mohammadjavad zare bahnamiri

The purpose of this study is to systematically review the impact of ownership structure on corporate social responsibility (CSR) disclosure using a meta-analysis approach. To this end, the research used domestic journals approved by the Ministry of Science, Research, and Technology, as well as international journals indexed in JCR and Scopus. Based on the meta-analysis protocol and after removing unrelated articles, 55 studies were selected: 12 Iranian studies published until 2021 and 43 international studies from 2002 to 2021.
The Lipsey and Wilson method was applied to analyze the data. Cochran’s Q test and the I² statistic were used to examine the heterogeneity among the studies. If the p-value in the heterogeneity test is less than 0.05, a random effects model is applied. Therefore, this model was applied to most of the study’s variables.
The findings show that government ownership, foreign ownership, and long-term institutional ownership positively influence CSR disclosure. In contrast, ownership concentration, executive ownership, and short-term institutional ownership have a negative effect. These results help regulators and stakeholders by highlighting key external corporate governance factors that can improve CSR practices.

AbstractThe behavioral finance perspective posits that fluctuations in security prices are significantly influenced by investors' emotional responses, which can, in turn, affect stock returns. Understanding the sources of stock price changes is critical within asset pricing theory, highlighting the necessity of exploring the effects of investor sentiment on stock returns. This study aimed to investigate how investor sentiment shocks impact both normal and abnormal returns in the oil products sector of the Tehran Stock Exchange. To measure abnormal returns, we employed the six-factor model developed by Fama and French (2018). Investor sentiment was evaluated using the Relative Strength Index (RSI), the Psychological Line Index (PLI), trading volume, and the Adjusted Turnover Rate (ATR). Data were collected from 12 companies in the oil products industry over a period of 1,584 months, spanning from 2010 to 2020. The findings revealed that the influence of sentiment shocks on normal returns was more pronounced than on abnormal returns. Conversely, the impact of normal return shocks on sentiment was initially positive but became negative over time. Additionally, positive shocks to abnormal returns adversely affected investor sentiment, with the peak effect observed after five periods. This research enhances the understanding of how investor sentiment shocks influence normal and abnormal returns in the oil products sector, offering valuable insights into behavioral differences among investors.

Financial reports are considered important sources of information for economic decision-making, such as commercial transactions, and management, through earnings manipulation, provides false or misleading information for decision-makers. On the other hand, tadlis leads to false representation in transactions, and the customer does not become aware of the truth of the subject of the transaction. Also, taqrir arises from putting one or both parties to the transaction at risk, which can lead to the invalidation of the transaction. This matter, on the one hand, leads to the failure of earning lawful sustenance, and on the other hand, creates a problem in enforcing Islamic rulings, which is one of the goals of the Islamic government. The present research, using a library method and accounting and jurisprudential resources, seeks to answer the question of whether earnings management can be an example of tadlis or taqrir in a transaction. From the jurisprudential perspective, if a person, before the transaction and knowingly, denies the existing defect in the sale item or states a perfection attribute that the sale item lacks, tadlis has occurred. Therefore, because the management team, before the conclusion of the contract and with knowledge, manipulates the earnings or loss of the business unit, earnings management can be an example of tadlis, and the management team, by manipulating profit, commits a forbidden act. Furthermore, the other party to the contract has the option to either annul or ratify the transaction, without the right to claim compensation. Also, earnings manipulation and management lead to the creation of perfection attributes that the company lacks or conceals the defect in profitability, which can be an example of taqrir in a transaction and lead to the corruption and invalidation of the transaction, because both parties or one party to the transaction may be exposed to unusual risk. However, if both parties are aware of the taqrir, the transaction will not be invalid.

The aim of this study is to investigate the effect of risk on the efficiency of banks using data coverage analysis. The statistical population of this study includes banks listed on and active in the Tehran Stock Exchange in the fiscal years 2012 to 2023. The collected data was examined using Excel software and then Deap software. This study is an applied research type. There are many methods in the field of applied research, the most important of which are historical, descriptive, correlational, causal (post-event), and experimental. In descriptive analysis, central and dispersion statistical indices are used; in inferential analysis, mixed regression is used to test the research hypotheses based on econometric models, and classical regression is used considering all assumptions. To examine the effect of risk on bank efficiency, after collecting the required data from the sampled banks, the researchers employed the data coverage analysis method to calculate the efficiency of each bank. Also, to examine the effect of risk on the efficiency of the banks under study in the years 2013 to 2022, the researchers estimated a model using the panel data regression econometric method for the three risks under study. The results reveal that there is no significant relationship between the risks under assessment and the efficiency of the banks.

