درماندگی مالی

Financially distressed firms are actively seeking ways to minimize tax-related cash outflows. During the COVID-19 pandemic, as financial constraints intensified, tax savings emerged as a vital source of internal financing for these firms. As a result, financially distressed firms are more likely to adopt tax avoidance strategies. This study aimed to investigate the impact of financial distress on tax avoidance and how this relationship manifested during the COVID-19 pandemic. The research sample consisted of 162 firms listed on the Tehran Stock Exchange (TSE) from 2017 to 2022. The findings revealed that financial distress had a positive and significant effect on tax avoidance, indicating that firms experiencing greater financial distress were more likely to engage in tax avoidance throughout the study period. Additionally, the COVID-19 pandemic did not significantly moderate the relationship between financial distress and tax avoidance. While this study contributed to the existing literature on the effects of financial distress on tax avoidance, it also enhanced our understanding of how financial crises, particularly those resulting from COVID-19, influenced this relationship. Moreover, contrary to the initial research hypotheses, the findings suggested that COVID-19 did not significantly impact the relationship between financial distress and tax avoidance.
Keywords: Financial Distress, Tax Avoidance, COVID-19 Pandemic, Tax Discount.
JEL Classification: H26, G32, M41, M48
 
In times of financial distress, firms often deplete a significant portion of their cash reserves, exacerbating their financial challenges. This state of distress drives companies to seek ways to reduce tax-related cash outflows, increasing the likelihood that financially distressed firms will engage in tax avoidance (Brondolo, 2009). Several studies (e.g., Edwards et al., 2016; Richardson et al., 2015; Mokhtari, 2019; Hajiha et al., 2017) provide evidence supporting a positive relationship between financial distress and tax avoidance. The COVID-19 pandemic has introduced unprecedented economic challenges and uncertainties for firms worldwide. In response to these uncertainties, managers are compelled to develop various strategies, with tax avoidance emerging as a preferred method for generating internal cash flows. This study aimed to examine the impact of financial distress on tax avoidance and investigate the moderating effect of the COVID-19 pandemic on the relationship between financial distress and tax avoidance. Our research contributed significantly to the existing literature. First, our findings enhanced the body of research on tax avoidance among financially distressed firms (e.g., Edwards et al., 2016; Dang & Tran, 2021; Sadjiarto et al., 2020; Putri & Chariri, 2017; Nugroho et al., 2020; Mokhtari, 2019; Qavi Panjeh & Gharib, 2018; Hajiha et al., 2017). Second, by considering both macroeconomic and firm-level factors that influenced corporate tax strategies, this study deepened our understanding of how firms utilized tax avoidance as a strategy during periods of uncertainty. This study examined the following hypotheses:H1: Financial distress is associated with tax avoidance. 
H2: The COVID-19 pandemic moderates the relationship between financial distress and tax avoidance.
 
Materials & Data were collected from 162 firms listed on the Tehran Stock Exchange (TSE) between 2015 and 2022, resulting in a total of 972 firm-year observations. The sample excluded firms from the insurance, financial, and banking sectors. Tax avoidance was measured as the difference between a firm's cash taxes paid adjusted for any tax refunds receivable and the product of its pretax book income and the statutory tax rate. This measure was then scaled by the book value of the firm's assets. A firm was considered to engage in tax avoidance when its cash taxes paid were less than its pretax income multiplied by the statutory tax rate. Since part of the tax paid in the current period might pertain to taxes determined in prior periods, the tax expense reported in the income statement was used in place of cash taxes paid for this analysis. Financial distress was assessed using the Altman Z Score. The COVID-19 pandemic served as the moderating variable represented as a dummy variable with a value of 1 for the COVID-19 period and zero otherwise. The years 2019 and 2020 were designated as the COVID-19 outbreak period. To test the research hypotheses, regression models were estimated using the Generalized Least Squares (GLS) estimator.
 
Table 1 presents the results of the GLS regression analysis regarding the impact of financial distress on tax avoidance, as well as the moderating effect of COVID-19 on this relationship. Column 1 shows the effect of financial distress on tax avoidance. The findings indicated that the coefficient for financial distress was positive and statistically significant, suggesting that financial distress was associated with increased tax avoidance. Furthermore, the results revealed that the coefficient for the interaction term (COVID*FD) was not statistically significant, indicating that the COVID-19 variable did not have a significant effect on the relationship between financial distress and tax avoidance. Consequently, the model estimation results did not support our second hypothesis.
 
