This work investigates the relational behavior of corporate social responsibility (CSR) and its effect on firms' financial distress (FD). The population of the study consists of all the non-financial firms presently listed in the equity market of Pakistan. The yearly data set of 213 non-financial companies is selected from 2005 to 2017 with total observations of 2769. The analysis of the study based on OLS regression, fixed effect, and random effect models. The study also uses the GMM technique to guard against potential problems of endogeneity and heteroskedasticity that arise from the use of panel data. Results indicate that higher investment in CSR leads to reduced/lower financial distress. It suggests that investment in CSR raises the reputation and creditworthiness of firms. Key findings are robust as confirmed by alternative proxies of financial distress. Overall findings advocate that CSR helps in reducing default risk or financial distress and creates a better corporate environment that ultimately improves organizations' economic outlook.

The aim of this study is to establish a framework for measuring and managing credit risk for fifteen leasing companies in Iran. An analysis on the influence of internal factors on credit performance will then be performed. This will enable a leasing industry to progress towards its goals and objectives in the most direct and effective way. Credit risk consists of probability of non-return. This may be in the form of bankruptcy or a decrease in financial and credit situation of the lessee. We can assume a correlated market and credit risk. The variables are extracted from the Central Bank of Kanoon Leasing Association in Iran. Numerical analysis reveals that lessee credit risk can have a substantial impact on a lease term structure.

Measuring different risk factors such as credit risk in banking industry has been an interesting area of studies. The artificial neural network is a nonparametric method developed to succeed for measuring credit risk and this method is applied to measure the credit risk. This research’s neural network follows back propagation paradigm, which enables it to use historical data for predicting future values with very good out of sample fitting. Macroeconomic variables including GDP, exchange rate, inflation rate, stock price index, and M2 are used to forecast credit risk for two Iranian banks; namely Saderat and Sarmayeh over the period 2007-2011. Research data are being tested for ADF and Causality Granger tests before entering the ANN to achieve the best lag structure for the research model. MSE and R values for the developed ANN in this research respectively are 86×?10?^(-4) and 0.9885, respectively. The results showed that ANN was able to predict banks’ credit risk with low error. Sensibility analyses which has accomplished on this research’s ANN corroborates that M2 has the highest effect on the ANN’s credit risk and should be considered as an additional leading indicator by Iran’s banking authorities. These matters confirm validation of macroeconomic notions in Iran’s credit systematic risk.

Measuring different risk factors such as credit risk in banking industry has been an interesting area of studies. The artificial neural network is a nonparametric method developed to succeed for measuring credit risk and this method is applied to measure the credit risk. This research’s neural network follows back propagation paradigm, which enables it to use historical data for predicting future values with very good out of sample fitting. Macroeconomic variables including GDP, exchange rate, inflation rate, stock price index, and M2 are used to forecast credit risk for two Iranian banks; namely Saderat and Sarmayeh over the period 2007-2011. Research data are being tested for ADF and Causality Granger tests before entering the ANN to achieve the best lag structure for the research model. MSE and R values for the developed ANN in this research respectively are 86×?10?^(-4) and 0.9885, respectively. The results showed that ANN was able to predict banks’ credit risk with low error. Sensibility analyses which has accomplished on this research’s ANN corroborates that M2 has the highest effect on the ANN’s credit risk and should be considered as an additional leading indicator by Iran’s banking authorities. These matters confirm validation of macroeconomic notions in Iran’s credit systematic risk.

In this paper, we present a study to measure the relationship between top management turnover with earnings management and default risk and earnings forecast error in the Tehran Stock Exchange. The proposed study selects necessary information from 117 firms from the exchange over the period 2005-2010 and, using ordinary least squares technique as well as Pearson correlation ratios, examine three hypotheses of this paper. The results of the survey indicate that there are some meaningful relationships between change in top management with earning management, default risk and earning forecast error.