The behavioral finance perspective shows that some changes in stock prices are dependent on the emotional reasons of investors. The emotional behavior of investors leads to returns in stocks. The purpose of this research is to investigate the mediating effect of conservatism on the relationship between investor sentiment and excess returns. The statistical population of the research included all the companies admitted to the Tehran Stock Exchange, based on the conditions considered (systematic elimination method), 2767 companies were selected during the period of 2010-2020. The results of the research hypotheses test showed that there is a negative relationship between investor sentiment and conservatism and between investor sentiment and abnormal returns, but the role of conservatism as a mediating variable between investor sentiment and excess returns has not been confirmed. Changes in investors' sentiments lead to the identification of risks and opportunities related to changes in the company's behavioral factors and provide conditions for improving the companies' performance.

Scientometric studies offer insights into the production, dissemination, and application of scientific knowledge, enabling managers at various research and executive levels to make informed decisions in their respective fields. The aim of this research is to analyze and review studies indexed in the Scopus database that pertain to profit management in Asian countries.

This research is an applied study conducted using documentary methods and scientometric techniques. The research community comprises 1,465 scientific publications indexed in the field of profit management. The time frame of the study spans 25 years, from 1998 to 2023, focusing on researchers in this field who are registered in the Scopus database.

The highest growth trend in scientific productions occurred during the period from 2022 to 2023, while the lowest growth trend was observed from 1998 to 2014, during which fewer than 100 degrees were produced. Following that, from 2015 to 2023, scientific production exhibited a positive trend, increasing from 100 to 337 degrees. The majority of scientific publications were released during the years 2022 to 2023. In the following analysis, the growth rate of scientific productions was examined. According to this survey, although scientific productions were limited in the early years, it has experienced significant growth. The highest growth rate was recorded in 2001 at 4.0%, while the lowest growth rate occurred in 2000, with a decline of -1.0% due to a lack of degree production. According to ten highly cited articles on profit management, the total number of citations for scientific productions related to this field in the Scopus database during the review period was 27,002. On average, each article received approximately 11.59 citations.The highest change rate of 471 citations in 2010 is attributed to the study by Jian et al. (2010), which focused on support through related party transactions. The article Private and Public Ownership and Earnings Management: Evidence from Chinese Listed Companies by Ding et al. (2007) has the lowest citation rate, with only 287 citations. Among the total of 6,470 authors in the field of earnings management listed in Scopus from 1998 to 2023, Wang, with 30 documents, Salehi, with 23 documents, and Li, with 22 documents, are the most frequently cited authors. In terms of countries, China leads with 775 documents, followed by Indonesia with 267 documents and Malaysia with 261 documents. The most productive journal in this field is the Asia Pacific Journal of Accounting and Economics, which has published 45 documents. Additionally, the University of Uttara Malaysia isthe most prolific institution, contributing 68 papers. The emerging issues surrounding earnings management research from 2014 to 2016 have been addressed in topics such as income, which has a link strength of 215, shareholders with a link strength of 116, and listed companies with a link strength of 123. Additionally, issues related to profit management, with a link strength of 2,264, corporate governance with a link strength of 706, and real profit management with a link strength of 508 have been examined during the period from 2018 to 2019. Furthermore, topics such as financial reporting quality with a link strength of 82, company performance with a link strength of 80, and audit costs with a link strength of 55 have emerged as significant areas of interest for researchers in recent years. According to high-frequency word analysis, the clustering of research keywords consists of eight interwoven keyword clusters.

In any case, considering the significance of the subject and the limited number of studies conducted in this field, it is essential to focus more attention on this area. This focus aligns with the goals of preventing and uncovering profit management issues. An analysis of journals, universities, and the most cited authors indicates that future collaborative efforts among researchers are necessary in this domain. In order to benefit from the findings of such research, scholars in the field of accounting, like their counterparts in other disciplines, should prioritize scientometric studies. This approach will help establish a coherent framework for understanding the current stateof accounting research and enhance the quality of published accounting articles. Based on this, suggestions have been presented to complete this research and expand scientific studies in the field of accounting knowledge, specifically focusing on the subject area of profit and reviewing related research.