Discussion & Tax avoidance is a strategy used to minimize tax liabilities. Theoretical arguments and empirical evidence suggest that financially distressed firms have a stronger incentive to engage in tax avoidance as tax savings can provide an alternative source of financing. Furthermore, during crises, such as the COVID-19 pandemic, the significance of tax savings increases for firms facing heightened financial challenges. This study investigated the moderating effect of COVID-19 on the relationship between financial distress and tax avoidance. The findings indicated that financial distress positively affected tax avoidance, supporting the notion that financially distressed firms are more likely to adopt tax avoidance strategies. These results are consistent with previous theoretical frameworks and empirical studies, including Edwards et al. (2016), Dang and Tran (2021), Mills and Newberry (2005), Sadjiarto et al. (2020), Putri and Chariri (2017), Nugroho et al. (2020), Richardson et al. (2015), Hasan et al. (2017), Akamah et al. (2021), Dyreng and Markle (2016), Hajiha et al. (2017), and Mokhtari (2019). Moreover, while several studies indicated that firms adopt aggressive tax strategies to mitigate the adverse effects of uncertainty (Lee et al., 2021; Guenther et al., 2019; Huang et al., 2017), this study found that the COVID-19 outbreak did not significantly moderate the relationship between financial distress and tax avoidance. The contributions of this study enrich the growing body of literature on corporate tax strategies during times of crisis. The findings have important implications for both corporate managers and investors. For corporate managers, the results underscore the importance of tax avoidance strategies. When considering tax avoidance, managers should weigh the benefits, such as reduced tax liabilities and increased cash flow, against potential costs, including audit expenses, penalties, and reputational damage that may arise.

