فهرست مطالب ali asghar anvaryrostamy

Due to the high costs of financial distress and bankruptcy and the importance of timely prediction of financial distress to take corrective actions, this study examines the research related to the prediction models of financial distress and bankruptcy in Iran and other countries. This study is a review of research that examines 102 research in Iran and 298 research in other countries during the years 1930-2023, describing theories and analyzing the nature, trend, composition, and periods of using models and presenting practical and research recommendations. The results indicate an upward trend of research in Iran, while it is declining in other countries. The noteworthy point is the use of various combinations of models and techniques in other countries is more diverse than in Iran, and the prominence of intelligent models that are based on evolutionary calculations. The research concludes by providing executive and research recommendations. The recommendations include diversifying predicting techniques, emphasizing models based on evolutionary computing algorithms, paying attention to the predictions after the crisis, developing special models for small and medium-sized companies, separating the criteria for recognizing financial distress and bankruptcy in the Tehran Stock Exchange, making dynamic predictions, and developing more focused and specialized models to increase accuracy. This study addresses the shortcomings of past research, provides more up-to-date information and insights on the dynamic predictions, compares these studies in Iran to other countries, and provides some executive recommendations and research topics based on the results.

Financial distress indicates that a company is approaching bankruptcy and the vast and profound negative effects of bankruptcy on society, necessitate reviewing bankruptcy prediction models. Therefore, predicting financial distress in its early stages can inform the stakeholders of companies about their future possible losses (Zhou et al, 2023). Global crises like the 2008 financial crisis and the COVID-19 pandemic have forced even very strong international companies to continuously monitor their financial situation (Woodlock & Dangol, 2014; Hassan, 2022; Papik & Papikova, 2023). These environmental dynamics require reforms and more accurate predicting methods for the financial health of enterprises, (Brygala, 2022) especially for small and medium-sized companies, which have weaker financial resources (Ciampi et al. 2021; Mirza et al., 2023). While existing literature reviews are often outdated or limited in scope, this study addresses these gaps by comprehensively analyzing the models and techniques used for predicting financial distress and bankruptcy in Iran and other countries from 1930 to 2023. Furthermore, the study compares the nature, trends, composition, and periods of use of predicting models in Iran and other countries and offers insights for future research.

Systematic review refers to examining, criticizing, and evaluating a specific research topic, extracting and interpreting data from published articles, and analyzing, and describing results based on clear evidence (Lasserson et al., 2020). It generates highly credible, low-bias, and quality scientific research documents (Yetley et al., 2016). This meta-analysis research systematically evaluates financial distress and bankruptcy prediction models by analyzing 102 studies from Iran and 298 from other countries from 1932 to 2023. Data were sourced from reputable Iranian and international databases and journals, including Emerald, Science Direct, and ProQuest. The study aims to rectify previous shortcomings, update information, and offer new research avenues based on the findings of this review.

In Iran, financial distress prediction studies that started in 2001 shows a rising trend, encompassing 13 studies conducted during 1968-2007 and 89 studies during 2007-2023. Financial distress prediction research in other countries started in 1932 and has shown a declining trend in recent years including 10 studies conducted during 1932-1968, 217 studies in 1968-2007, and 71 studies during 2007-2023. The 102 predicting techniques in Iran during 2001-2023 consisted of 43 statistical models, 40 intelligent models, 17 combined models, and 2 other models such as Data Envelopment Analysis and judgmental, etc. However, 298 studies in other countries include 117 statistical models, 125 intelligent models, 25 combined models, 21 theoretical models, and 10 other models such as Data Envelopment Analysis and judgmental, etc. Among the 102 papers from Iran, 61.76% introduced modified or new models, while 87.5% of global research presented innovative models. Techniques used globally from 1930 to 2023 include statistical, intelligent, hybrid, judgmental, and other models. Statistical models dominated earlier, however, intelligent models, particularly those inspired by animal collective intelligence, gained prominence and attention for their high accuracy.