Earnings, as one of the most important outputs of the accounting and financial reporting system, are of great interest to various users for making appropriate decisions in resource allocation. In addition to its quantitative value, if earnings lack appropriate quality, it can lead stakeholders astray and result in misinformed decisions. According to research literature, there are numerous definitions and classifications of earnings quality (EQ) and financial reporting quality (FRQ), along with measurement criteria, but there is no general consensus among them. The criteria examined in this study include a total of eight criteria based on accounting (accruals, persistence, predictability, and smoothness) and market-based (value relevance, timeliness, and conservatism) perspectives. For this purpose, after reviewing the research literature in this field, the advantages and disadvantages of these criteria were explored and critiqued. Research in this area can contribute to the development and determination of an optimal model in the context of the Iranian capital market.

One of the most important decisions of company managers is the one about combining and how to finance companies, that plays an important role in company's operation and efficiency. Decisions of financing can be affected by different factors. One of these factors is manager's personality trait. The main purpose in this article is to study the effects of managers' overconfidence on company's efficiency with considering the relevancy of intermediacy capital structure.

Statistical population in this research is the accepted companies in Tehran's Stock Exchange and its statistical sample includes 229 company data in a period of 6 years (2016-2020). Sampling method is systematic removal and the used method for sample estimation is firm multivariate regression.

The study's results have shown that in relevancy, overconfidence and company's efficiency with mediation of capital structure, there is no strong relation between manager's overconfidence and company's efficiency, and there is a strong reverse relation between capital structure and company's efficiency. This issue represents the complete mediation role of capital structure in this study, in other word all the effects of manager's overconfidence are applied on company's inefficiency by capital structure. 

Overconfidence makes managers not to make logical decisions and interpret their risk level properly. That's why the overconfident managers may use more debts in capital structure, despite the risk of financing from out of company. This issue makes the company's financial costs to grow and company's efficiency to descend

The purpose of this research is to investigate the prediction of corporate social responsibility reporting using financial ratios. To answer the research question, four prediction models of linear regression, K Nearest Neighbor, decision tree, and deep learning were investigated. Also, 61 financial ratios were used according to previous research using data related to listed and non-listed companies of Iran from the years 2012 to 2018. According to the re-sults obtained from the estimation of each of the proposed prediction mod-els, it can be stated that the k-nearest neighbor model has the lowest RMSE value, and in fact, this model predicts the amount of social responsibility with less error than other models. The linear regression model with the high-est RMSE value has a weaker performance than other models. LSTM model and decision tree respectively had the lowest RMSE value after the k-nearest neighbor model. As a result, since the LSTM model requires a large number of test sam-ples for deeper learning, it could not achieve high performance in the evaluated data set.Based on the investigations, it can be stated that the current research does not have a similar example inside or outside of Iran.

This research has been done with the aim of developing Iran's banking scenarios with an emphasis on big data. The current research is practical in terms of orientation and exploratory in terms of the goal. It is also mixed in terms of its philosophical, pragmatic and methodological foundations. To carry out the research in the first stage, 20 key drivers of the research were extracted through literature review and interviews with banking and technology experts. After screening with the fuzzy Delphi method, 8 factors were removed and the rest were evaluated with the Marcus decision making technique. The findings of the research show that the two factors of "technology regulation" and "technology transfer costs" were chosen as key uncertainties for developing research scenarios. Based on these two key uncertainties, four scenarios were developed based on interviews with the focus group with the titles of comprehensive banking, static banking, searching banking, wandering banking. In the comprehensive banking scenario, everything is in its optimal state; Technology transfer costs have decreased and regulators are supportive of the technologies. According to the findings of the research, considering drivers, key uncertainties and alternative scenarios by managers and decision makers can improve the performance and increase the competitive advantage of banks.