The primary objective of this research was to analyze the relative importance of working capital management factors in predicting financial distress among companies. The study population consisted of 167 companies listed on the Tehran Stock Exchange (TSE) from 2019 to 2023. 7 key working capital management indicators were selected based on their potential impacts on financial distress. Using Zavgren’s (1985) financial distress prediction model, the sample companies were classified into distressed and healthy groups. In the first step, a random forest algorithm was employed to assess the predictive power of the seven working capital management indicators in classifying companies as distressed or healthy. The results indicated that these indicators could successfully identify and predict the financial distress status of the companies with up to 85% accuracy. In the second step, the unique feature of the random forest algorithm was leveraged to rank the importance of each working capital component in achieving this 85% classification accuracy. The findings showed that the Average Collection Period (ACP) was significantly more important than the other working capital components in predicting financial distress.Keywords: Financial Distress, Working Capital Management, Average Collection Period (ACP), Random Forest AlgorithmJEL Classification: G01, G30, C38 IntroductionIn recent years, financial distress and bankruptcy have become increasingly prevalent issues for business enterprises. The financial literature offers various definitions to describe the state of financial distress and bankruptcy. While some researchers equate financial distress with bankruptcy, financial distress is more accurately viewed as a precursor to bankruptcy – a stage of financial decline that may or may not ultimately lead to a company's bankruptcy. Simply put, financial distress reflects a business entity's inability or weakness in fulfilling its obligations to creditors (Gerged et al., 2022). Given the rapid growth of joint-stock companies and the emergence of severe financial crises at both micro- and macro-economic scales, it is crucial to identify the key factors that can predict a company's financial health before it reaches the stage of bankruptcy, i.e., during the financial distress phase (Pourheydari et al., 2010). Evidence suggests that working capital management is a significant factor influencing the financial distress of business enterprises (Geng et al., 2015). Companies experiencing financial distress and bankruptcy often exhibit weaknesses in working capital management, particularly in cash control. Therefore, the aim of this study was to evaluate the predictive power of working capital management components in forecasting financial distress and rank the importance of each component in this prediction process.Materials & MethodsThe raw financial statement data for this research were extracted from Rahavard Novin Database and the Codal website. These data were then systematically organized in Excel. After applying certain eligibility criteria, a sample of 167 companies was identified as the accessible statistical population. To classify the sample companies into distressed and healthy groups, which served as the target variable (label), Zavgren’s (1985) financial distress prediction model was utilized. Subsequently, the predictive power of 7 key working capital management components in forecasting financial distress was tested using Python software and the random forest algorithm.The random forest method is based on ensemble learning, wherein the data are split into training and testing sets. During the learning phase, the model attempts to identify the inherent pattern or the relationship between the dependent variable (financial distress) and each explanatory variable (working capital management components) with the validity of this learning measured by the testing data. The random forest method employs a bagging approach, creating subsets from the entire dataset and determining the final result based on the average outcomes of these subsets. This approach helps to significantly mitigate the overfitting problem.One notable feature of the random forest algorithm is its ability to rank the importance of the input features in determining the trend of the target variables. This capability was leveraged in this research to answer the second research question, which focused on the relative importance of each working capital component in predicting financial distress. Research FindingsThe model achieved an accuracy of 85%, indicating that it could correctly predict whether a company was in financial distress or not based on what it learned during the training phase. Additionally, the model's F1-Score metric was 0.89 for identifying healthy companies and 0.76 for predicting distressed companies. These scores, being close to 1, suggested that the model's estimations were performed with a high degree of accuracy.The analysis of the relative importance of each working capital management component in achieving this 85% accuracy rate revealed some key insights. The Average Collection Period (ACP) was identified as the most important factor in predicting financial distress. Following the ACP, the Current Ratio (CR) ranked second, the Average Payable Period (APP) ranked third, and the Inventory Turnover In Days (ITID) ranked fourth in importance.These findings suggested that the initial signs of financial trouble for a company often stemmed from its failure to collect receivables in a timely manner, leading to an increased collection period. If the company's management did not effectively address this issue, other problems could likely arise, ultimately pushing the business entity into a state of financial distress. Discussion of Results & ConclusionThe results of the data analysis using the random forest algorithm indicated that working capital management indicators had an 85% predictive power for identifying financial distress in companies. This finding is consistent with those of the previous studies by Habib and Kayani (2022), Morshed (2020), and Li et al. (2018). Regarding the second research objective, which aimed to rank the importance of each working capital management component in predicting financial distress, the analysis revealed that the Average Collection Period (ACP) was the most significant factor. This suggested that a company's inability to collect receivables in a timely manner was a crucial early indicator of impending financial distress.An increase in the ACP could lead to a serious risk of bad debts and liquidity problems for the company. As a result, the company's management might need to secure additional working capital to fund operations, which could potentially increase the Weighted Average Cost of Capital (WACC). However, if the company failed to generate adequate returns to cover these elevated financing costs, it might ultimately fall into a state of financial distress (Panigrahi, 2014). Given the notable importance of the ACP compared to other working capital management components, it appeared that many of the underlying issues leading to financial distress stemmed from poor performance in collecting receivables. Therefore, this research underscored the critical need for robust management practices of receivables to maintain liquidity and avoid the escalating costs and risks associated with financial distress.

The main objective of this article is to examine the relationship between financial leverage and financial performance. After identifying this relationship, the moderating effect of financial distress on the strengths and weaknesses of this relationship will be investigated. Then, due to the importance of the effect of the currency crisis on the financial performance of companies, the effect of this moderating variable on the relationship between financial leverage and financial performance is also examined.

The statistical population investigated in this research comprises companies listed on the Tehran Stock Exchange during the period from 2013 to 2022. The data needed to analyze the relationships were collected using the library research method, utilizing databases from the Central Bank of Iran, the World Bank, and Codal. Systematic elimination was employed in the population to account for limitations, resulting in a final sample of 114 companies from various sectors of the Tehran Stock Exchange, excluding the financial and investment sectors. In this research, financial leverage is considered the independent variable, while financial performance measures such as return on assets, return on equity, Tobin's q, return on sales (ROS), and return on cash flow are treated as dependent variables. Financial distress and the currency crisis serve as moderating variables. Data were analyzed using correlation analysis and multivariate regression, with the Estimated Generalized Least Squares (EGLS) method applied to fit the model.

The findings indicate that financial leverage has a significant negative impact on financial performance, and this effect is attenuated in companies that face a higher risk of financial distress. Furthermore, the results suggest that a currency crisis intensifies the negative relationship between financial leverage and financial performance.

