ایمان داداشی

The aim of this study was to identify the factors affecting the organizational structure, operational mechanism and quality of auditing in Iranian auditing firms. A number of samples were collected to collect the information required for the research of 386 independent auditors from auditing firms, and the same number of questionnaires were distributed and collected among them for answering. The data collected by the questionnaires were analyzed by SPSS and Smart PLS software using structural equation modeling. Analysis of research hypotheses using structural equation modeling at 99% confidence level showed that the size of auditing firms, legal system, internal and external quality control of auditing firms and corporate governance mechanisms are positive factors affecting audit quality. Therefore, it can be said that with the increase of each of the factors of the size of auditing firms, legal system, internal and external quality control of auditing firms and corporate governance mechanisms, the quality of auditing increases. The legal system and internal and external quality control are among the factors that enhance and affect the organizational structure of auditing firms. The size of audit firms and internal and external quality control are among the factors affecting the operational mechanism of the audit. Therefore, it can be said that by increasing the size of auditing firms and quality control of internal and external auditing firms, the operational mechanism of auditing is improved. Finally, the results showed that the organizational structure of audit firms and the audit operating mechanism have a positive and significant effect on audit quality.

The purpose of the current research is the application of blockchain with a comparative approach of its effect on risk, flexibility and evaluation of internal control. This research is applicable in terms of purpose, and descriptive-analytical in terms of method and data collection method. The statistical population includes managers, senior experts, and employees of the Iranian Auditing Association and audit institutions, among which 306 people were obtained based on Cochran's formula. The validity of the questionnaire was confirmed using the content validity method. Cronbach's alpha coefficient was calculated to evaluate the reliability of the questionnaire; it was more than 0.7, which indicates the reliability of the questionnaire; and SPSS and Smart-PLS software were used for the relationships between research variables. The results of the research show that the lack of a specific protective framework in blockchain has a negative and significant effect on the management of internal audit control; internal audit control management has a positive and significant effect on improving risk management and financial performance of the company; internal audit control management has a positive and significant effect on increasing the financial flexibility of internal audit; and internal audit control management has a positive and significant effect on the improvement of internal control evaluation.

Today, due to economic volatility, the topic of stock price forecasting and investment risk has particular importance to stock traders. Recently, fuzzy logic has been used to model dynamically candle stick in order to stock trading analysis and predicting investment risk in stock trading but it has the challenge of sensitivity to the number of non-useful stock trading samples and the inappropriate tuning of membership functions designed for candlesticks patterns. In this paper, we propose the regression method based on radial basis function as a pre-processing step to reduce the insignificant stock trading samples and then we use the particle swarm optimization method to adjust the values of fuzzy membership functions of the candlestick patterns optimally. The jaya and firefly particle swarm based are able to optimize fuzzy membership functions of candlestick patterns robustly. The results of the proposed method on top iranian stocks trading companies show best performance of the proposed method.

ethical and cultural factors in the auditing field; due to the property of being invisible; They are not easily observable, and for this reason, asymmetric information appears and the company and the capital market move out of their optimal balance, which means market failure. Based on this, moral and cultural factors affect the interests of auditors and company stakeholders. The purpose of this research is to investigate the role of cultural factors and professional ethics of auditors on the interests of companies in the form of dynamic game theory.

The current research is of developmental type. In the present research, the game theory approach has been used. The first actors were the managers and board members of the top 50 capital market companies. The second community is the trusted members of the trusted auditing institutions of the Stock Exchange Organization. A questionnaire was used to collect information.

In this research, three neural network approaches of support vectors, Bayesian and Crossberg anti-propagation were used to repeat the game and create Nash equilibrium. Based on the results of Crossberg's anti-diffusion method, it has higher accuracy in modeling the behavior between players.

The results showed the fact that the increase of cultural and ethical indicators have a negative effect on the price and a positive effect on the company's sales. As a result, the improvement of cultural and ethical indicators increases the interests of all stakeholders and actors active in the capital market.