Iranian research on predicting financial distress and bankruptcy exhibits an upward trend, while global research has been declining recently. The results verify that intelligent models like neural networks and genetic algorithms, notably those inspired by animal collective intelligence such as the ant algorithm, and the firefly algorithm, the bird algorithm, demonstrated higher prediction accuracy than statistical models such as multiple discriminant analysis, logistic regression, probit, and theoretical models. Successful intelligent models, which widely used recently and exhibited a higher accuracy, include gradient boosting models (Jones, 2017), machine learning methods (Chen et al., 2023), and models based on collective intelligence such as the firefly algorithm (Bayat et al., 1997). Among the statistical techniques, the nonlinear logistic regression techniques demonstrated a high level of accuracy (Lohmann et al., 2022; Lohmann & Mollenhoff, 2023). In addition, 61.76% of 102 models from Iran and 87.5% of 298 articles abroad were innovative models. Recommendations include diversifying predicting techniques, emphasizing models that are based on evolutionary computing algorithms, attention to the necessity of post-crisis predictions, developing tailoring models for SMEs, separating criteria for financial distress and bankruptcy recognition in the Tehran Stock Exchange, the necessity of adopting dynamic predictions and (Lohmann & Molenoff, 2023) developing focused and industry-focused models for enhanced accuracy (Nazmi Ardakani et al., 2017).

Cash management policies have always been one of the most important financial policies in the process of managing companies. Considering the importance of cash management in companies and accounting standards, the presentation of the cash flow statement is considered one of the basic financial statements. Cash flows from operating activities indicate that the main and continuous operations and activities that generate the operating income of the commercial unit have led to sufficient cash flows to repay loans, maintain the operating capacity of the commercial unit, pay dividends, and make new investments. Various research has been conducted regarding the relationship between the sensitivity of cash holdings to cash flows. The results of some studies are also focused on the asymmetry of cash flows. However, investigating the effects of corporate governance mechanisms on the sensitivity of cash holdings under the condition of cash flow asymmetry is an issue that has not been investigated. Therefore, the purpose of this research is to investigate the sensitivity of cash holdings to asymmetric operational cash flows, considering the financial constraints and external supervision of institutional owners in companies listed on the Tehran Stock Exchange.

The statistical population consists of all the companies listed on the Tehran Stock Exchange during the Iranian calendar years 1393 to 1400 (2014 to 2022). The sample consists of 104 companies. To check the stationarity of the research variables, Levin, Lin, and Chu's unit root test was used, and to test the research hypotheses, the combined data regression method was utilized. Moreover, for the evaluation of the goodness of the proposed regression models, some necessary pretests were implemented.

The findings show that companies with higher operating cash flows hold more cash. Financial constraints and institutional ownership do not have a significant effect on the sensitivity of cash holdings to asymmetric operating cash flows. Results from control variables also indicate that the Tobin ratio and the size of the company have a positive and significant effect on cash holdings. However, no evidence was found of a positive influence of the debt ratio on the amount of cash holdings.

Findings indicate that the sensitivity of cash holdings to asymmetric operating cash flows is higher in companies with greater operating cash flows. This result may be due to the pursuit of future profitable opportunities or to reduce liquidity risk in the inflationary, unstable, and dynamic business environment of Iran. Other results show that financial restrictions and institutional ownership, contrary to expectations and existing literature, do not have a significant effect on the sensitivity of cash holdings to asymmetric operating cash flows. This lack of effect may be attributed to the presence of state ownership. The results underscore the need for further research in the future.

In economies where banks play a key role in aggregating savings and allocating credit to various sectors, it is crucial to evaluate the performance of the banking system using appropriate methods. This research paper presents a model for evaluating the efficiency of commercial banks listed in the Tehran Stock Exchange during the period from 2015 to 2020, with a focus on the impact of credit risk. The study employs a two-step descriptive-correlation retrospective method to rank the banks and explain the role of credit risk in their efficiency. Specifically, the efficiency of the banks is determined using inputs and outputs based on DEA (Data Envelopment Analysis) models. The calculation of efficiency using ideal SBM (Slacks-Based Measure) and DEA methods reveals that Mellat, Saderat, and Tejaret banks were the most efficient during the study period. Furthermore, Tobit and logistic regression models are used to investigate the relationship between the main determinants of credit risk and the efficiency of commercial banks. The findings indicate a statistically significant relationship between the two factors. Overall, this paper highlights the importance of evaluating the efficiency of the banking system in bank-oriented economies and provides a useful model for doing so. The research paper highlights the significant impact of credit risk on bank efficiency, emphasizing its role in shaping effective risk management strategies within the banking sector. It suggests that banks should prioritize these factors to enhance their operational efficiency.