The negative relationship between financial leverage and a company's financial performance indicates that as a company increases its debt level, the rise in borrowing costs outweighs the benefits. Managers can enhance the company's financial performance by reducing financial leverage levels and planning to increase internal funds, which should be considered an essential strategy to improve performance and mitigate external financing constraints. In high-risk financial distress conditions, the negative effect of financial leverage on financial performance tends to diminish. Management issues and the absence of operational and control systems are more pronounced in companies at a higher risk of financial distress; therefore, monitoring, limiting, and enhancing company efficiency due to high financial leverage will have a significantly positive effect on the financial performance of companies facing a high risk of financial distress (including payment defaults, bankruptcy, and liquidation). It can be asserted that higher financial leverage can improve the financial performance of companies due to its disciplinary role. Additionally, during a currency crisis, which typically results in higher inflation, investment costs and the utilization of loans and financial resources will increase. Reducing financial leverage during such crises, when access to foreign financial resources becomes more expensive and difficult, may lead to improved financial performance for the company.

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).

In the era when companies are facing many challenges to survive in competitive markets, it is important to identify the factors affecting financial crises. One of the ways to help investors and companies is to provide models to predict financial distress. The purpose of this research is to investigate the ability of rough set theory and compare it with artificial and fuzzy neural networks to predict the financial distress of companies active in the Tehran Stock Exchange. For this purpose, 329 distressed companies from non-distressed corporations were selected during the period of 2006 to 2020. The neural networks investigated in this research are: Multi-layer Perceptron (MLP) neural network, Radial Basis Function (RBF) network, and Adaptive Fuzzy Inference Network (ANFIS) as well as the software used to create the ROSETTA rough set and the software The tool used to design artificial and fuzzy neural networks is MATLAB software. The results obtained in this research show the high efficiency of the rough sets model with 98/7 percent accuracy for predicting financial distress.

This study aimed to evaluate the performance of the gradient boosting decision tree model, the parameters of which were optimized with the improved Gray Wolf Algorithm (GWO) by adding financial network-related variables via the selected models of predicting financial distress. The proposed model of this study was implemented on the data of 123 manufacturing companies admitted to the Tehran Stock Exchange and Iran Fara Bourse Co. (IFB) from 2014 to 2021. Initially, the financial network was formed and then, the financial distress of companies was predicted by integrating the network-based variables with financial ratios and using a gradient boosting decision tree model. The model of the gradient boosting decision tree had better performance in terms of precision and Type I error by adding Financial Network Indicators (FNI) compared to the two models of K-Nearest Neighbor (KNN) and Logistic Regression (LR). Companies with betweenness centrality and high degree centrality were found to be less prone to financial distress and vice versa. This is the first study to predict financial distress by using financial network-related variables integrated with financial ratio variables through the novel gradient boosting decision tree method, the parameters of which were optimized with the improved GWO.

Four major approaches to financial analysis can be defined. Traditional, Ultra -Traditional, modern and postmodern approach. In the novel theory of business resilience system, in measuring financial risks or otherwise, conditions are provided that by comprehensiveness and also considering the rule of contingency paradigm, the possibility of measuring financial indicators and other cases on a case-by-case basis. Or comprehensive, be provided. Or comprehensive, be provided. Statistical data of listed companies in the period 1390 to 1398 were used. In this study, using different methods based on innovative financial instruments, the power of these instruments in estimating financial distress, traditional, ultra -Traditional, modern and postmodern perspectives are compared. Finally, it was found that the financial engineering method, relying on the theory of business resilience system in determining financial distress, reduces the fluctuation of the estimate and increases the accuracy of the estimate of financial distress. Of course, along with new methods, traditional methods should always be considered as indicators to determine the extent of financial distress and for comparison.

This study was aimed to predict financial distress using financial, economic and stock market variables in the form of binary logistic regression models, Merton and hybrid models. For this purpose, the information of 168 distressed companies selected based on specific criteria of distress and 168 healthy companies listed on the Tehran Stock Exchange between2006-2019 and two years ago, one year ago and distress year has been used. In this study, from 17 financial ratios, 4 economic variables and 4 stock market variables have been used. The innovation of the present study is the development of a hybrid financial distress prediction model that for the first time combines the financial, economic and stock market variables of the accounting model with the default variable of the structural model.The results showed that the hybrid model has better explanatory power than Merton and binary logistic regression model and although the existence of the variable probability of Merton model improves the explanatory power of the hybrid model, but the explanatory power of binary logistic regression model is better than the Merton model

Aim of study:

 In this study, by reviewing at the literature of financial distress prediction, a deep learning method has been developed that automates and shortens the earlier procedures of financial distress prediction. At last, the developed method is validated by comparing it to the earlier methods of this area.

