a total value up to $ 800 million, administrative corruption has significantly increased in 2010s with 13 large corruption cases and a total value up to $ 14 billion and a growth of over 1500% compared to 1990s and 2000s (The Iranian Students News Agency[1] (ISNA), 2017). The spread of such amount of corruption in administrative and bureaucratic system of the country can have irreparable economic and social consequences. The statistic investigations have shown that macroeconomic indicators including economic growth, employment, unemployment, poverty, inflation and income distribution have become less favorable in 2010 compared to previous decades (Statistical Center of Iran, Iran Statistical Yearbook, 2017). In this regard, the following questions can be raised: is there a relationship between corruption and the indicators of income distribution and poverty in the country? Since the volume of international and unilateral sanctions on Iran has increased in the 2010s, does such a widespread growth in the volume and value of corruption cases in Iran have a relationship with sanctions?   METHODOLOGY Therefore, the purpose of the present study is to investigate the effect of sanctions on causal relationship between corruption, income inequality and poverty in Iran during 1984 to 2020. For this purpose, the indices of per capita income, poverty line, administrative corruption and control of corruption, Atkinson and Gini have been utilized to investigate their interactions through Toda-Yamamoto Causality Test.   FINDINGS Findings of research show that:   sanctions have affected the causality of per capita income on administrative corruption. sanctions have affected the causality of corruption control on per capita income. sanctions have affected the causality of income distribution on per capita income. sanctions had no effect on causality of per capita income, on Atkinson Index and vice versa. sanctions had no effect on causality of per between poverty line, on administrative corruption and vice versa. sanctions had an effect on causality of per between poverty line, on corruption control and vice versa. sanctions had a significant positive effect on poverty line, but had no significant effect on GINI index. In other words, sanctions have affected the causality of income distribution on poverty line. sanctions had no effect on causality of poverty line, on Atkinson Index and vice versa. sanctions had no effect on causality of GINI index, on administrative corruption and vice versa. sanctions had no effect on causality of GINI index, on corruption control and vice versa. sanctions had a significant positive effect on administrative corruption, but had no significant effect on Atkinson index. In other words, sanctions have affected the causality of income distribution on administrative corruption. sanctions had a significant positive effect on Atkinson index, but had no significant effect on corruption control. In other words, sanctions have affected the causality of corruption control on income distribution.     CONCLUSION The purpose of the present study was to investigate the effect of sanctions on causal relationship between corruption, income inequality and poverty in Iran during1984 to 2020. For this purpose, the indices of per capita income p, poverty line, Atkinson, GINI, administrative corruption and corruption control were investigated. In general, the following results were obtained from the present study:   Income distribution is not an effective variable for poverty in Iran. Corruption is an effective variable for causality of poverty in Iran and its significance level is higher under sanctions condition. Corruption and poverty cannot properly explain the income distribution in Iran. However, the corruption control can be the cause of income distribution and poverty line is a proper representative for the cause of income distribution under sanctions conditions. Income distribution is a strong variable for causality of corruption in Iran. Poverty can properly explain the causality of corruption in Iran under sanctions condition, but is not the cause of corruption under normal condition.   According to the obtained results, it seems that sanctions condition is an effective variable for the relationship between variables of income distribution, corruption and poverty. However, the effective factors of income distribution need further investigations in future.