Financial innovations has been challenged the banking sector and can improve it. They cover a variety of financial businesses such as online lending, asset management platforms, trading management, mobile payment platforms, etc. All these services generate a large amount of data every day (Hasan et al, 2020: 1). Analyzing this volume of data is difficult, giving rise to the concept of "big data" (Munawar et al, 2020: 2). Big data as one of the important fields of future technology has attracted the attention of various industries (Raguseo & Vitari, 2018: 5206). In general, big data refers to a large volume of structured or unstructured data that is generated and stored at a high speed (Dicuonzo et al, 2019: 41). Big data has found its position in the banking industry; Because of the useful data they have stored in recent years (Rakhman et al, 2019: 1632). Recent applications of big data in banking have been for improving customer relationship management, marketing, optimizing strategic management and human resources (Parmar, 2018: 33; Hassani et al, 2018: 2). Therefore, it can be said that nowadays big data plays a major role in providing financial and banking services, and the realization of its potential benefits in banking is more from technical aspects and affects the organizational structure of banking and mobilizes a large number of different actors (Diniz et al, 2018: 151- 152). With changes in customer expectations and increased competition, the banking industry is no longer able to ignore technological innovations in the banking sector. Due to the numerous applications and benefits of big data in various industries, including the banking industry, and it's becoming more widespread in the future, this technology is becoming a prominent research topic (Phan & Tran, 2022: 6.)

What are the plausible scenarios for banking in Iran with an emphasis on big data?

Many studies conducted in the field of banking and big data deal with the role of big data in improving the performance of the banking industry (for instance: Shakya & Smys, 2021; Gonsalves & Jadhav, 2020; Hung et al, 2020; Parmar, 2018). Also, another part of the studies conducted with a future research approach in the banking sector without focusing on innovative financial technologies and specifically big data (for instance: Baumgartner & Peter, 2022; Eskandari et al,2020). The focus on innovative banking and financial technologies with a Futures Studies approach has been weak (for instance: Maja & Letaba, 2022; Murinde et al, 2022; Hajiheydari et al, 2021; Broby, 2021; Harris & Wonglimpiyarat, 2019). And the role of big data in the Futures Studies of the banking industry has been seen to be very limited due to the relatively large amount of data available in banks and its effect on performance and gaining a competitive advantage (for instance: Valero et al, 2020). Therefore, despite the studies conducted in the field of banking and big data, some of these researches have paid attention to the present time, and the researches conducted in the future of the banking industry have also been without focusing on the role of big data. Now, the most important theoretical gap in research is the lack of studies on the future of banking in Iran with an emphasis on big data.

The current research is pragmatism due to the use of qualitative and quantitative methods from the perspective of philosophical foundations. It is also exploratory in terms of purpose due to the identification of drivers and practical in terms of direction due to the application of the results in the analysis of the future of banking in Iran. In the current research, two methods of literature review and interviews with experts are used to identify drivers, both of which are qualitative methods. According to Popper, the interview tool is based on the expert dimension. The literature review is evidence-based and uses articles and scientific texts to identify factors. Fuzzy Delphi, which is semi-quantitative and evidence-based, is used to screen and determine key drivers that require great accuracy. Then, to determine the key uncertainties, the MARCOS technique is used based on the importance and uncertainty indicators of the Global Business Network (GBN) approach, which is a quantitative and evidence-based technique. Finally, interviews with focus groups are used to write the scenario, which is a qualitative method based on the expert dimension. The theoretical community of the research includes academic experts and managers of the banking sector and are aware of new banking and financial technologies (Fintechs) and specifically big data. The selection of the participants is based on their knowledge and nobility of the research topic and the importance of their presence in the research, and finally 15 people were selected by purposeful sampling using the snowball method. Experts have at least 10 years of relevant work experience and a master's degree.

This research has clarified the situation of this area by identifying the shaping factors and drivers of the future of banking in Iran. Two factors of "technology regulation" and "technology transfer costs" were chosen as key uncertainties for developing research scenarios. Based on these two key uncertainties, four scenarios were developed based on interviews with the focus group with the titles of comprehensive banking, static banking, searching banking, wandering banking. In the comprehensive banking scenario, everything is in its optimal state; Technology transfer costs have decreased and regulators are supportive of the technologies. Considering drivers, key uncertainties and alternative scenarios by managers and decision makers can improve the performance and increase the competitive advantage of banks.