The developed Convolutional Neural Network is used to extract knowledge within 14 related features to financial distress, through 3 consecutive years. In order to avoid the probable generalization error, the advantage of K-fold method to slice the entire 300 sample into sub-samples, was taken. Finally, the advantage of non-parametric Wilcoxon test was taken to verify the differences between the accuracy of the proposed method with the earlier ones.

Results of this study implies that the Convolutional Neural Network beats the other well-known methods in this area such as logistic regression and support vector machine with 95 percent confidence interval, in terms of prediction accuracy.

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.

Behavioral finance explains contradictory patterns with market efficiency hypotheses with behavioral biases. One of the most common price patterns in the stock market is the pattern of momentum, which can be driven by investors' adjustment and anchoring bias and disposition effect. In this study, the role of adjustment and anchoring bias and disposition effect on the formation of momentum returns on the Tehran Stock Exchange are examined. Using the portfolio study method and the data of the research period of 2007-2016, it was found that investors are more affected by adjustment and anchoring bias compared to disposition effect and form a pattern of momentum by reversing against the maximum price thresholds with a one-year period as the reference price. Also, among the maximum thresholds, investors are most affected by the maximum price of 26 weeks with a six-month waiting period, and further analysis and analysis using the Fama-Macbeth regression and the Fama-French three-factor model confirm these results.

The purpose of this research is to investigate the information of circulation capital management information for predicting financial helplessness based on artificial neural networks and particle cumulative optimization algorithms. The statistical population of the research consists of 120 companies listed in Tehran Stock Exchange during the years 2008-2019. In order to achieve the goals of the research, first, by studying previous studies in the field of financial distress, 28 variables affecting financial distress and then, using the leading logistic regression model, the estimated model and 5 variables were selected. Then, in order to verify the information of the information management information in circulation, comparing the research model with attention and regardless of the circulation of circulation management based on the combination of artificial neural networks and the optimization of the cumulative particle movement. The results of the two models based on the combination of artificial neural networks and the optimization algorithm of cumulative particle movement showed that the development of the research model reduced the error of neural network training with the cumulative particle movement algorithm to 0.0641. Also, with the development of the research model, the subcutaneous level of Rock increases to 6,248 and, consequently, the research model is added to 70.5%. This result shows the effectiveness of the entry of capital management in the research model.

In the present study, the effect of the spread of financial distress in Iranian banks with a dynamic conditional turbulence approach for the use of economic decision makers and financial managers has been investigated.The statistical population of the study includes Mellat, Tejarat, Saderat and Parsian banks, which have been analyzed in the period of 2016-2016. The present study, using the KMV method and the concept of distance to default, and using the VAR model and the DCC-GARCH method, has investigated the possibility of spreading financial distress to other banks.The results show that there is a significant relationship between the risk of financial helplessness of banks with each other; Bank Mellat has the highest risk of transmission of helplessness and Parsian Bank shows the least effectiveness. According to the results of the model, increasing the operational risks of banks, including credit risk and market risk, has a significant effect on increasing the risk of financial distress and this risk can spread to other banks in the banks' communication network and then the entire economy.

Due to contractual demands, litigation costs and legal preferences in banks, the role of conditional conservatism is important in order to increase banking transparency. Also, in the conditions of information asymmetry and financial distress, uncertainty about the quality of loans is reduced, bank managers try to reduce agency costs by increasing the quality of information through conservative reporting and increasing the informing about banks real value. Based on this, the aim of the research is to investigate the effect of information asymmetry and financial distress on relationship between accounting conservatism and loan quality portfolio in Iran's banking system. For this purpose, the data of 15 banks accepted in the Iranian capital market between 2012 and 2020 have been used. The analysis of the results using multiple regression showed that conditional conservatism has increased the loan quality portfolio (increasing the quality of bank assets and reducing the ratio of non-current claims), and informational asymmetry and financial distress with a positive effect have modified and intensified the relationship between conditional conservatism and loan quality portfolio.