Contribution:

Deviation from cultural and ethical indicators by auditors increases inefficiency in the company and reduces the level of interests of the beneficiaries.

Moral hazards reduce public trust in published information and increase information asymmetry in the capital market, and through this channel it affects the value of the company and reduces the interests of companies. Accordingly, the purpose of this research is to examine the role of auditors' professional ethics on the interests of companies in the form of dynamic game theory. The current research is of developmental type. The current research is in the field of game theory. The first player was the managers and board members of the top 50 capital market companies (152 People) and the second player was the trusted members of the trusted auditing institutions of the Stock Exchange Organization with the first rank (123 People). To estimate the model, information related to the indicators has been collected using a questionnaire. Based on the results, it can be said that auditor bias has a negative effect on the interests of companies in the form of dynamic game theory. The increase in the risk of fraud leads to a decrease in profits and an increase in the level of investment inefficiency in companies.

 In most return forecasting models, the return of the total market is used as one of the factors affecting the return of securities. In most of these models, such as the pricing model of capital assets and Black-Scholes, the data distribution is assumed to be normal. This is while the distribution of the total return is not necessarily normal and often has a significant difference from the normal distribution. If such a hypothesis is confirmed, the expected return predicted by these models will not be very effective in financial decisions. The purpose of this research is to model the total return of Tehran Stock Exchange based on the Laplace distribution and examine the dependence of the total return fluctuations on the desired distribution. In order to examine the distribution of the total daily return and its weekly fluctuations, data related to a 15-year period between 1387 and 1401 and R statistical software were used. The data analysis showed that the total daily return followed the Laplace distribution and the weekly fluctuations of the total return followed the distribution obtained based on the Laplace distribution. These findings make the use of models with the assumption of normality of total return to predict stock returns in Tehran Stock Exchange a major challenge and are a clear proof of the ineffectiveness of these models. .

In this research, the effect of day characteristics as one of the most important environmental factors influencing the decision-making process has been investigated. Daily observations of 2011 to 2020 and ARDL regression were used. After performing statistical pre-tests, including mean, collinearity, autocorrelation, and non-homogeneity of variance. The results showed that variables such as wind speed, gold with a lag, daylight savings, January effect, and the calendar effect of holidays have a positive and significant effect on stock market returns. However, the gold variable has a negative effect on it. Additionally, Variables like gold with a lag, humidity, and wind speed have a positive effect on the volume of transactions. On the other hand, variables such as weather with a lag, horizon level, exchanges rate, gold with a lag, daylight, and Saturday effect have a negative effect on the volume of transactions.  Furthermore, the variables of temperature, humidity with one lag, Daylight with one lag, gold, and the calendar effect of holidays have a positive effect on the number of transactions. Conversely, variables like wind speed, weather with one lag, daylight, currency growth rate, horizon level, and the effect of Saturday have a negative effect on the number of transactions.

Consumers often avoid online shopping and e-commerce websites due to the lack of transparency of information on the websites. In this research, it seeks to investigate the effects of electronic commerce transparency and mechanization on the accounting systems of Tehran Stock Exchange member companies that do business online. The research method is based on the mixed method. Data (accountants & financial experts) have been collected by implementing the foundational data theory method and conducting 18 in-depth interviews using the protocol and based on the multiple purposeful sampling method (intensity and snowball). Also, categories and themes were determined by using open coding, axial coding and selective coding using ATLAS.ti software. In a quantitative study, 298 people were randomly selected using a standard questionnaire. Data analysis was done using Lisrel software. The obtained results show that 1) transparency of products, transparency of buyers and transparency of transfers have a significant effect on the transparency of financial information; 2) the perception of information transparency has a significant effect on the intention to buy online and increase consumers; 3) Perceptual risk also has a significant effect on repurchase and perception of transparency on information transparency.