Agency and signaling theories are one of the most important management drivers for risk disclosure. Considering the impact of auditing on reducing the agency conflict, the present study investigates the relationship between risk disclosure with audit report clauses.

The data of 169 firms listed in Tehran Stock Exchange have been used during the years 2013 to 2020. The models were fitted using logistic and poisson regressions. To measure the level of risk disclosure, the number of relevant sentences and relevant paragraph in the board activity reports, management interpretive reports and the notes to the financial statements has been used.

The findings show that with the increase in the level of risk disclosure, the probability of issuing a modified audit report decrease. According to this, there is a negative relationship between the increase in risk disclosure and the number of clauses in the audit report. Examining the level of risk disclosure and the type of clauses in the audit report shows that by increasing the level of disclosure based on the number of paragraphs, the probability of issuing an auditor's report containing the clauses of the lack of sufficient reserve for performance taxes, the lack of sufficient reserve for suspecting claims, audit confirmations, and the clause of lack of documents decreases.

Contribution:

 The effect of risk disclosure on audit report has not been investigated. Findings, while emphasizing the importance of risk disclosure reporting, clarify the necessity of approving risk disclosure reporting framework to improve users' decision making.

In this research, the manipulation of operational cash flows in companies listed on the Tehran Stock Exchange companies and its motivational aspects in the form of capital market incentives (beat benchmarks (achieving predetermined goals) and financial distress) were investigated during the period of 2011-2020 in the form of seasonal and annual data. In order to do this research, 150 listed companies were investigated during the mentioned period. In order to test the hypotheses, Pooled/Panel regression analysis in the form of seasonal data, and the chi-square test in the form of annual data were used in EViews 9 software. In this research, the independent variables are the dummy variables that determine the fourth quarter and the first quarter of the financial year in the form of quarterly data to test the first hypothesis, and the variables of the levels and changes of operational cash flows and financial distress in the form of annual data to test the second hypothesis. In the current research, dependent variables include changes in working capital based on changes in the cash collection cycle and changes in net non-cash working capital as criteria for measuring the manipulation and management of operational cash flows in the form of quarterly data to test the first hypothesis. Also, in this research, the control variables of sales changes, net earnings and its changes were used in the form of seasonal data to test the first hypothesis. The findings of the research indicate the confirmation of the manipulation of operational cash flows by the investigated companies. Other results indicate that beat benchmarks (the achievement of predetermined goals) and financial distress have been effective in manipulating operational cash flows.

The current research seeks to identify and analyze key drivers affecting the future of auditing in Iran, focusing on blockchain technology. The current research has an applied nature in terms of orientation and its methodology is multi-method. The theoretical population of the research were the senior experts of the auditing organization, the Supreme Audit Court and the country's capital market in the field of auditing and digital financial technologies. The sampling method was done in a judgmental way according to the expertise of experts. The sample size in this study was equal to 15 people. The drivers of the research were obtained through literature review and interviews with experts. Then, for screening and prioritizing the research drivers, two questionnaires of expertise and prioritization were used, respectively. Among the drivers, 11 drivers had a defuzzy number higher than 0.6 and were selected for the final ranking. The degree of priority of the remaining drivers was evaluated by the Cocoso method. According to the indicators of Cocoso technique, the drivers of blockchain development in industries and other fields and the level of blockchain adoption by organization and audit institutions had the highest degree of priority. Teaching financial digital technologies such as blockchain to managers, auditors and financial users, integrating the policies of regulator bodies in the field of supervision and standardization and aligning global standards with blockchain technology requirements were among the most important practical proposals of the research.

Delay is an event that increases the completion time of a part of the project, which is one of the main problems in the executive projects of the country and causes an increase in project costs as well as damages. Project delays pose significant risks to the project that are dangerous for the project to continue. These risks are of particular importance in oil and gas projects. The purpose of this study is to identify and rank the risks related with delays in oil and gas projects. The present study is applied in terms of orientation and quantitative in terms of methodology. The statistical population of the study is managers and experts of risk and delay in oil and gas projects in the country. Among these people,15 people were selected as the sample by judgmental sampling method. For data collection, two questionnaires of expertise and prioritization were used, both of which had validity and reliability. In the first step, the risks associated with project delays were extracted by reviewing project risk and delay articles. In the next step, these risks were screened using the Binominal test.11 risks had a significance coefficient higher than 5% and were excluded from the calculations. The remaining 8 risks were prioritized using the Codas distance technique. According to the data of the relative evaluation matrix and the scores of each risk, the risks of sanctions, inflation, and lawsuits and complaints had the highest priority, respectively. Finally, research proposals were developed based on significant risks.