This study investigates the impacts of corporate governance on the interaction between the agency costs and information efficiency of stock prices. In this research, the agency issues measured by the interaction between corporate growth opportunities and its free cash flows. Four corporate governance mechanisms examined in this research are financial expertise and independence of the board of directors, internal audit and institutional shareholders. The sample consists of 130 companies from 1394 to 1400. The results show that agency cost has a significantly negative effect on the information efficiency of stock prices. Moreover, the findings confirm that among the four corporate governance mechanisms, only institutional shareholders significantly moderates the negative effects of agency cost on the information efficiency of stock prices. However, we could not find any evidence on the moderating role of internal auditor, board financial expertise and independence. The results of this research showed that the growth opportunities and free cash flows of companies are among the most important variables affecting the information efficiency of stock prices in the Iranian Stock Exchange.

Due to the importance that investors place on the return of their shares, the phenomenon of falling share prices leads to a decrease in returns. Many researchers believe that the changes in the company's stock price originate from its internal information management. Since the stock price changes not only through financial information but also through social, governance, economic and environmental information, therefore publishing a special report such as the governance dimension of sustainable performance reporting can affect the liquidity of stocks and the crash risk. Using the structural equation modeling approach, the present research seeks to predict the crash risk of stock price and its liquidity through governance sustainablity reporting and the forecasting accuracy and earnings comparability. Using the systematic elimination method, 147 companies listed on the Tehran Stock Exchange for the period of 2011 to 2014 selected as a sample. PLS software used to analyze the data. The results showed that the governance component of sustainablity performance reporting leads to an increase in the comparability of earnings, an increase in the liquidity of stocks and a reduction in the crash risk of stock prices. Other findings showed that the comparability of earnings has a mediating role on the relationship between the reporting of sustainable governance performance and the crash risk of stock and liquidity. Meanwhile, the accuracy of the earnings forecast does not have a significant effect on the crash risk of the stock price and the liquidity.

One of the most critical responsibilities of bank managers is liquidity management. The purpose of this study is to design and implement a combined mathematical model of the Analytical Hierarchy Process (AHP) and Goal Programming (GP) for bank liquidity management. Initially, a conceptual model was drawn up, followed by the definition of model variables based on the bank's financial statements. Due to the presence of multiple, conflicting goals and the differing importance of each goal, the GP technique was utilized. The weighted nature of the GP model allowed for the calculation of the importance of ratios affecting the bank's liquidity within the objective function through the Analytical Hierarchy Process. The model was solved, and the analytical results were presented to bank officials. Finally, the effectiveness of the proposed model was validated through a survey of seven main questions related to seven evaluation dimensions from 30 banking experts. Given the similarities in banking systems in the country, with minor modifications, the proposed model and pattern of this research could provide the necessary foundations for efficient liquidity management in other banks as well as help other Iranian banks to enhance their liquidity management efficiency with some adjustments.

Banks, as key participants in the financial market, play a crucial role in expediting production processes and driving economic growth. They achieve this by gathering savings and idle liquidity, and channeling these resources into vital economic sectors. Enhancing efficiency and performance is a primary management objective for these institutions, as it leads to improved productivity. However, given the multitude of indicators and criteria used to assess efficiency, it is essential to identify and employ appropriate indicators based on specific guidelines. This study adopts an analytical-descriptive approach, collecting data from 2014 to 2019 through documentary research and surveys. The goal is to identify the influential indicators for evaluating the efficiency of commercial banks in Iran, as determined by a panel of experts. These indicators are then prioritized using factor analysis and the Fuzzy Delphi method. The findings reveal the existence of 19 key factors that determine bank efficiency, categorized into 7 main groups. These factors encompass financial, human, environmental and geographical, structural and organizational, physical, technical, and financial ratios. Together, they account for 62.45% of the total variance and serve as the most significant inputs and outputs that explain and determine the efficiency of commercial banks in Iran.

