This paper presents an empirical investigation to study the relationship between the liquidity and the reward of board of directors on 136 selected firms listed on Tehran Stock Exchange over the period 2007-2011.The study considers nine different factors including return on assets (ROA), return on equities (ROE), Q-Tobin, changes of ROA and ROE, etc. In our study, there is a direct relationship between firm sizes with reward of board & apos; s directors. In addition, there is a direct relationship between changes of ROE with reward of board & apos; s directors. Moreover, there is a direct relationship between changes of operational cash flow with reward of board & apos; s directors. Finally, there is a direct relationship between changes of Q-Tobin ratio with reward of board & apos; s directors but the relationship was reverse for some years. These evidences confirm that there was a meaningful relationship between rewarding board of directors and share liquidity.

Technology development has influenced various fields, and financial field is one of them. Applying new technologies in financial field has led to the emergence of a new kind of reporting called Internet Financial Reporting, and is used increasingly day by day due to the increasing use of internet. Adopting this kind of reporting has caused changes in the process of informing stockholders and other users. Since increasing and updating information quality can influence on decision makers to buy/sell their stock certificate, and, on the other hand, the demand for buying and selling stock certificate might influence on stock price, we aimed to evaluate the effect of internet financial reporting on the stock price of listed companies in Tehran Stock Exchange. For this purpose, a group of companies was selected as the experimental group, and some others as the control group. Then, we investigated stock price changes in both groups, and compared changes. The results indicate that internet financial reporting had no effect on the stock price in the investigated companies.

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.