The main purpose of this study is to investigate the risk situation (idiosyncratic risk, market risk and total risk) in Firm life cycle stages and the impact of State and non- State ownership on this relationship. In this regard, first, using the Fama-French Three-Factor model, the idiosyncratic risk in the sample firms was estimated. Market risk was calculated based on the standard deviation of the annual return of the stock index and the total risk is calculated by sum of market risk and idiosyncratic risk. The different stages of the companies' life cycle were determined using the patterns obtained from the three categories of cash flow statements (operating, investment and financing). In this research, the data of 136 firms have been used and in order to examine the research hypotheses, six regression models of panel data have been used. Respectively, idiosyncratic risk, market risk and total risk considered as dependent variable in these models. The results of estimating the final models show that idiosyncratic risk, market risk and total risk in the stages of introduction, growth and decline are higher compared to the stages of maturity and shake-out of firm life cycle stages. In addition, the results of this study indicate a significant effect of State ownership on the relationship between idiosyncratic risk and life cycle stages of firms listed in the Tehran Stock Exchange.

Today, achieving the economic goals of any country is impossible without the public participation of society, because the accumulation of micro-savings of the people leads to the formation of capital, which economists consider one of the most important factors of economic development. Therefore, the purpose of this study is to identify the components affecting the financial stress of Individual capital market investors and to explain the pattern of relationships between economic and non-economic components of financial stress and its consequences. Hypertext technique was used to identify items of financial stress and its consequences with the help of library and field resources; For group decision making and component classification of items from Lawshe index and fuzzy Delphi technique; Using the Demetel method, the relationships between economic and non-economic components were modeled. This research is an exploratory mix in terms of research strategy, survey and one-time in terms of execution time, and MAXQDAv10, SPSS and Excel software have been used to analyze the data collected in the country's securities market. Findings include identifying the pattern of relationships between 10 components (5 economic components and 5 non-economic components) for the variable of financial stress and 4 components (2 economic components and 2 non-economic components) for the variable consequences of financial stress. The results show that the component of "economic factors" with a net effect of 1.6858 is the most important component of the cause of financial stress of Individual shareholders and the component of "consequences for society" with a net effect of -2/2013 is the most important component of the outcome (effect) of financial stress. By rooting out the financial stressors of Individual shareholders, a very effective ground is provided for Individual investors to create new horizons of decision-making in the capital market, economic sub-sectors and especially capital market policy areas.

Tax revenues are one of the most important sources of government income and provide a major part of its expenses. The main goal of this research is which of the decision tree algorithm and linear multivariate regression methods provides a better prediction of tax evasion of legal taxpayers. Based on the theoretical foundations and background studies of a set of variables including 57 financial and non-financial indicators at three macroeconomic levels, taxpayers and tax auditors, in a sample consisting of 964 cases of legal entities at the level of the Mazandaran General Administration of Tax Affairs for the years 2012 to 2019 with The use of Python and Stata software has been investigated. At first, the sine-cosine identification algorithm was used to select the influencing variables. The results of the data analysis showed that the variables at the level of taxpayers and tax auditors are more effective in predicting tax evasion. Also, the findings indicate that the predictive power of the decision tree algorithm is higher than the linear multivariate regression

The aim of the current research is to Identification of non-fragile variables affecting quality of tax audits, considering the environmental characteristics, conditions governing taxpayers and tax auditors. The present applied research was carried out in a period of 2012-2021 and using the data of 511 tax files of Mazandaran province. In order to present the model, first, through interviews with experts and a review of research literature, 64 factors affecting the quality of tax audits were identified in the form of three groups of characteristics of taxpayers, tax auditors and macro factors, then the relevant data were entered into BMA, TVP- DMA and TVP-DMS models. Based on the error rate, the BMA model had the highest accuracy. After estimating the model, 17 main indicators in tax auditors(the tax audits quality of past period, work experience, individual or group handling, auditor expertise, auditors' use of information, auditor's workload, conducting audits in several tax sources , dealing with related parties, the existence of an unofficial invoice, using other people's business card); Intra-company variables(accrued earning management; debt ratio) and macroeconomic variables(inflation, unofficial exchange rate, tax complexity, tax fairness, business environment index and social capital index) were identified as influencing variables. Based on the results, it is quite evident that tax audit quality has a multidimensional nature. Considering the lack of comprehensive research in the field of tax audit quality in this regard, a multifaceted model has been designed; It provides a comprehensive view of the quality of tax audit.