Rating of companies in the business environment is one of the most important tools for identifying internal strengths and weaknesses and external opportunities and threats of companies. The increasing expansion of new approaches for evaluating the performance and reporting of companies in developing countries, on the one hand, and the lack of a comprehensive model for rating companies admitted to the Tehran Stock Exchange, on the other hand, prompted researchers to create a practical model based on the integrated reporting approach for comprehensive evaluation and rating the listed companies on TSE. The model was designed using fuzzy Delphi technique, fuzzy Dimtel and improved fuzzy AHP technique consist of 5 criteria, 19 sub-criteria and 63 indicators. Results show, the financial performance is the most important and the environment is the least important criteria for determining the rating of companies, while the environment has the most and financial performance has the least influence on other criteria. Also, the top 10 indicators among 63 indicators make up a total of 49% of the weight of the model, and it is necessary for company managers to pay special attention to them in order to improve their ranking.

In today's interconnected global economy, changes in one market can have ripple effects across related markets, making it essential for economic and financial policymakers and experts to accurately predict these mutual impacts. Various methods have been developed to forecast the impact and mutual impressions of financial markets. In this study, a generic framework is proposed for forecasting economic and financial market indicators using the interrelationship of shocks among global financial markets and a system dynamic approach.

To demonstrate the stages of the proposed generic framework and system dynamics modeling, as an example, the study forecasts the Iranian economic and the Tehran Stock Exchange indicators using their interactions with eleven major global financial markets, including London, Tokyo, Shanghai, Frankfurt, Paris, Milan, SIX Swiss, Istanbul, Korea, Bombay Stock Exchanges, and Dubai Financial Market. The New York Stock Exchange index return is used as a stimulant or driver for the other stock exchanges in the model.

The results indicate that the proposed forecasting model successfully predicted the Iranian economic and the Tehran Stock Exchange indicators. Furthermore, the study finds that while Iranian exports are sensitive to global financial markets, the sensitivity of imports and production returns to global financial markets is low.

The proposed generic framework and system dynamics modeling can provide valuable insights for predicting different economies using their interactions with the global economy and finances.

The purpose of this study is to design a model to investigate the impact of variables of intellectual capital, profit quality, nature of audit report and capital structure on the risk of bankruptcy of Tehran Stock Exchange companies. The present study is an applied, analytical-mathematical research. Its spatial territory is Tehran Stock Exchange and its temporal territory is from 1391 to 1397. In this study, regression test method has been used to predict bankruptcy. Findings show that intellectual capital of 0.212 of changes in bankruptcy risk forecasting, profit quality of about 0.207 of changes of bankruptcy risk forecasting, nature of audit report 0.194 of changes of bankruptcy risk forecasting, and structure Capital 0.362 predicts changes in corporate bankruptcy risk.

The probability of stock prices crash has great importance in portfolio analysis and pricing of capital assets. Therefore, one of the major issues that investors face in the capital markets is predicting the fall of stocks. Given this necessity, the purpose of this study is to provide an approach to estimate the risk of stock price crashes. Recently, methods called "artificial neural networks" have been used to predict monetary and financial variables in parallel with structural models and time series. These models, which are actually derived from the brain learning process, use computer computational speed to learn complex relationships between variables and use them to predict the future. Using the data of 20 companies listed in the Tehran Stock Exchange, the present study presents models to estimate the probability of stock prices crash in the Iranian stock market using artificial neural networks. The results indicate that artificial neural networks have good performance in estimating the probability of stock prices crash in the Iranian stock market.