The purpose of this study was to investigate the effect of peripheral uncertainty on the relationship between the herd behavior of investors and the price of negative stock shock. Because the capital market is one of the main pillars of the country's economic growth and development, the incidence of any disturbance and deviation in prices causes problems with allocating and equipping funds. Reducing stock prices is an example of the disorders created in the capital market. On the other hand, one of the factors that lead to the fluctuations of return and instability of financial markets is the herd behavior of investors. If investors lack sufficient information about environmental factors, environmental uncertainty occurs. Such a situation also affects the organization. Environmental uncertainty overshadowed the use of financial statements. Changes in stock prices and corporate status are cases that can not speak in full confidence in their occurrence. As a result, environmental uncertainty is part of the economic environment. To test the research hypotheses, 156 companies from the Tehran Stock Exchange during the years 2010 to 2020 were selected by screening method. To test the hypotheses, multivariate regression models were performed using STATA14 software. The results indicate that herd behavior has a significant effect on price shock and environmental uncertainty has no effect on the relationship between the two.

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.

Due to the expansion of societies and economic and commercial issues, the demand for financial and economic information has increased and the financial statements of companies that contain this information are of great importance to users. This information is effective on people's decisions when it is reported at the right time, otherwise some of them lose their economic value. The timely submission of financial statements depends on the completion period of the audit process, as financial statements cannot be released before the audit is completed. The duration of annual audit is stated as one of the most important factors determining the timeliness of financial reporting of companies. The delay in submitting the audit report reduces the credibility and quality of financial reporting. According to previous researches, the timely presentation of financial reports enriches the content of information, which affects the value of the company. Considering that the reporting of financial information is delayed until it is not audited by independent auditors, the auditing of financial statements by external independent auditors is very important, because audited financial statements are considered to be the most important reliable sources for investors and their users. It can be said that financial statements audited by external independent auditors are the only and best source for external users. The issue of delay in providing the audit report is significant and vital for external users because faster access to information is effective in their optimal decision-making and also increases the possibility of earning profit. The purpose of this study is to apply a meta-analysis approach to combine and analyze contradictory findings from various studies to investigate the effect of characteristics and accounting complexity criteria of the company on the audit report lag. 

The current research is applied research in terms of classification based on the goal, and descriptive-meta-analytical in terms of nature and method. Meta-analysis is a research approach that helps the researcher to achieve a suitable combination of the quantitative results of contradictory and non-contradictory studies in the past, to explain the contradictions and to identify the structural variables that moderate the results of the past studies. Meta-analysis basically follows the steps of a scientific research and has almost the same steps as quantitative research, with the difference that here the sampling unit is a study instead of a subject, and because it is actually a combination of data from several studies to produce a single estimate, therefore, from a statistical point of view, It is considered a type of multi-factorial method. The community under investigation of this research is internationally published authoritative articles (A*, A, B, C) in the list of 2019 (ABDC) and domestic authoritative articles approved by the Ministry of Science, Research and Technology. In order to achieve the purpose of the research using Lipsey & Wilson (2001) method and also Cochran’s Q test and Egger test, a total of 77 studies including 20 internal studies during 2010 Up to 2019 and 57 foreign studies during the years 1989 to 2019 were reviewed. In these researches audit report lag was considered as a dependent variable and the characteristics of the Organizational complexity as an independent variable. CMA2 software is used in meta-analysis in order to perform the necessary calculations, including calculating the effect size, after collecting the desired variables from the relevant research. 

The results of this study shows that there is a negative relationship between the firm size and the audit report lag, while other characteristics, extraordinary items, subsidiary, merger or acquisition, foreign sales, restatement of financial statements and Business segments of the company have a positive relationship with the audit report lag. 