Bankruptcy Prediction is one of the branches of finance that has received more attention in in recent research as bankruptcy patterns have been developed. In most of the researches in the field of Prediction the financial performance of companies and in particular, bankruptcy, only Predicting or comparing the predictive power of models using historical information of financial statements has been done. Since historical accounting information has been used more in Iran, the main purpose of this study is to consider the effects of inflation on input variables in designing a bankruptcy prediction model. Therefore, the variables in design of two different models were classified into two groups of financial ratios, adjusted and historical. Then, the ratios were identified using the LARS algorithm that had the highest ability to differentiate between bankrupt and non-bankrupt companies. Finally, the final bankruptcy prediction model was designed using the logit regression test and SVM and Naive Bayesian algorithms. For this purpose, the data of 50 companies listed on the Tehran Stock Exchange were used, which had experienced bankruptcy according to Article 141 of the Commercial Code. The results of this study indicate that the financial ratios adjusted based on the price index are more suitable predictor for corporate bankruptcy. Also, the bankruptcy prediction model designed by SVM algorithm can be a very good predictor for corporate bankruptcy with 99.4% accuracy.

The capital structure influences various management decisions such as cash maintenance. The capital structure of companies does not necessarily correspond to the structure of the goal. Companies adjust the capital structure by considering adjustment and deviation costs. The role of liquidity in the dynamic nature of capital structure, especially the speed with which companies adjust their capital structure to the goal, is examined. It seems that a company with higher liquidity will face lower costs associated with issuing new securities and is therefore more inclined to quickly correct any deviations from its optimal leverage ratio from its target ratio. To verify this claim, In this study, 106 companies listed on the Tehran Stock Exchange between 1384-1398 were examined. Through rolling regression, the effect of each heading of cash flow statement on the adjustment speed in upper and lower lever companies was tested. The results show that the speed of reaching the optimal leverage in companies with low optimal leverage is higher than companies with high optimal leverage. This indicates a lack of attention to future debt capacity in companies listed on the Tehran Stock Exchange. The results also show that the only cash flow from operations is a moderating factor in the speed of adjustment of the optimal leverage for companies above the optimal leverage.

Governments need stable and reliable financial resources to carry out their public duties, and taxes have long been one of the most important sources of funding for governments to carry out their duties. Preventing or reducing the amount of tax evasion has been one of the important concerns of governments in the economic field in recent years. In this research, the category of tax evasion using artificial intelligence and focusing on a set of 57 financial and non-financial indicators at the macro-economic level, taxpayers and tax auditors, in a sample including 978 legal taxpayers' files of the General Directorate of Tax Affairs of Mazandaran. It has been examined for the years 2011 to 2018. In this research, in order to extract effective features, sine cosine and gray wolf optimization algorithms were used, and decision tree and artificial neural network algorithms were used to model tax evasion and test features. Based on the results, the sine-cosine optimization algorithm along with the decision tree predictor has a lower error value than other models and provides a more accurate model for predicting tax evasion.

One of the most important issues in the field of financial management is that investors can distinguish favorable investment opportunities from unfavorable ones. One way to help investors is to anticipate the financial recovery (exit from helplessness) of companies with financial distress. Therefore, this study intends to provide a model for predicting financial recovery using the support vector machine algorithm for companies listed on the Tehran Stock Exchange. To achieve this goal, 54 financial variables were determined using the Lars feature selection algorithm and to test the accuracy of the results of the proposed model, the support vector learning algorithm was used. For this purpose, in the period of 2001-2018, the information of 167 helpless companies that were out of financial helplessness and revived was extracted. The research findings show that the research model accurately predicts the recovery time of the financially helpless company from financial distress with 74% accuracy.