The purpose of this research is to examine the effect of ownership structure and audit features on the financial distress likelihood by considering the moderating effect of managerial ability. This study utilized partial least squares structural equations modeling (PLS-SEM) analysis and data from 107 firms listed in the Tehran Stock Exchange. Audit features measured by auditor size and audit opinion and ownership structure measured by the block-holder ownership and institutional ownership. Backward logit analysis was used to calculate the financial distress likelihood. DEA technique and Tobit regression were used to measure the managerial ability. The results of the study show that audit features have a positive effect on the likelihood of financial distress. Moreover, the effect of ownership structure on the financial distress likelihood and the moderating effect of manage-rial ability were not confirmed. This paper offers evidence on the extent to which distress is associated with corporate governance and managerial ability from a developing country. The paper should be of interest to the regulatory bodies and practitioners because in many developing countries the implementation of corporate governance mechanisms is voluntary and is not yet required.

 Today, management accounting techniques provide the valuable information needed by managers of organizations and are expected to be effective on management control systems. The main purpose of this study is to provide a model that can explain the impact of different management accounting techniques on the performance of management control systems of companies.

The statistical population of this study is all companies active in the Tehran Stock Exchange whose major shareholders are the government or are affiliated with the government in 2019 and the statistical sample is 140 companies. The research measurement tool was a questionnaire. Correlation analysis and causality analysis (path analysis) have been used to test the research hypotheses.

Findings showed that the use of management accounting techniques by companies has significant differences and that different management accounting techniques do not have the same effect on the performance of management control systems.

According to this research, with the improvement of management accounting techniques, the efficiency of management control systems will also be improved. Also, the techniques available in the resource reduction category have the most direct impact on the efficiency of the management control system, and the other techniques are sources of      information, planning, management control, and value creation, respectively.

This research investigates the effect of social performance indicators on credit risk in the banking industry. To this end, a 6 years period was considered during 2012- 2017. Finally, 20 banks selected as the study population. The independent variable of this research is the social performance index of banks. In order to elaborate on the social function index checklist, the method of meta-‌synthesis was used. The content analysis method was then used to measure the rank of social responsibility indicators (number of disclosed items on the total number of items disclosed). In addition, the dependent variable is credit risk. The statistical method used to test the hypothesis proposed in the panel data research. The results indicate a positive and significant relationship between social performance indicators and associated risk factors. That is, the greater the risk of social performance indicators in banks and their operating cost, the higher the risk of credit. On the other hand, the coefficient of estimation of 0.44 for social performance indicators shows that 0.44 changes in banks' credit risk are due to changes in social performance. Also, bank managers should be more cautious in applying social responsibility mechanisms and not assume that this effect should logically be negative is always true.

Iran's tax system has not been developed for various reasons, including the abundance of oil revenues at various times, in proportion to the country's economic capacity, and despite repeated emphasis in upstream documents on the need to cut government funding for oil revenues by increasing non-revenue share Oil, especially tax revenues, have not materialized so far. Therefore, the transformation of the tax system is essential in order to improve its situation in terms of its functions and in order to achieve the goals set out in the law of the Sixth Plan, especially its quantitative goals. The purpose of this study is to investigate the relationship between cash profit and the independence of the board of directors on tax avoidance of the company within the framework of the country's tax system. The test method of the hypotheses of this research has been done by the econometric method of data panel and using the Ivyuz software. Research findings show that; Cash payments have a significant effect on tax avoidance. Also, the percentage of non-executive managers has a significant effect on tax avoidance. And the percentage of non-executive managers does not have a significant effect on the relationship between cash dividends and tax avoidance. The results of this study can be useful for the decisions of managers, investors, shareholders, creditors, tax authorities, tax law makers, auditing and other auditors, stock exchange and securities organizations, analysts and researchers.

This study investigates the effects of external financing requirement on corporate governance index and the value of companies listed on the Tehran Stock Exchange. This research in nature is a descriptive - correlation research. The sample consistes of 180 companies during 1392 to 1396. The hypotheses tested by multivariable regression panel data. The results show that the corporate governance have an insignificant effect on the companys’ value. However, the need for external financing has significant effects on both the value of the companies and corporate governance index. Based on the results, it is recommended that business managers seriously seek to enhance the quality of corporate governance in controlled entities if they seek to influence the factors that enhance capital structure. Capital structure selection is optimized. Based on the results, it is recommended that business managers seriously seek to enhance the quality of corporate governance in controlled entities if they seek to influence the factors that enhance capital structure. Capital structure selection is optimized.

