درخت حوزه‌های تخصصی

Based on IFRS laws, British companies have started providing their reporting systems according to International Standards Requirements regarding disclosing their financial derivatives since January 2005. In 2013, Iran revised its Accounting Standard No. 15 to include the derivative instruments. The present study aims at investigating the effect of this revision on financial derivatives and instruments, and the effect of earning management on the relationship between the level of financial derivatives and instruments and risk-adjusted discount rates. From generalized least squares regression panel data, it was found that based on the first hypothesis, the companies which disclose their financial instruments based on No. 15 internal standard have a lower risk- adjusted discount rate, implying an increase in profit and a price rise in the markets. The findings also confirmed the second hypothesis, attesting to the effect of earning management on the relation-ship between financial derivatives and instruments disclosure and excess return. Findings of the research third hypothesis represent that there is a direct meaningful relationship between disclosure level of financial instruments and company value. So, it can be concluded that instruments` disclosures and financial derivatives can decrease risk-adjusted discount rate and increase companies` values in terms of standard number 15.

Fraud is a common phenomenon in business, and according to Section 24 of the Iranian Auditing Standards, it is the fraudulent act of one or more managers, employees, or third parties to derive unfair advantage and any intentional or unlawful conduct. Financial statements are a means of transmitting confidential management information about the financial position of a company to shareholders and other stakeholders. In this paper, by reviewing the literature, 6 indicators of current ratio, debt ratio, inventory turnover ratio, sales growth index, total asset turnover ratio, and capital return ratio as input and detection of financial fraud as output are considered for the fuzzy neural network. The database was compiled for 10 companies in the period from 2010 to 2018 after clearing and normalizing qualitatively between 1 to 5 discrete numbers with very low or very high meanings, respectively. The fuzzy neural network model with 161 nodes, 448 linear parameters, 36 nonlinear parameters, and 64 fuzzy laws with two methods of accuracy approximation of mean squared error and root mean squared error has been set to zero and 0.0000001 respectively. This neural network can be used for prediction.

Implementation credit rating for Corporates is influenced by Different circum-stances, systems, processes, and cultures in each country. In this study, we pro-posed a Factor analysis modified approach for determine important factors on real data set of 123 accepted corporate in Tehran Securities Exchange for the years 2009-2017 of diverse range of 52 variable. We estimated the priority score for 49 factors. The three factors, Debt to Equity Ratio, Current debt-to-equity ratio and proprietary ratio exclude due to high correlation with others. The results indicated that three macroeconomic factors: Price Index of Consumer Goods and Services, exchange rate and Interest rate determinants were more effective on the credit ratings. In addition, Financial Ratios and non-Financial Ratios such as Return on equity (ROE), Long-term debt-to-equity ratio, Benefit of the loan, ratio of com-modity to working capital, Current capital turnover, Return on Working Capital, Quick Ratio, Current Ratio, Net Profit margin, Gross profit margin, had effect on credit rating accepted corporate in Tehran Securities Exchange. The Nonparametric statistical test to validate the consistency between AHP ranking and Factor analysis revealed, the new approach has a moderated consistency with AHP. In conclusion, the Factor analysis modified approach could be applied significantly to evaluate efficiency and ranking factors with minimum loss of information.

Nowadays, the great benefits of cloud computing have dramatically increased the number of e-banking users. Hence, the competition in the banking industry has boosted and managers need to evaluate their branches on a regular basis. To this end, this study aims to evaluate cloud-based banking systems based on the Quality of Service (QoS) attributes using the Dynamic Network Data Envelopment Analysis (DNDEA) model. The main advantage of this research is that the efficiency of cloud-based bank branches can be estimated more realistically according to their internal structure over a specific time span. To conduct the experiment, 40 bank branches in Iran are analyzed by considering between-period and divisional interactions during 2018-2019. A cloud-based bank branch is conceptualized as a set of three inter-connected divisions including capabilities, intermediate process, and profitabilities. Some outputs of sub-DMUs 2 and 3 are treated as desirable and undesirable carry-overs between consecutive periods. In addition, the cost items and QoS attributes are considered as the inputs and outputs of divisions, respectively. The results indicate that 28 bank branches were efficient and all of the inefficiencies fall in divisions 1 and 3. Moreover, the number of efficient branches has been reduced from 2018 to 2019.