 In the science of management and accounting, there is a principle that in the culture of investment, capital is free from risk and tends towards yield and profit. Therefore, according to the importance of investing in companies, in the current research, the moderating effect of risk management on the relationship between market orientation strategies and entrepreneurship of companies admitted to the Tehran Stock Exchange has been investigated. The statistical population of this research includes all the companies admitted to the Tehran Stock Exchange, which were analyzed using the systematic elimination sampling method of 127 companies. and were analyzed. To check the hypothesis, multivariate regression models and panel data method (fixed effects) were used using Eviews10 software. The findings of this study about the results of the first hypothesis show that risk management has a positive effect on the relationship between market orientation strategy with unsystematic risk and abnormal returns, but it has a moderating effect on the relationship between market orientation strategy and risk. It is not systematic. The results regarding the second hypothesis also indicate the impact of risk management on the relationship between the strategy of entrepreneurship with systematic risk, systematic risk of failure and abnormal returns, and this impact was positive.

The aim of this study was to investigate the effect of dispersion of revenue growth and stock returns on GDP forecast. Financial indicators from the Tehran Stock Exchange and the economic index of the research were used based on the rates announced on the website of the Central Bank. The statistical sample of the study included 119 companies that during the period of 2009 to 2018, informations was selected quarterly from companies listed on the stock exchange and hypotheses were tested using the VAR model and Granger causality in Eviews10 software. Based on the results of the analysis of hypotheses, it can be said that the dispersion of income growth with a break in the second period has a significant effect on GDP and also there is no two-way causal relationship between the dispersion of income growth and GDP. Therefore, the variable of income growth dispersion can not predict GDP. Also, dispersion in stock rate of return with a break in different periods does not have a significant effect on GDP and also there is no two-way causal relationship between dispersion in stock rate of return and GDP. Therefore, the variable of dispersion in the stock rate is not able to predict GDP.

Investment is one of the most important issues in the capital market; So that proper management can affect the value of the company and can ensure the survival of companies by increasing future cash flows. On the other hand, corporate investment is affected by various factors, including corporate risks, which can have different opportunities and threats for managers. Therefore, the main purpose of this study is to investigate the relationship between special risk and investment of companies listed on the Tehran Stock Exchange. For this purpose, during the eight-year period between 2011 and 2018, 143 companies and a total of 1144 year-companies have been studied. The present study is in the group of descriptive-post-event research in terms of applied purpose and data collection method, which has been investigated using composite data and Eveiws8 software regression models. Findings of the present study show that in general there is a positive (negative) and significant relationship between special risk and domestic (foreign) investment of companies in the study period.

In the present era, the provision of data-based products and services has led to the emergence of large economic and financial data at the level of organizations and companies,which is increasing in volume on a daily basis, so to manage and analyze data, obtain Data analysis skills have become essential.One of the issues that has a great impact on the output of economic and financial data analysis is the behavior of human resources and employees of organizations in the face of financial and economic data.Therefore, in this study, the effect of data analytical skills on the financial behavior of employees of the accounting department of non-governmental public institutions(Case study:Housing Foundation of the Islamic Revolution)was investigated and its results were presented.The methods of data collection in this research are library and field. In this research, the Housing Foundation of the Islamic Revolution and all employees of the accounting and support department are considered as a spatial territory with a sample size of 234.The data were analyzed using structural equation modeling and in particular path analysis technique using Smart PLS software.Findings show that data analytics skills have a significant effect on the financial behavior of accounting staff of non-governmental public institutions.The effect of data analytical skills on the financial behavior of female employees of the accounting department of non-governmental public institutions towards men is the same as that of experienced and inexperienced people.Also,data analysis skills have a significant effect on loss avoidance, overconfidence, collective performance, risk perception, economic forecasting and information retrieval.