This paper is to develop a pattern to forecast adjusted returns according to the downside risk for the Tehran Stock Exchange. For this pursues, first, it seeks to find the factors affecting the adjusted return of companies listed on the Tehran Stock Exchange, taking into account the role of downside risk in the context of information asymmetry. In the second stage, it provides a pattern of forecasting adjusted returns based on downside risk. In fact, the main problem is that several internal and external effective variables affect the adjusted return according to the downside risk, and each of these variables may have positive or negative (ambiguous) effects on the adjusted return according to the downside risk. This study is to investigate the ambiguity about the role of variables affecting adjusted returns. The results of the regression model test show that the variables of capital structure, net profit, return on assets, board of directors, and duality of duties of managing director and chairman of the board at 99% confidence level and variables of dividend ratio and operating cash flow at 95% confidence level have significant effects on the adjusted return.

Financial markets especially the capital market are among the most important means of equipping and allocating financial sources. The stock exchange market is one of the elements of this market. Considering the financial and economic strategic importance of this market, in case there is extensive disruption and deviation in it, it will be seriously problematic to equip and allocate the financial sources of the country. One of the factors causing these issues is the price bubble. Considering the importance of price bubble and following the financial crises caused by the formation of a bubble, the main aim of this study was to investigate the factors affecting the price bubble in the Tehran stock exchange (TSE) market. The statistical sample of this study consists of 166 companies listed in the Tehran stock exchange (TSE) market from 2007 to 2017. In this study, the effective factors and fitting of the main model have been studied using spss software and EViews. The obtained results indicate that based on the fitting of the model, except for variables of size and transparency of information which have not shown a significant effect on the price bubble, other variables including stockholder composition, P/E ratio and liquidity speed have a significant effect on the price bubble.

This main purpose of this study is to provide a generic process pattern for optimizing the investment management process in Iranian banks. To this end, the literature and related processes were examined, three types of legal, systemic, and goal constraints on investment were mentioned, and investment management constraints on banks, including bank liquidity management constraints, were emphasized. Then, the special distinction of banks in terms of criteria and indicators and two levels of investment performance evaluations were explained. Finally, the components and, accordingly, the generic process pattern for bank investment management were presented. In this pattern, the necessity of using a systemic approach and the necessity of coordinating its banking and non-banking activities in the form of a large set in modeling and optimization were emphasized. The proposed pattern through improving the coordination and a more logical sequence of the investment management process in banks will provide the basis for improving the efficiency of Iranian banks.

The present study investigates the effects of managerschr('39') overconfidence on the quality of auditing and audit fees for companies listed on the Tehran Stock Exchange. To estimate the model and testing the research hypotheses, 128 companies were selected as a sample from 1389 to 1396. After presenting the results of descriptive statistics, the stationary tests, and the correlation test, F-Lemmer and Hausman tests were performed to determine the type of estimation model. Finally, the panel data method with fixed effects was determined for regression analysis. The results indicating the positive effects of the managers’ overconfidence on the quality of auditing and audit fees on one hand, and the positive effects of audit quality on the cost of auditing.

In this study, the relationship between overconfidence and the cost of auditing fees of companies listed on the Tehran Stock Exchange with the mediating role of the type of auditing firm is examined. This research is of applied and descriptive-correlation type. To estimate the model and test the research hypotheses, out of 480 companies, 128 companies were selected as a sample by the systematic elimination method during the period 1389 to 1396. Research findings showed; In the regression model related to the research hypotheses, the F-statistic for all three research hypotheses is equal to 2.36, 2.50, 2.49 and the significance level of this statistic is less than 0.05, which indicates The appropriateness of the fit is done. Also, Watson camera statistics are equal to 2.15, 2.16, 2.15, respectively, and therefore there is no possibility of serial autocorrelation in the model. The coefficients for determining these models were 0.25, 0.26, and 0.26, respectively, indicating the suitability of the regression model. According to the presented statistics, all three research hypotheses are confirmed and it can be stated that there is a relationship between managers 'confidence and selection of auditing type, between auditing type and auditing cost and also between managers' confidence and auditing fees.