Considering the expansion of accounting and auditing knowledge and the importance of providing timely information for financial and economic decisions, the factors that cause delays in the submission of audit reports and ultimately audited financial statements are very important. The delay in submitting the audit report causes an imbalance in the level of access of users to financial information, which can ultimately affect the capital market transactions and the value of the company. The conclusion of the present meta-analysis showed that one of the complexity criteria used in most studies is the size of the company. Larger companies, with more resources and facilities, seek for faster auditing and reduce delays in the audit report. It can also be stated that the existence of other criteria related to the complexity of the company will lead to the expansion of the audit process and increase the delay in submitting the audit report.

According to capital market research, the negative stock price shock in any market is a function of environmental factors and specific characteristics of the company, and any insight on how to describe and predict the shock can affect the decisions of investors and activists in the stock market. In this study, based on data related to 140 companies listed on the Tehran Stock Exchange.we have attempted to predict stock price shocks with emphasis on financial ratios. In order to select the optimal variables from the set of 96 variables, two evolutionary algorithms of particle swarm optimization and genetic algorithm have been used. After applying the mentioned algorithms, finally, 8 variables affecting permanent and temporary shocks were extracted, which in the regression model mentioned in the research, their effect on the predictor of shock was investigated. the results of RSME model are the permanent shock (genetic algorithm), permanent shock (particle swarm optimization), temporary shock (genetic algorithm) and temporary shock (particle swarm optimization (particle swarm optimization), 5.8433 , 5.6284 , 7.537 and 7.295 . as we observe , RSME in permanent shock based on genetic algorithm is more than RSME permanent shock model based on the evolutionary algorithm of particle swarm optimization. also in the transient shock model based on the genetic algorithm , the model is more than RSME of the temporary shock model based on the evolutionary algorithm of particle swarm optimization . It can therefore be stated that the estimated regression is based on the selected variables from the evolutionary algorithm of the particle swarm optimization, and has better predictive power than the selected variables of the genetic algorithm.

The present research aimed to identify and analyze effective drivers in the future of crypto fintechs using a fuzzy approach. The research was applied in terms of purpose and had a quantitative methodology. The theoretical population consisted of financial experts in fintechs and blockchain technology. Judgmental sampling was performed based on the expert's expertise. A sample size of 10 was studied. Two quantitative techniques, fuzzy Delphi and DEMATEL, were utilized in the study, and the expert and effect assessment questionnaires, which had desired validity and reliability, were used to collect data. The research had three stages; first, 22 drivers were obtained using the literature review and interviews with experts. Second, the drivers were screened by distributing expert assessment questionnaires and the fuzzy Delphi method. Third, 10 drivers were selected for the final analysis due to the suitable deffuzification number. Such drivers were evaluated by distributing effect assessment questionnaires and the fuzzy DEMATEL technique. The drivers of national regulatory policies, the development of national RegTechs, and the development of smart contracts in the financial industry respectively had the highest net effects. Practical suggestions were extracted based on prior drivers and interviews with focus groups.

Any illegal attempt to not pay taxes, such as not giving the necessary information about taxable income and benefits to the authorities, is called tax evasion, and determining the level of tax evasion and having a better understanding of its reasons will help in the process of finding mechanisms to prevent it or reduce its intensity. the purpose of this study is to prioritize effective factors on tax evasion. For this purpose, by collecting the necessary information through library method and then distribution of questionnaire in two stages, the collected data were analyzed using Fuzzy Analytic Hierarchy Process method. the statistical population studied in this study is senior managers, staff managers and active experts who have a working relationship in different units and departments of the tax administration, among which 20 were selected as samples. To determine the importance and ranking of the main factors of the main factors and effective sub - factors on tax evasion, Group Fuzzy Analytic Hierarchy Process (GFAHP) has been used. The results of the prioritization of research variables have shown that the the most important main factor for tax evasion is the determined executive factor and the main economic - environmental and legal factors in the next priorities.

The purpose of this research is to investigate the simultaneous effect of social responsibility on the company and the emotions of investors. Today, moral issues and actions for economic development are focused on improving the quality of human resources on a wider scale, the community. As a result, firms plan their strategy purposes that have positive effects on the community. Some companies are losing responsibility activities and maintaining a good impression and attracting market trust.