Financial distress, which is defined as the uncertainty about the company's ability to meet its obligations and repay its debts, has been estimated by different models divided into three groups of fundamental models (based on accounting or financial data), structural models (based on the company's capital structure or market information) and hybrid models.  Accurately predicting financial distress is still a major point of challenge for financial researchers. Scholars acknowledge that financial distress will be experienced when it happens. Therefore, the best thing to do is to initially estimate the probability of a company's financial distress. In this regard, in the current study, first, a hybrid model was presented to investigate the ability of financial distress prediction models. Next, in order to compare the hybrid model with accounting-based models, the second version of Altman's Z model known as the Z˝ model was used. To compare the hybrid model with market-based models, Merton's model was used in three groups including healthy, distressing, and distressed companies.

In this research, by reviewing past studies, 47 variables affecting financial distress, such as accounting variables, market variables, and macroeconomic indicators were identified. Afterward, considering the frequency and successful performance of these variables in past studies, 19 variables were selected. In the next step, using the Stepwise regression test, among the 19 variables, 10 variables with probability values ​​smaller than 0.05 were chosen. Also, to determine the dependent variable, the European option pricing model (Merton's model) was used. Finally, by the use of the Multinomial logit model and identifying the relationship between the dependent and independent variables, the hybrid model for predicting financial distress was designed. In order to compare the produced hybrid model with accounting-based fundamental models, the second version of Altman's Z model known as the Z˝ model was used. To compare the hybrid model with market-based structural models, Merton's model was used. Moreover, in order to test the ability of financial distress prediction models, a sample including 100 companies listed on the Tehran Stock Exchange (TSE) or Iran FaraBourse (IFB) was selected. Then, considering the defined criteria, these companies were divided into three groups consisting of healthy, distressing, and distressed companies. Finally, the ability of the above-mentioned models in predicting financial distress was investigated.

Research findings indicated that in the hybrid model, the ratios of Net Working Capital to Total Assets (WCTA), Operating Cash Flow to Total Assets (OCTA), Sales to Total Assets (STA), Net Income to Total Assets (NITA), Short-term and Long-term Debts to Equity (TLTE), Price to Earnings Per Share (P/E) and Price to Sales (P/S), and the variable of Interest Rate (INT) had significant relations with company's financial distress probability. Also, a comparison of the hybrid model and conventional models revealed that in the group of financially distressed companies, respectively, the Z˝ model with 100% accuracy, Merton's model with 85% accuracy, and the hybrid model with 90% accuracy had correctly predicted the financial situation of the companies. While, in the group of financially distressing companies, the accuracy of the Z˝ model, Merton's model, and the hybrid model in predicting the financial situation of the companies, stood at 50%, 85%, and 85%, respectively. In addition, in the group of healthy companies, these models were able to correctly predict 95%, 85%, and 90% of the companies' financial situation, respectively.

According to achieved results, the Z˝ model has higher predictive power on healthy and distressed companies, compared to the hybrid and Merton models. While, the hybrid and Merton models are better at predicting the financial situation of distressing companies than the Z˝ model. Therefore, considering that the performance of the market-based model of Merton in predicting the financial situation of the companies is weaker than those of the Z˝ and that the hybrid models which are mainly formed by financial or accounting ratios, and also in regard to the findings of past studies which proved the inefficiency of the stock market in Iran, it can be concluded that it is better to use accounting variables in future research in the field of predicting financial distress.

Monte Carlo Markov chain methods are a set of algorithms for sampling possible distributions based on building a Markov chain with desirable properties. One of the most common Markov Monte Carlo chain algorithms is the Metropolis-Hastings algorithm. Therefore, the purpose of this study is to evaluate the relationship between financial distress and stock returns using the Metropolis-Hastings algorithm in the Tehran Stock Exchange. For this purpose, 151 companies were selected from the Tehran Stock Exchange in the period 2011 to 2020 using systematic elimination sampling method. R software was used to test the research hypotheses. Altman Z and Olson's score were also used to calculate financial distress. Also, in evaluating the relationship using the Metropolis-Hastings algorithm, two different previous distributions for the research variables were used. The results of the study showed that for Altman's financial distress variable, the accuracy of estimating financial distress was higher with the previous non-informed distribution. For O-Olson's financial helplessness variable, the precision of financial distress was higher with Zelner's previous distribution. Meanwhile, in the previous non-informed distribution, the effect of financial distress was not significant and was significant in Zelner's previous distribution.