This research investigates the financing strategies of companies under normal and the economic crisis conditions over the years 2003-2016.

The statistical population of this research consists of companies listed on the Tehran Stock Exchange and Iran Over-The-Counter market (OTC). Research hypotheses have been tested through Multivariate Regression Equation.

The findings indicate that in both normal and crisis periods, the ROA (Return on Assets) and size of the company have a positive impact on the financing through the stock market. In addition, despite the negative impacts of sales growth, profitability, and average financial leverage on equity financing, tangible assets and market risk fail to indicate any effect on it. The results also demonstrate that the ROA and size of the company have negative effects on financing through the debt market over both normal and crisis periods. In addition, notwithstanding the negative impact of sales growth during the crisis period on debt financing, this effect is positive in the normal condition. Moreover, profitability and financial leverage have positive effects on debt financing during both normal and crisis times. Finally, it has been revealed that tangible assets have a positive impact on debt financing during the crisis period, but a negative effect during the normal period. Market risk has no effect on debt financing.

The purpose of this study is to explain the relationship between the characteristics of corporate governance or leadership on the probability of financial stress of companies on the Tehran Stock Exchange and to explain the moderating role of management capabilities on this relationship.

107 companies from 6 industries were surveyed during the years 92 to 96. Independent variables related to measuring corporate governance characteristics were divided into two categories. First, the characteristics related to the board of directors of companies, which are measured by the two variables of board size and independence of the board of directors, and second, are the audit characteristics of companies that have been measured using the variables of auditor size and type of auditor report. Data with the structural equation modeling approach of least partial squares.

The findings indicate that the characteristics of the board of directors have a negative effect on the probability of financial stress at the level of 10% error and the audit characteristics at the level of 5% error have a positive effect on the probability of financial stress. But the moderating role of management abilities on these relationships was not confirmed.

The results of this study indicate that improving some corporate governance or governance characteristics such as corporate board characteristics can reduce the likelihood of corporate financial stress, while improving some of these characteristics, such as improving corporate auditing characteristics, despite initial expectations. Not only are they unable to reduce the likelihood of financial stress, but they also have a detrimental effect on the likelihood of stress.

The primary purpose of this research is to investigate the impacts of corporate governance on credit risk in the Iranian banking industry. The sample consists of 20 banks listed on the Tehran Stock Exchange during 2011-2016, using panel data. In this research, credit risk and corporate governance are the dependent and independent variables, respectively. The meta-synthesis method was used for compiling a checklist of corporate governance indicators. Then, the content analysis method was applied for measuring the corporate governance index; i.e., the number of dimensions disclosed on the total number of disclosable dimensions. The results indicate that after adjusting the control variables namely the size, the financial leverage, the ratio of capital adequacy, the GDP and inflation, there is a significant negative relationship between corporate governance quality and the credit risk, which means more effective corporate governance will reduce information asymmetry, increases the clarity and stakeholder confidence, and finally reduces banks’ credit risk. Accordingly, the final recommendation is to reduce credit risk by improving the mechanisms of corporate governance in the Iranian banking industry.

Prediction of stock returns has always been one of the most important issues in finance. Investors have attracted to use of Fama-French Five-Factor Model (FFFFM) as one of the powerful methods for pricing financial assets and predicting the stock returns. This research investigates the predictability of stock returns by including some important firms features namely cash holdings, dividend rate, and free cash flow to equity to FFFFM. Statistical samples consist of 75 companies listed on the Tehran Stock Exchange (TSE) during 2003-2017. The results of panel data test indicate positive significant effects of all variables in FFFFM (i.e. book to market value ratio, company size, growth opportunity, profitability, and investment) as well as newly added firms feature variables (cash holding, dividend rate, and free cash flow to equity). However, the investment has a negative impact on the returns due to the initial estimate of primary FFFFM. In addition, the results indicate that the inclusion of firms feature variables significantly improve the predictive power of stock returns. Finally, by comparing the predictive power of the models, the best prediction model is determined.