One of the proven topics in psychology is the influence of people's emotion's on the decision-making process and their judgment about future events. In such a way that when people have positive emotions, they make optimistic choices and when they have negative emotions, they make pessimistic choices. Therefore, the emotional tendencies of investors indicate the margin of optimism and pessimism of shareholders towards a share. The purpose of this study is to use financial ratios to predict the emotional tendencies of investor's. To answer the research questions, data related to 97 financial ratios belonging to companies listed on the Tehran Stock Exchange during the period 2006 and 2018 have been used. In order to derive effective financial ratios, the neighborhood component analysis algorithm method was used, which ultimately led to the selection of 7 ratios. To measure the emotional tendencies of investor's, four indicators of relative strength, psychological line, trading volume and stock turnover adjustment rate have been applied. Finally, we have combined these indicators with the help of PCA method. The results showed that the ratios of percentage changes in sales changes, net profit to assets and the ratio of inventory changes to sales changes have a positive and significant effect on investor's feelings.

Report readability is an important tool for effective communication between companies and users of company information. Considering the importance of the readability of the report and its financial consequences, this research has been conducted with the aim of investigating the impact of the readability of the financial report on the financing policy, emphasizing the role of corporate governance in companies admitted to the Tehran Stock Exchange. The research sample includes the data of 142 companies admitted to the Tehran Stock Exchange, in the period from 2009 to 2018. Multivariable regression models with panel data have been used to test the research hypotheses. To measure the readability, two indices of fugue and text length were used, and to measure the quality of corporate governance, the model of Jabarzadeh Kongerloi et al. (2013) was used. The obtained results show that the readability of the financial report has a significant direct relationship with business credit and a significant negative relationship with cash retention. In other words, companies with more readable financial reports receive more business credit from suppliers. And they keep less cash. Additional analysis shows that strong corporate governance quality moderates and strengthens the positive relationship between liquidity and business credit, as well as the negative relationship between liquidity and cash holdings.

The success of any asset pricing model should depend on the proportionality of the real options in the value of the companies whose returns are tested in the model test. The purpose of this study is to investigate the Investment Option Model Indicators and its impact on stock returns. The statistical population of this study consisted of 146 companies in the period 1390-1397. This research is applied in terms of and in terms of nature and content is correlational and the research hypotheses are estimated using multivariate regression approach and panel data method. The results of the hypotheses test showed that investment options based on Grullon's four-factor model affect stock In addition, the results showed that there is a significant difference between the explanatory power of Fama and French's five-factor asset pricing model and the investment options model based on Grullon's four-factor model in explaining stock returns. And the explanatory power of the Grullon's four-factor model in explaining stock returns is more than the Fama and French five-factor model.

T Readability is an important tool for effective communication with company information users. Given the importance of readability reporting and financial consequences, this study aimed to investigate the effect of readability financial reporting on credit with emphasis on the role of management ability in listed companies in Tehran stock exchange. The sample consisted of all firms listed in Tehran stock exchange (tse) during the period 2009 - 2019 to test the hypothesis of research hypotheses using multivariate regression with panel data.To measure the readability of the two index FUG and the LENGTH of the text and to measure management ability, Demarjan et al. (2012) model was used. The results show that the readability of the financial report has a significant direct relationship with business credit and a significant negative relationship with cash retention. In other words, companies with more readable financial statements receive more business credit from suppliers. Additional analysis shows that management ability modulates and strengthens the positive relationship between readability and business credibility.

The main approach of this study is to provide solutions to managers, economists, and tax auditors. To have a clearer perspective of the transactional relationships that distress the taxpayer transaction tax also help them to choose the best strategy to improve tax revenue. In this paper the fuzzy Delphi method was used to identify the indicators affecting suspicious bank accounts. The data collected from experts familiar with the subject of research. Similarly, to rank the factors, the BMW technique was used, and the modeling method of partial least squares equations (PLS) have been used to determine the relationships between dimensions and indices. The results show the ranking of 26 sub-criteria of the research indicate that the two sub-criterions: performing operations and cash transactions less than the set ceiling and depositing cash in financial institutions and then transferring it quickly by remittance, are the first and second most important sub-criteria from below Cash operations group, respectively. The results of model also show that cash transaction factors have the most significant impact on bank account transaction tax.