In financial markets, the effect of profitability anomaly, distress anomaly, lotterynees anomaly and idiosyncratic volatility have been investigated individually. However, the potential relationship among these anomalies have not been analyzed yet. Recently it has been raised that growth options effect on the symmetry of the return distribution function and it can describe the potential relationship among anomalies and risk premium of anomalies. This relationship has been investigated in this research by use of the statistical properties governing over third order moments of return distribution function and isolating expected idiosyncratic skewness derived from growth options. For this purpose, data of 114 companies listed in Tehran Stock Exchange were collected during 2011 to 2016. Hypotheses were tested using portfolio approach and alpha evaluation of factor models. The findings shows that there is relationship between profitability, distress, lotteryness, idiosyncratic volatility and stock return, but the common capital asset pricing models cannot explain premium risk of these anomalies. These findings confirm profitability, lotteryness, distress and idiosyncratic volatility puzzle indirectly in the capital market of Iran and show that investors can earn extra return by using these anomalies.

Corporate financial distress is the most unpleasant event that will result in catastrophic issues for its stakeholders. In addition to the huge losses for the business itself, this event can potentially affect the country's economy. Therefore, quick and timely detection of financial distress is essential to support various financial and social investments. In this regard, the present study aimed at providing a combined model of corporate default prediction and classifying firms into three groups: healthy, stressed and distressed. In this study, first, using a library research method, 47 variables or ratios were identified, selected and classified into three groups: fundamental or financial variables, market variables and macroeconomic variables. Then, considering the frequency and successful performance of these ratios in previous studies and by performing statistical tests, potential variables affecting financial distress were identified. In this study, multinomial logistic regression was used to provide a combined model of corporate default prediction. Also, in order to measure corporate default, the Black-Scholes-Merton (BSM) model was used. Findings indicated that 8 variables, including 5 financial variables, 2 market variables and 1 macroeconomic variable were statistically significant in the final model, and in fiscal year 1398, the accuracy of this model was 90% in the group of distressed firms, 85% in the group of stressed firms and 90% in the group of healthy firms.

One of the most important issues in the field of financial management is how the investors distinguish between favorable investment opportunities and undesirable ones. One of the ways to help investors is to provide financial distress prediction models. According to the various studies have been made to develop these type of models, in this study the combination of artificial neural networks (ANN) and genetic algorithm (GA) techniques based on Zimensky prediction ratios is used for modeling financial distress. The research statistical population includes public companies in Tehran stock exchange which admitted between October 2013 to October 2015 and among them 66 distressed and 150 going concern companies were selected as the research sample using screening method. The results indicate that the power of both artificial neural network and genetic algorithm models in financial distress prediction are equal (95 percent), however, the prediction error of neural network is relatively low compared to genetic algorithm.

Despite the fact that accruals have a central role in preparing financial reports and are viewed as a dynamic research domain, the studies on accruals have not provided a profound understanding of how firm level economic factors affect accruals. In this study, the firms’ working capital is regarded as a form of investment. Also, using theoretical predictions from a real option-based investment framework, the present study aims to examine the effects of stock returns volatilities on changes in firm working capital accruals. In addition, the moderating effect of financial distress on the relationship between stock return volatilities and working capital accruals is studied.

The statistical sample of this research consists of 111 firms listed in the Tehran Stock Exchange from 2005 to 2016. The research hypotheses are also tested by Generalized Least Squares (GLS) regression analysis, using the pooled data.

The results show that there is a significant negative relationship between volatilities of stock return and changes of working capital accruals of firms in general. On the other hand, the results indicate a firm’s financial distress does not decrease the negative effect of stock return volatilities on working capital accruals. Furthermore, another finding suggests that stock returns volatilities have a positive effect on the items of capital liabilities and negatively affect items of working capital in accruals; however, the firms’ inventories, here, are considered as exceptions and returns volatilities have positively impacted the firms’ inventories.