The period of research during 2014 and 2020 and the research sample consisted of 130 Tehran Stock Exchange. To test the research hypotheses, simultaneous equations and econometric software have been used.

The results indicate a positive, meaningful, and simultaneous effect on investor sentences.

As a result, managers recognize the expectations of investors and respond to them. This leads to a positive reaction of the capital market than the stock value of such companies. Whenever the company realizes that investors deal with more feelings, they move more toward social responsibility. Enhancing knowledge: Studies on the emotions of investors and responsibilities show that the present study does not have a similar sample in Iran in terms of the subject, goal, and method of measuring the emotions of investors.

The purpose of this study is to use a meta-analysis approach to analyze and combine inconsistent results of past researches on the impact of the auditor and audit-related features on the audit report lag. These variables include the auditing firm size, auditor industry specialization, auditor change, the audit tenure, type of auditor report and auditor busy season

in order to achieve the purpose of the research using Lipsey & Wilson (2000) method  and also Cochran’s Q test and Egger test, a total of 75 studies including 20 internal studies during 2010 Up to 2019 and 55 foreign studies during the years 1989 to 2019 were reviewed. In these researches audit report lag was considered as a dependent variable and the characteristics of the audit firm as an independent variable.

The results show that there is no significant relation between audit firm size and the auditor tenure with audit report lag. While auditor change, type of auditor report, and auditor busy season have a positive relation with audit report lag. Finally, there is a negative relation between auditor industry specialization and audit report lag.

The specialized auditors in companies can speed up the audit process and make financial reports available to users with less audit lag. also to reduce audit report lag, firms should reduce the auditor change and first-year audit and Provide the necessary infrastructure to reduce material misstatements in corporate financial statements and the publishing of qualified report by auditors, which leads to Timely publish of financial statements that enriches the content of information.

Every investor pays special attention to the main factor in their decisions: a return. What is essential for users of financial information is not the procedures and principles used in accounting, but the exit from the financial system, because it helps them achieve their goals. Many capital market concerns focus on accounting and auditing operations. Therefore, the auditor's independence is the basis of public trust in the audit process and the assurance of auditors` reports. For this purpose, this study investigates the effect of auditor switching on abnormal returns. Therefore, three hypotheses have been formulated, and a sample consisting of 365 companies listed on the Tehran Stock Exchange during the years 2010 to 2020 has been selected. The results indicate that auditor switching has no significant effect on abnormal returns. Also, between the CU switch and CD switch, the CD switch has a negative and significant effect on abnormal returns.

According to capital market research, the negative shock of stock price in any market is a function of environmental factors and specific characteristics of the company and any insight into how to describe and predict the shock can influence the decisions of investors and stakeholders. In this study, based on the data related to 96 financial ratios of 140 companies listed on the Tehran Stock Exchange during a period of 9 years between 2010 and 3012, we have predicted a negative shock of stock price based on the meta-heuristic approach. In this research, in order to extract the optimal financial ratios, genetic algorithms and particle swarm optimization have been used. The proposed model is then tested using these extracted features by a support vector machine with a radial core and an artificial neural network. The results showed that the variables extracted from the particle swarm optimization algorithm, together with the support vector machine learning algorithm, create better results for predicting shocks (temporary and permanent) and their number.

In a volatile economic environment, financial decision making is always associated with risk. Bankruptcy, as one of the most important risks, has a significant impact on the interests of the firm's stakeholders, so presenting appropriate bankruptcy forecasting patterns is of the utmost importance. In this study, after reviewing the theoretical literature and selecting the financial ratios used in previous bankruptcy prediction models as the variable input of the initial model, the financial ratios were adjusted based on the price index and then, using the LARS algorithm, the ratios that have the highest ability to differentiate between bankrupt and non-bankrupt firms were identified, and finally, using the SVM and Naive Bayesian algorithms, the final bankruptcy prediction model was developed. For this purpose, the data of 50 companies listed in Tehran Stock Exchange who had experienced bankruptcy for at least one year from 2008 to 2018 under Article 141 of the Commercial Code were used. The results show that the adjusted financial ratios based on the price index in the model presented by SVM algorithm can be a very good predictor for bankruptcy of companies with an accuracy of 99.4%.