Performance measurement in managerial accounting is normally associated with cash flow and it is executed based on different figures such as testing information content abuse and accounting figures. However, increasing the information content in accrual components of earning and internal performance measurement provides additional informative insights. This paper studies the relationship between operating cash flows and earnings along with total shareholder returns. The study chooses the information of 54 firms from Tehran Stock Exchange. The results show that there were some meaningful relationship between the operating cash flow, profitability and the returns of all stakeholders. However, this happens by increasing profitability and cash flow of information asymmetry proportion to their correlation with the economic efficiency of shareholders’ returns.
Automatic teller machine (ATM) is one of the most popular banking facilities to do daily financial transactions. People use ATM services to pay bills, transfer funds and withdraw cash. Therefore, we can treat ATM as a tradition inventory problem and use simulation technique to analysis the amount of cash required on different occasions such as regular days, holidays, etc. The proposed model of this paper uses genetic algorithm to determine the replenishment cash strategy for each ATM. The survey uses all transactions accomplished during the fiscal years of 2011-2012 on one of Iranian banks named Ayande. The study categorizes various ATM based on the average daily transactions into three groups of low, medium and high levels. The preliminary results of our survey indicate that it is possible to do setup different strategies to manage cash in various banks, optimally.
This paper presents a comparative study of using a linear probability and Logit models to predict credit risk of the customers in some branches of Bank Mellat in Tehran, Iran. The statistical population of this research includes the applicants of the facilities granted by Bank Mellat in Tehran during the year 2008. Each branches of Bank Mellat of Tehran has been considered as a cluster, where a sample has been taken using simple random method. The sample size consists of 176 companies, 109 legal entities are classified as those ones good at settling their accounts, and 67 as those ones tardy in settling their accounts. The financial ratios of these companies have been calculated based on their audited financial statements and by descriptive and analytical methods of two statistical models. The results show that liquidity ratios are not significant factors for the prediction of credit risks and these two models are not significantly different from each other in this term. Moreover, the accuracy values of credit risk prediction of linear and Logit models are 73.7 percent and 80.3 percent, respectively. Therefore, Logit model is more consistent with reality and more appropriate for such a prediction.